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CBSE Class 11 Statistics for Economics Notes

Chapter 1: Concept of Economics and Significance of Statistics in Economics

Statistics for Economics | Functions, Importance, and Limitations

Chapter 2: Collection of Data

Data Collection & Its Methods
Sources of Data Collection | Primary and Secondary Sources
Direct Personal Investigation: Meaning, Suitability, Merits, Demerits and Precautions
Indirect Oral Investigation : Suitability, Merits, Demerits and Precautions
Difference between Direct Personal Investigation and Indirect Oral Investigation
Information from Local Source or Correspondents: Meaning, Suitability, Merits, and Demerits
Questionnaires and Schedules Method of Data Collection
Difference between Questionnaire and Schedule
Qualities of a Good Questionnaire and types of Questions
What are the Published Sources of Collecting Secondary Data?
What Precautions should be taken before using Secondary Data?
Two Important Sources of Secondary Data: Census of India and Reports & Publications of NSSO
What is National Sample Survey Organisation (NSSO)?
What is Census Method of Collecting Data?
Sample Method of Collection of Data
Methods of Sampling
Father of Indian Census
What makes a Sampling Data Reliable?
Difference between Census Method and Sampling Method of Collecting Data
What are Statistical Errors?

Chapter 3: Organisation of Data

Organization of Data
Objectives and Characteristics of Classification of Data
Classification of Data in Statistics | Meaning and Basis of Classification of Data
Concept of Variable and Raw Data
Types of Statistical Series
Difference between Frequency Array and Frequency Distribution
Types of Frequency Distribution

Chapter 4: Presentation of Data: Textual and Tabular

Textual presentation of data: meaning, suitability, and drawbacks.

Tabular Presentation of Data: Meaning, Objectives, Features and Merits
Different Types of Tables
Classification and Tabulation of Data

Chapter 5: Diagrammatic Presentation of Data

Diagrammatic Presentation of Data: Meaning , Features, Guidelines, Advantages and Disadvantages
Types of Diagrams
Bar Graph | Meaning, Types, and Examples
Pie Diagrams | Meaning, Example and Steps to Construct
Histogram | Meaning, Example, Types and Steps to Draw
Frequency Polygon | Meaning, Steps to Draw and Examples
Ogive (Cumulative Frequency Curve) and its Types
What is Arithmetic Line-Graph or Time-Series Graph?
Diagrammatic and Graphic Presentation of Data

Chapter 6: Measures of Central Tendency: Arithmetic Mean

Measures of Central Tendency in Statistics
Arithmetic Mean: Meaning, Example, Types, Merits, and Demerits
What is Simple Arithmetic Mean?
Calculation of Mean in Individual Series | Formula of Mean
Calculation of Mean in Discrete Series | Formula of Mean
Calculation of Mean in Continuous Series | Formula of Mean
Calculation of Arithmetic Mean in Special Cases
Weighted Arithmetic Mean

Chapter 7: Measures of Central Tendency: Median and Mode

Median(Measures of Central Tendency): Meaning, Formula, Merits, Demerits, and Examples
Calculation of Median for Different Types of Statistical Series
Calculation of Median in Individual Series | Formula of Median
Calculation of Median in Discrete Series | Formula of Median
Calculation of Median in Continuous Series | Formula of Median
Graphical determination of Median
Mode: Meaning, Formula, Merits, Demerits, and Examples
Calculation of Mode in Individual Series | Formula of Mode
Calculation of Mode in Discrete Series | Formula of Mode
Grouping Method of Calculating Mode in Discrete Series | Formula of Mode
Calculation of Mode in Continuous Series | Formula of Mode
Calculation of Mode in Special Cases
Calculation of Mode by Graphical Method
Mean, Median and Mode| Comparison, Relationship and Calculation

Chapter 8: Measures of Dispersion

Measures of Dispersion | Meaning, Absolute and Relative Measures of Dispersion
Range | Meaning, Coefficient of Range, Merits and Demerits, Calculation of Range
Calculation of Range and Coefficient of Range
Interquartile Range and Quartile Deviation
Partition Value | Quartiles, Deciles and Percentiles
Quartile Deviation and Coefficient of Quartile Deviation: Meaning, Formula, Calculation, and Examples
Quartile Deviation in Discrete Series | Formula, Calculation and Examples
Quartile Deviation in Continuous Series | Formula, Calculation and Examples
Mean Deviation: Coefficient of Mean Deviation, Merits, and Demerits
Calculation of Mean Deviation for different types of Statistical Series
Mean Deviation from Mean | Individual, Discrete, and Continuous Series
Mean Deviation from Median | Individual, Discrete, and Continuous Series
Standard Deviation: Meaning, Coefficient of Standard Deviation, Merits, and Demerits
Standard Deviation in Individual Series
Methods of Calculating Standard Deviation in Discrete Series
Methods of calculation of Standard Deviation in frequency distribution series
Combined Standard Deviation: Meaning, Formula, and Example
How to calculate Variance?
Coefficient of Variation: Meaning, Formula and Examples
Lorenz Curveb : Meaning, Construction, and Application

Chapter 9: Correlation

Correlation: Meaning, Significance, Types and Degree of Correlation
Methods of measurements of Correlation
Calculation of Correlation with Scattered Diagram
Spearman's Rank Correlation Coefficient
Karl Pearson's Coefficient of Correlation
Karl Pearson's Coefficient of Correlation | Methods and Examples

Chapter 10: Index Number

Index Number | Meaning, Characteristics, Uses and Limitations
Methods of Construction of Index Number
Unweighted or Simple Index Numbers: Meaning and Methods
Methods of calculating Weighted Index Numbers
Fisher's Index Number as an Ideal Method
Fisher's Method of calculating Weighted Index Number
Paasche's Method of calculating Weighted Index Number
Laspeyre's Method of calculating Weighted Index Number
Laspeyre's, Paasche's, and Fisher's Methods of Calculating Index Number
Consumer Price Index (CPI) or Cost of Living Index Number: Construction of Consumer Price Index|Difficulties and Uses of Consumer Price Index
Methods of Constructing Consumer Price Index (CPI)
Wholesale Price Index (WPI) | Meaning, Uses, Merits, and Demerits
Index Number of Industrial Production : Characteristics, Construction & Example
Inflation and Index Number

Important Formulas in Statistics for Economics

Important Formulas in Statistics for Economics | Class 11

Presentation of Data refers to the exhibition of data in such a clear and attractive way that it is easily understood and analysed. Data can be presented in different forms, including Textual or Descriptive Presentation, Tabular Presentation, and Diagrammatic Presentation.

Textual Presentation

Textual or Descriptive Presentation of Data is one of the most common forms of data presentation. In this, data is a part of the text of the study or a part of the description of the subject matter of the study. It is usually preferred when the quantity of data is not very large. For example, there are 50 students in a class, among them 30 are boys and 20 are girls. This is the data that can be understood with the help of a simple text and no table or pie diagram is required for the same.

Suitability

Textual Presentation of Data is suitable when the quantity of data is not large. It means that a small portion of data that is presented as a part of the subject matter of study can become useful supportive evidence to the given text. Therefore, instead of saying that the price of petrol is skyrocketing, it can be said that the price of petrol has increased by 20% in the last 2 years, and this statement will be more meaningful and precise. Under textual presentation of data, an individual does not have to support the text with the help of a diagram or table as the text in itself is very small and has few observations.

Advantages of Textual Presentation of Data

Textual Presentation of Data has the following benefits:

1. It allows the researcher to make an elaborate interpretation of data during the presentation.

2. A researcher can easily present qualitative data that cannot be presented in tabular or graphical form using the textual presentation of data.

3. If the data is present in small sets, a textual presentation can be easily used. For example, there are 50 students in a class, among them, 30 are boys and 20 are girls. This is the data that can be understood with the help of a simple text and no table or pie diagram is required for the same.

Disadvantages of Textual Presentation of Data

Textual Presentation of Data has the following drawbacks:

1. One of the major drawbacks of the textual presentation of data is that it provides extensive data in the form of text and paragraphs which makes it difficult for the user of data to draw a proper conclusion at a glance. This facility is provided in tabular or diagrammatic presentation of data.

2. This method of presenting data is not suitable for large sets of data as these sets contain too many details.

3. Besides, one has to read through the whole text in order to understand and comprehend the main point of the data.

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Textual And Tabular Presentation Of Data

Think about a scenario where your report cards are printed in a textual format. Your grades and remarks about you are presented in a paragraph format instead of data tables. Would be very confusing right? This is why data must be presented correctly and clearly. Let us take a look.

Textual Presentation

The discussion about the presentation of data starts off with it’s most raw and vague form which is the textual presentation. In such form of presentation, data is simply mentioned as mere text, that is generally in a paragraph. This is commonly used when the data is not very large.

This kind of representation is useful when we are looking to supplement qualitative statements with some data. For this purpose, the data should not be voluminously represented in tables or diagrams. It just has to be a statement that serves as a fitting evidence to our qualitative evidence and helps the reader to get an idea of the scale of a phenomenon .

For example, “the 2002 earthquake proved to be a mass murderer of humans . As many as 10,000 citizens have been reported dead”. The textual representation of data simply requires some intensive reading. This is because the quantitative statement just serves as an evidence of the qualitative statements and one has to go through the entire text before concluding anything.

Further, if the data under consideration is large then the text matter increases substantially. As a result, the reading process becomes more intensive, time-consuming and cumbersome.

Data Tables or Tabular Presentation

A table facilitates representation of even large amounts of data in an attractive, easy to read and organized manner. The data is organized in rows and columns. This is one of the most widely used forms of presentation of data since data tables are easy to construct and read.

Components of Data Tables

Table Number : Each table should have a specific table number for ease of access and locating. This number can be readily mentioned anywhere which serves as a reference and leads us directly to the data mentioned in that particular table.
Title: A table must contain a title that clearly tells the readers about the data it contains, time period of study, place of study and the nature of classification of data .
Headnotes: A headnote further aids in the purpose of a title and displays more information about the table. Generally, headnotes present the units of data in brackets at the end of a table title.
Stubs: These are titles of the rows in a table. Thus a stub display information about the data contained in a particular row.
Caption: A caption is the title of a column in the data table. In fact, it is a counterpart if a stub and indicates the information contained in a column.
Body or field: The body of a table is the content of a table in its entirety. Each item in a body is known as a ‘cell’.
Footnotes: Footnotes are rarely used. In effect, they supplement the title of a table if required.
Source: When using data obtained from a secondary source, this source has to be mentioned below the footnote.

Construction of Data Tables

There are many ways for construction of a good table. However, some basic ideas are:

The title should be in accordance with the objective of study: The title of a table should provide a quick insight into the table.
Comparison: If there might arise a need to compare any two rows or columns then these might be kept close to each other.
Alternative location of stubs: If the rows in a data table are lengthy, then the stubs can be placed on the right-hand side of the table.
Headings: Headings should be written in a singular form. For example, ‘good’ must be used instead of ‘goods’.
Footnote: A footnote should be given only if needed.
Size of columns: Size of columns must be uniform and symmetrical.
Use of abbreviations: Headings and sub-headings should be free of abbreviations.
Units: There should be a clear specification of units above the columns.

The Advantages of Tabular Presentation

Ease of representation: A large amount of data can be easily confined in a data table. Evidently, it is the simplest form of data presentation.
Ease of analysis: Data tables are frequently used for statistical analysis like calculation of central tendency, dispersion etc.
Helps in comparison: In a data table, the rows and columns which are required to be compared can be placed next to each other. To point out, this facilitates comparison as it becomes easy to compare each value.
Economical: Construction of a data table is fairly easy and presents the data in a manner which is really easy on the eyes of a reader. Moreover, it saves time as well as space.

Classification of Data and Tabular Presentation

Qualitative classification.

In this classification, data in a table is classified on the basis of qualitative attributes. In other words, if the data contained attributes that cannot be quantified like rural-urban, boys-girls etc. it can be identified as a qualitative classification of data.

Quantitative Classification

In quantitative classification, data is classified on basis of quantitative attributes.

Temporal Classification

Here data is classified according to time. Thus when data is mentioned with respect to different time frames, we term such a classification as temporal.

Spatial Classification

When data is classified according to a location, it becomes a spatial classification.

A Solved Example for You

Q: The classification in which data in a table is classified according to time is known as:

Qualitative
Quantitative

Ans: The form of classification in which data is classified based on time frames is known as the temporal classification of data and tabular presentation.

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Diagrammatic Presentation of Data

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Home Blog Design Understanding Data Presentations (Guide + Examples)

Understanding Data Presentations (Guide + Examples)

Cover for guide on data presentation by SlideModel

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Presentation of the data through bar charts

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

Product A: Men’s Shoes
Product B: Women’s Apparel
Product C: Electronics
Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

Men’s Shoes (Product A): Yellow
Women’s Apparel (Product B): Orange
Electronics (Product C): Violet
Home Decor (Product D): Blue

Accurate bar chart representation of data with a color coded legend

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

January: $45,000
February: $55,000
March: $45,000
April: $60,000
May: $ 70,000
June: $65,000
July: $62,000
August: $68,000
September: $81,000
October: $76,000
November: $87,000
December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

Top-level rectangle: Total Budget
Second-level rectangles: Departments (Engineering, Marketing, Sales)
Third-level rectangles: Projects within each department
Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left. Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies. It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides. You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

60-69: 1 student (Kate)
70-79: 4 students (David, Emma, Grace, Jack)
80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency. To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns.

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas.

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions.

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart . https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts. https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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Data presentation: A comprehensive guide

Learn how to create data presentation effectively and communicate your insights in a way that is clear, concise, and engaging.

Raja Bothra

Building presentations

Hey there, fellow data enthusiast!

Welcome to our comprehensive guide on data presentation.

Whether you're an experienced presenter or just starting, this guide will help you present your data like a pro.

We'll dive deep into what data presentation is, why it's crucial, and how to master it. So, let's embark on this data-driven journey together.

What is data presentation?

Data presentation is the art of transforming raw data into a visual format that's easy to understand and interpret. It's like turning numbers and statistics into a captivating story that your audience can quickly grasp. When done right, data presentation can be a game-changer, enabling you to convey complex information effectively.

Why are data presentations important?

Imagine drowning in a sea of numbers and figures. That's how your audience might feel without proper data presentation. Here's why it's essential:

Clarity : Data presentations make complex information clear and concise.
Engagement : Visuals, such as charts and graphs, grab your audience's attention.
Comprehension : Visual data is easier to understand than long, numerical reports.
Decision-making : Well-presented data aids informed decision-making.
Impact : It leaves a lasting impression on your audience.

Types of data presentation

Now, let's delve into the diverse array of data presentation methods, each with its own unique strengths and applications. We have three primary types of data presentation, and within these categories, numerous specific visualization techniques can be employed to effectively convey your data.

1. Textual presentation

Textual presentation harnesses the power of words and sentences to elucidate and contextualize your data. This method is commonly used to provide a narrative framework for the data, offering explanations, insights, and the broader implications of your findings. It serves as a foundation for a deeper understanding of the data's significance.

2. Tabular presentation

Tabular presentation employs tables to arrange and structure your data systematically. These tables are invaluable for comparing various data groups or illustrating how data evolves over time. They present information in a neat and organized format, facilitating straightforward comparisons and reference points.

3. Graphical presentation

Graphical presentation harnesses the visual impact of charts and graphs to breathe life into your data. Charts and graphs are powerful tools for spotlighting trends, patterns, and relationships hidden within the data. Let's explore some common graphical presentation methods:

Bar charts: They are ideal for comparing different categories of data. In this method, each category is represented by a distinct bar, and the height of the bar corresponds to the value it represents. Bar charts provide a clear and intuitive way to discern differences between categories.
Pie charts: It excel at illustrating the relative proportions of different data categories. Each category is depicted as a slice of the pie, with the size of each slice corresponding to the percentage of the total value it represents. Pie charts are particularly effective for showcasing the distribution of data.
Line graphs: They are the go-to choice when showcasing how data evolves over time. Each point on the line represents a specific value at a particular time period. This method enables viewers to track trends and fluctuations effortlessly, making it perfect for visualizing data with temporal dimensions.
Scatter plots: They are the tool of choice when exploring the relationship between two variables. In this method, each point on the plot represents a pair of values for the two variables in question. Scatter plots help identify correlations, outliers, and patterns within data pairs.

The selection of the most suitable data presentation method hinges on the specific dataset and the presentation's objectives. For instance, when comparing sales figures of different products, a bar chart shines in its simplicity and clarity. On the other hand, if your aim is to display how a product's sales have changed over time, a line graph provides the ideal visual narrative.

Additionally, it's crucial to factor in your audience's level of familiarity with data presentations. For a technical audience, more intricate visualization methods may be appropriate. However, when presenting to a general audience, opting for straightforward and easily understandable visuals is often the wisest choice.

In the world of data presentation, choosing the right method is akin to selecting the perfect brush for a masterpiece. Each tool has its place, and understanding when and how to use them is key to crafting compelling and insightful presentations. So, consider your data carefully, align your purpose, and paint a vivid picture that resonates with your audience.

What to include in data presentation

When creating your data presentation, remember these key components:

Data points : Clearly state the data points you're presenting.
Comparison : Highlight comparisons and trends in your data.
Graphical methods : Choose the right chart or graph for your data.
Infographics : Use visuals like infographics to make information more digestible.
Numerical values : Include numerical values to support your visuals.
Qualitative information : Explain the significance of the data.
Source citation : Always cite your data sources.

How to structure an effective data presentation

Creating a well-structured data presentation is not just important; it's the backbone of a successful presentation. Here's a step-by-step guide to help you craft a compelling and organized presentation that captivates your audience:

1. Know your audience

Understanding your audience is paramount. Consider their needs, interests, and existing knowledge about your topic. Tailor your presentation to their level of understanding, ensuring that it resonates with them on a personal level. Relevance is the key.

2. Have a clear message

Every effective data presentation should convey a clear and concise message. Determine what you want your audience to learn or take away from your presentation, and make sure your message is the guiding light throughout your presentation. Ensure that all your data points align with and support this central message.

3. Tell a compelling story

Human beings are naturally wired to remember stories. Incorporate storytelling techniques into your presentation to make your data more relatable and memorable. Your data can be the backbone of a captivating narrative, whether it's about a trend, a problem, or a solution. Take your audience on a journey through your data.

4. Leverage visuals

Visuals are a powerful tool in data presentation. They make complex information accessible and engaging. Utilize charts, graphs, and images to illustrate your points and enhance the visual appeal of your presentation. Visuals should not just be an accessory; they should be an integral part of your storytelling.

5. Be clear and concise

Avoid jargon or technical language that your audience may not comprehend. Use plain language and explain your data points clearly. Remember, clarity is king. Each piece of information should be easy for your audience to digest.

6. Practice your delivery

Practice makes perfect. Rehearse your presentation multiple times before the actual delivery. This will help you deliver it smoothly and confidently, reducing the chances of stumbling over your words or losing track of your message.

A basic structure for an effective data presentation

Armed with a comprehensive comprehension of how to construct a compelling data presentation, you can now utilize this fundamental template for guidance:

In the introduction, initiate your presentation by introducing both yourself and the topic at hand. Clearly articulate your main message or the fundamental concept you intend to communicate.

Moving on to the body of your presentation, organize your data in a coherent and easily understandable sequence. Employ visuals generously to elucidate your points and weave a narrative that enhances the overall story. Ensure that the arrangement of your data aligns with and reinforces your central message.

As you approach the conclusion, succinctly recapitulate your key points and emphasize your core message once more. Conclude by leaving your audience with a distinct and memorable takeaway, ensuring that your presentation has a lasting impact.

Additional tips for enhancing your data presentation

To take your data presentation to the next level, consider these additional tips:

Consistent design : Maintain a uniform design throughout your presentation. This not only enhances visual appeal but also aids in seamless comprehension.
High-quality visuals : Ensure that your visuals are of high quality, easy to read, and directly relevant to your topic.
Concise text : Avoid overwhelming your slides with excessive text. Focus on the most critical points, using visuals to support and elaborate.
Anticipate questions : Think ahead about the questions your audience might pose. Be prepared with well-thought-out answers to foster productive discussions.

By following these guidelines, you can structure an effective data presentation that not only informs but also engages and inspires your audience. Remember, a well-structured presentation is the bridge that connects your data to your audience's understanding and appreciation.

Do’s and don'ts on a data presentation

Use visuals : Incorporate charts and graphs to enhance understanding.
Keep it simple : Avoid clutter and complexity.
Highlight key points : Emphasize crucial data.
Engage the audience : Encourage questions and discussions.
Practice : Rehearse your presentation.

Don'ts:

Overload with data : Less is often more; don't overwhelm your audience.
Fit Unrelated data : Stay on topic; don't include irrelevant information.
Neglect the audience : Ensure your presentation suits your audience's level of expertise.
Read word-for-word : Avoid reading directly from slides.
Lose focus : Stick to your presentation's purpose.

Summarizing key takeaways

Definition : Data presentation is the art of visualizing complex data for better understanding.
Importance : Data presentations enhance clarity, engage the audience, aid decision-making, and leave a lasting impact.
Types : Textual, Tabular, and Graphical presentations offer various ways to present data.
Choosing methods : Select the right method based on data, audience, and purpose.
Components : Include data points, comparisons, visuals, infographics, numerical values, and source citations.
Structure : Know your audience, have a clear message, tell a compelling story, use visuals, be concise, and practice.
Do's and don'ts : Do use visuals, keep it simple, highlight key points, engage the audience, and practice. Don't overload with data, include unrelated information, neglect the audience's expertise, read word-for-word, or lose focus.

1. What is data presentation, and why is it important in 2023?

Data presentation is the process of visually representing data sets to convey information effectively to an audience. In an era where the amount of data generated is vast, visually presenting data using methods such as diagrams, graphs, and charts has become crucial. By simplifying complex data sets, presentation of the data may helps your audience quickly grasp much information without drowning in a sea of chart's, analytics, facts and figures.

2. What are some common methods of data presentation?

There are various methods of data presentation, including graphs and charts, histograms, and cumulative frequency polygons. Each method has its strengths and is often used depending on the type of data you're using and the message you want to convey. For instance, if you want to show data over time, try using a line graph. If you're presenting geographical data, consider to use a heat map.

3. How can I ensure that my data presentation is clear and readable?

To ensure that your data presentation is clear and readable, pay attention to the design and labeling of your charts. Don't forget to label the axes appropriately, as they are critical for understanding the values they represent. Don't fit all the information in one slide or in a single paragraph. Presentation software like Prezent and PowerPoint can help you simplify your vertical axis, charts and tables, making them much easier to understand.

4. What are some common mistakes presenters make when presenting data?

One common mistake is trying to fit too much data into a single chart, which can distort the information and confuse the audience. Another mistake is not considering the needs of the audience. Remember that your audience won't have the same level of familiarity with the data as you do, so it's essential to present the data effectively and respond to questions during a Q&A session.

5. How can I use data visualization to present important data effectively on platforms like LinkedIn?

When presenting data on platforms like LinkedIn, consider using eye-catching visuals like bar graphs or charts. Use concise captions and e.g., examples to highlight the single most important information in your data report. Visuals, such as graphs and tables, can help you stand out in the sea of textual content, making your data presentation more engaging and shareable among your LinkedIn connections.

Create your data presentation with prezent

Prezent can be a valuable tool for creating data presentations. Here's how Prezent can help you in this regard:

Time savings : Prezent saves up to 70% of presentation creation time, allowing you to focus on data analysis and insights.
On-brand consistency : Ensure 100% brand alignment with Prezent's brand-approved designs for professional-looking data presentations.
Effortless collaboration : Real-time sharing and collaboration features make it easy for teams to work together on data presentations.
Data storytelling : Choose from 50+ storylines to effectively communicate data insights and engage your audience.
Personalization : Create tailored data presentations that resonate with your audience's preferences, enhancing the impact of your data.

In summary, Prezent streamlines the process of creating data presentations by offering time-saving features, ensuring brand consistency, promoting collaboration, and providing tools for effective data storytelling. Whether you need to present data to clients, stakeholders, or within your organization, Prezent can significantly enhance your presentation-making process.

So, go ahead, present your data with confidence, and watch your audience be wowed by your expertise.

Thank you for joining us on this data-driven journey. Stay tuned for more insights, and remember, data presentation is your ticket to making numbers come alive!

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TEXTUAL, TABULAR & DIAGRAMMATIC PRESENTATION OF DATA

STATISTICS : PRESENTATION OF DATA

Data can be presented in three ways:

Textual presentation
Tabular presentation
Diagrammatic presentation

1. Textual Mode of presentation is layman’s method of presentation of data. Anyone can prepare, anyone can understand. No specific skill(s) is/are required.

2. Tabular Mode of presentation is the most accurate mode of presentation of data. It requires a lot of skill to prepare, and some skill(s) to understand. Table facilitates comparison.

But, Table should be good enough as per some points of view:

1. Appealing
2. Well-balanced
3. Compulsory Title and Table Number
4. Title should be self-explanatory
5. Units must be properly mentioned
6. Comparison should be easy
7. Sources and footnotes (if any) must be mentioned at the bottom

Below is a sample of how a table should look like:

Table No. 1: Format of a table

* Sources: 1. Kailasha Foundation – Fun & Learn Portal LMS Directory *Footnotes: The entire upper part of the table is called BOX HEAD.

3. Diagrammatic Mode of Presentation:

A. Non-Frequency Diagrams: Non-frequency diagrams correspond to the data which are NOT frequency data. (a) Bar Diagrams (b) Line Diagrams (Historiagram) (c) Pie Diagram or Pie Chart

B. Frequency Diagrams: Frequency Data are presented. Mostly class-intervals are presented via this mode. Three most common frequency diagrams are: (a) Histogram (b) Frequency Polygon (c) Ogives: (i) Less than type Ogives (ii) More than type Ogives

1. Bar Diagram and Line Diagram are inter-convertible
2. Bar Diagram and Line Diagram can both be of simple and multiple types
3. Multiple bar diagram or Multiple Line diagram is used when two related series (in same unit) are to be compared
4. Multiple axis bar diagram or Multiple axis Line diagram is used when units in the two series are different

ILLUSTRATIONS OF PRESENTATION OF DATA:

Bar Diagrams:

Line Diagram:

Multiple Bar Diagram:

Frequency Polygon:

FREQUENCY CURVE:

A smooth join of all vertices of a frequency polygon. This is broadly divided into four shapes:

(i) Bell Shaped (Most Common Shape) (ii) U-Shaped (iii) J – Shaped: Simple J – shaped & Inverted J – Shaped (iv) Mixed Curve (Second Most Common Shape)

1. CENSUS: The collection of data from every element in a population or universe or arena of statistical enquiry.
2. SAMPLE: The collection of data from subgroup or subset of the population.
3. FREQUENCY: The number of times a certain value or class of values occurs.
4. CUMULATIVE FREQUENCY: The running total of the frequencies at each class interval level.
5. FREQUENCY DISTRIBUTION: The organization of raw data in table form with classes and frequencies.
6. CLASS LIMITS: The originally assigned extreme values of classes are called class limits, viz. Lower class limit and upper class limit.
7. CLASS WIDTH: The difference between the upper and lower boundaries (NOT limits) of any class.
8. CLASS BOUNDARY: After making the distribution continuous, the upper class boundary of a class becomes equal to the lower class boundary of the next class.
9. CLASS MARK: The mid-point of any class is called the class mark.

VIDEO DESCRIPTIONS:

Hindi explanation:.

ENGLISH EXPLANATION:

Thanks for learning at Kailasha Foundation – Fun & Learn Portal.

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Become an actuary and step out of the crowd

Become an actuary to step out of the crowd

NOUNS – Lecture 1

10 Data Presentation Examples For Strategic Communication

Written by: Krystle Wong Sep 28, 2023

Knowing how to present data is like having a superpower.

Data presentation today is no longer just about numbers on a screen; it’s storytelling with a purpose. It’s about captivating your audience, making complex stuff look simple and inspiring action.

To help turn your data into stories that stick, influence decisions and make an impact, check out Venngage’s free chart maker or follow me on a tour into the world of data storytelling along with data presentation templates that work across different fields, from business boardrooms to the classroom and beyond. Keep scrolling to learn more!

Click to jump ahead:

10 Essential data presentation examples + methods you should know

What should be included in a data presentation, what are some common mistakes to avoid when presenting data, faqs on data presentation examples, transform your message with impactful data storytelling.

Data presentation is a vital skill in today’s information-driven world. Whether you’re in business, academia, or simply want to convey information effectively, knowing the different ways of presenting data is crucial. For impactful data storytelling, consider these essential data presentation methods:

1. Bar graph

Ideal for comparing data across categories or showing trends over time.

Bar graphs, also known as bar charts are workhorses of data presentation. They’re like the Swiss Army knives of visualization methods because they can be used to compare data in different categories or display data changes over time.

In a bar chart, categories are displayed on the x-axis and the corresponding values are represented by the height of the bars on the y-axis.

It’s a straightforward and effective way to showcase raw data, making it a staple in business reports, academic presentations and beyond.

Make sure your bar charts are concise with easy-to-read labels. Whether your bars go up or sideways, keep it simple by not overloading with too many categories.

2. Line graph

Great for displaying trends and variations in data points over time or continuous variables.

Line charts or line graphs are your go-to when you want to visualize trends and variations in data sets over time.

One of the best quantitative data presentation examples, they work exceptionally well for showing continuous data, such as sales projections over the last couple of years or supply and demand fluctuations.

The x-axis represents time or a continuous variable and the y-axis represents the data values. By connecting the data points with lines, you can easily spot trends and fluctuations.

A tip when presenting data with line charts is to minimize the lines and not make it too crowded. Highlight the big changes, put on some labels and give it a catchy title.

3. Pie chart

Useful for illustrating parts of a whole, such as percentages or proportions.

Pie charts are perfect for showing how a whole is divided into parts. They’re commonly used to represent percentages or proportions and are great for presenting survey results that involve demographic data.

Each “slice” of the pie represents a portion of the whole and the size of each slice corresponds to its share of the total.

While pie charts are handy for illustrating simple distributions, they can become confusing when dealing with too many categories or when the differences in proportions are subtle.

Don’t get too carried away with slices — label those slices with percentages or values so people know what’s what and consider using a legend for more categories.

4. Scatter plot

Effective for showing the relationship between two variables and identifying correlations.

Scatter plots are all about exploring relationships between two variables. They’re great for uncovering correlations, trends or patterns in data.

In a scatter plot, every data point appears as a dot on the chart, with one variable marked on the horizontal x-axis and the other on the vertical y-axis.

By examining the scatter of points, you can discern the nature of the relationship between the variables, whether it’s positive, negative or no correlation at all.

If you’re using scatter plots to reveal relationships between two variables, be sure to add trendlines or regression analysis when appropriate to clarify patterns. Label data points selectively or provide tooltips for detailed information.

5. Histogram

Best for visualizing the distribution and frequency of a single variable.

Histograms are your choice when you want to understand the distribution and frequency of a single variable.

They divide the data into “bins” or intervals and the height of each bar represents the frequency or count of data points falling into that interval.

Histograms are excellent for helping to identify trends in data distributions, such as peaks, gaps or skewness.

Here’s something to take note of — ensure that your histogram bins are appropriately sized to capture meaningful data patterns. Using clear axis labels and titles can also help explain the distribution of the data effectively.

6. Stacked bar chart

Useful for showing how different components contribute to a whole over multiple categories.

Stacked bar charts are a handy choice when you want to illustrate how different components contribute to a whole across multiple categories.

Each bar represents a category and the bars are divided into segments to show the contribution of various components within each category.

This method is ideal for highlighting both the individual and collective significance of each component, making it a valuable tool for comparative analysis.

Stacked bar charts are like data sandwiches—label each layer so people know what’s what. Keep the order logical and don’t forget the paintbrush for snazzy colors. Here’s a data analysis presentation example on writers’ productivity using stacked bar charts:

7. Area chart

Similar to line charts but with the area below the lines filled, making them suitable for showing cumulative data.

Area charts are close cousins of line charts but come with a twist.

Imagine plotting the sales of a product over several months. In an area chart, the space between the line and the x-axis is filled, providing a visual representation of the cumulative total.

This makes it easy to see how values stack up over time, making area charts a valuable tool for tracking trends in data.

For area charts, use them to visualize cumulative data and trends, but avoid overcrowding the chart. Add labels, especially at significant points and make sure the area under the lines is filled with a visually appealing color gradient.

8. Tabular presentation

Presenting data in rows and columns, often used for precise data values and comparisons.

Tabular data presentation is all about clarity and precision. Think of it as presenting numerical data in a structured grid, with rows and columns clearly displaying individual data points.

A table is invaluable for showcasing detailed data, facilitating comparisons and presenting numerical information that needs to be exact. They’re commonly used in reports, spreadsheets and academic papers.

When presenting tabular data, organize it neatly with clear headers and appropriate column widths. Highlight important data points or patterns using shading or font formatting for better readability.

9. Textual data

Utilizing written or descriptive content to explain or complement data, such as annotations or explanatory text.

Textual data presentation may not involve charts or graphs, but it’s one of the most used qualitative data presentation examples.

It involves using written content to provide context, explanations or annotations alongside data visuals. Think of it as the narrative that guides your audience through the data.

Well-crafted textual data can make complex information more accessible and help your audience understand the significance of the numbers and visuals.

Textual data is your chance to tell a story. Break down complex information into bullet points or short paragraphs and use headings to guide the reader’s attention.

10. Pictogram

Using simple icons or images to represent data is especially useful for conveying information in a visually intuitive manner.

Pictograms are all about harnessing the power of images to convey data in an easy-to-understand way.

Instead of using numbers or complex graphs, you use simple icons or images to represent data points.

For instance, you could use a thumbs up emoji to illustrate customer satisfaction levels, where each face represents a different level of satisfaction.

Pictograms are great for conveying data visually, so choose symbols that are easy to interpret and relevant to the data. Use consistent scaling and a legend to explain the symbols’ meanings, ensuring clarity in your presentation.

Looking for more data presentation ideas? Use the Venngage graph maker or browse through our gallery of chart templates to pick a template and get started!

A comprehensive data presentation should include several key elements to effectively convey information and insights to your audience. Here’s a list of what should be included in a data presentation:

1. Title and objective

Begin with a clear and informative title that sets the context for your presentation.
State the primary objective or purpose of the presentation to provide a clear focus.

2. Key data points

Present the most essential data points or findings that align with your objective.
Use charts, graphical presentations or visuals to illustrate these key points for better comprehension.

3. Context and significance

Provide a brief overview of the context in which the data was collected and why it’s significant.
Explain how the data relates to the larger picture or the problem you’re addressing.

4. Key takeaways

Summarize the main insights or conclusions that can be drawn from the data.
Highlight the key takeaways that the audience should remember.

5. Visuals and charts

Use clear and appropriate visual aids to complement the data.
Ensure that visuals are easy to understand and support your narrative.

6. Implications or actions

Discuss the practical implications of the data or any recommended actions.
If applicable, outline next steps or decisions that should be taken based on the data.

7. Q&A and discussion

Allocate time for questions and open discussion to engage the audience.
Address queries and provide additional insights or context as needed.

Presenting data is a crucial skill in various professional fields, from business to academia and beyond. To ensure your data presentations hit the mark, here are some common mistakes that you should steer clear of:

Overloading with data

Presenting too much data at once can overwhelm your audience. Focus on the key points and relevant information to keep the presentation concise and focused. Here are some free data visualization tools you can use to convey data in an engaging and impactful way.

Assuming everyone’s on the same page

It’s easy to assume that your audience understands as much about the topic as you do. But this can lead to either dumbing things down too much or diving into a bunch of jargon that leaves folks scratching their heads. Take a beat to figure out where your audience is coming from and tailor your presentation accordingly.

Misleading visuals

Using misleading visuals, such as distorted scales or inappropriate chart types can distort the data’s meaning. Pick the right data infographics and understandable charts to ensure that your visual representations accurately reflect the data.

Not providing context

Data without context is like a puzzle piece with no picture on it. Without proper context, data may be meaningless or misinterpreted. Explain the background, methodology and significance of the data.

Not citing sources properly

Neglecting to cite sources and provide citations for your data can erode its credibility. Always attribute data to its source and utilize reliable sources for your presentation.

Not telling a story

Avoid simply presenting numbers. If your presentation lacks a clear, engaging story that takes your audience on a journey from the beginning (setting the scene) through the middle (data analysis) to the end (the big insights and recommendations), you’re likely to lose their interest.

Infographics are great for storytelling because they mix cool visuals with short and sweet text to explain complicated stuff in a fun and easy way. Create one with Venngage’s free infographic maker to create a memorable story that your audience will remember.

Ignoring data quality

Presenting data without first checking its quality and accuracy can lead to misinformation. Validate and clean your data before presenting it.

Simplify your visuals

Fancy charts might look cool, but if they confuse people, what’s the point? Go for the simplest visual that gets your message across. Having a dilemma between presenting data with infographics v.s data design? This article on the difference between data design and infographics might help you out.

Missing the emotional connection

Data isn’t just about numbers; it’s about people and real-life situations. Don’t forget to sprinkle in some human touch, whether it’s through relatable stories, examples or showing how the data impacts real lives.

Skipping the actionable insights

At the end of the day, your audience wants to know what they should do with all the data. If you don’t wrap up with clear, actionable insights or recommendations, you’re leaving them hanging. Always finish up with practical takeaways and the next steps.

Can you provide some data presentation examples for business reports?

Business reports often benefit from data presentation through bar charts showing sales trends over time, pie charts displaying market share,or tables presenting financial performance metrics like revenue and profit margins.

What are some creative data presentation examples for academic presentations?

Creative data presentation ideas for academic presentations include using statistical infographics to illustrate research findings and statistical data, incorporating storytelling techniques to engage the audience or utilizing heat maps to visualize data patterns.

What are the key considerations when choosing the right data presentation format?

When choosing a chart format , consider factors like data complexity, audience expertise and the message you want to convey. Options include charts (e.g., bar, line, pie), tables, heat maps, data visualization infographics and interactive dashboards.

Knowing the type of data visualization that best serves your data is just half the battle. Here are some best practices for data visualization to make sure that the final output is optimized.

How can I choose the right data presentation method for my data?

To select the right data presentation method, start by defining your presentation’s purpose and audience. Then, match your data type (e.g., quantitative, qualitative) with suitable visualization techniques (e.g., histograms, word clouds) and choose an appropriate presentation format (e.g., slide deck, report, live demo).

For more presentation ideas , check out this guide on how to make a good presentation or use a presentation software to simplify the process.

How can I make my data presentations more engaging and informative?

To enhance data presentations, use compelling narratives, relatable examples and fun data infographics that simplify complex data. Encourage audience interaction, offer actionable insights and incorporate storytelling elements to engage and inform effectively.

The opening of your presentation holds immense power in setting the stage for your audience. To design a presentation and convey your data in an engaging and informative, try out Venngage’s free presentation maker to pick the right presentation design for your audience and topic.

What is the difference between data visualization and data presentation?

Data presentation typically involves conveying data reports and insights to an audience, often using visuals like charts and graphs. Data visualization , on the other hand, focuses on creating those visual representations of data to facilitate understanding and analysis.

Now that you’ve learned a thing or two about how to use these methods of data presentation to tell a compelling data story , it’s time to take these strategies and make them your own.

But here’s the deal: these aren’t just one-size-fits-all solutions. Remember that each example we’ve uncovered here is not a rigid template but a source of inspiration. It’s all about making your audience go, “Wow, I get it now!”

Think of your data presentations as your canvas – it’s where you paint your story, convey meaningful insights and make real change happen.

So, go forth, present your data with confidence and purpose and watch as your strategic influence grows, one compelling presentation at a time.

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Present Your Data Like a Pro

Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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Making Data Talk: The Science and Practice of Translating Public Health Research and Surveillance Findings to Policy Makers, the Public, and the Press

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4 Presenting Data

Published: July 2009
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Data presentation can greatly influence audiences. This chapter reviews principles and approaches for presenting data, focusing on whether data needs to be used. Data can presented using words alone (e.g., metaphors or narratives), numbers (e.g., tables), symbols (e.g., bar charts or line graphs), or some combination that integrates these methods. Although new software packages and advanced techniques are available, visual symbols that can most readily and effectively communicate public health data are pie charts, bar charts, line graphs, icons/icon arrays, visual scales, and maps. Perceptual cues, especially proximity, continuation, and closure, influence how people process information. Contextual cues help enhance meaning by providing sufficient context to help audiences better understand data. Effective data presentation depends upon articulating the purpose for communicating, understanding audiences and context, and developing storylines to be communicated, taking into account the need to present data ethically and in a manner easily understood.

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1.3: Presentation of Data

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Learning Objectives

To learn two ways that data will be presented in the text.

In this book we will use two formats for presenting data sets. The first is a data list, which is an explicit listing of all the individual measurements, either as a display with space between the individual measurements, or in set notation with individual measurements separated by commas.

Example $\PageIndex{1}$

The data obtained by measuring the age of $21$ randomly selected students enrolled in freshman courses at a university could be presented as the data list:

\[\begin{array}{cccccccccc}18 & 18 & 19 & 19 & 19 & 18 & 22 & 20 & 18 & 18 & 17 \\ 19 & 18 & 24 & 18 & 20 & 18 & 21 & 20 & 17 & 19 &\end{array} \nonumber \]

or in set notation as:

\[ \{18,18,19,19,19,18,22,20,18,18,17,19,18,24,18,20,18,21,20,17,19\} \nonumber \]

A data set can also be presented by means of a data frequency table, a table in which each distinct value $x$ is listed in the first row and its frequency $f$, which is the number of times the value $x$ appears in the data set, is listed below it in the second row.

Example $\PageIndex{2}$

The data set of the previous example is represented by the data frequency table

\[\begin{array}{c|cccccc}x & 17 & 18 & 19 & 20 & 21 & 22 & 24 \\ \hline f & 2 & 8 & 5 & 3 & 1 & 1 & 1\end{array} \nonumber \]

The data frequency table is especially convenient when data sets are large and the number of distinct values is not too large.

Key Takeaway

Data sets can be presented either by listing all the elements or by giving a table of values and frequencies.

Presentation of Data

Statistics deals with the collection, presentation and analysis of the data, as well as drawing meaningful conclusions from the given data. Generally, the data can be classified into two different types, namely primary data and secondary data. If the information is collected by the investigator with a definite objective in their mind, then the data obtained is called the primary data. If the information is gathered from a source, which already had the information stored, then the data obtained is called secondary data. Once the data is collected, the presentation of data plays a major role in concluding the result. Here, we will discuss how to present the data with many solved examples.

What is Meant by Presentation of Data?

As soon as the data collection is over, the investigator needs to find a way of presenting the data in a meaningful, efficient and easily understood way to identify the main features of the data at a glance using a suitable presentation method. Generally, the data in the statistics can be presented in three different forms, such as textual method, tabular method and graphical method.

Presentation of Data Examples

Now, let us discuss how to present the data in a meaningful way with the help of examples.

Consider the marks given below, which are obtained by 10 students in Mathematics:

36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

Find the range for the given data.

Given Data: 36, 55, 73, 95, 42, 60, 78, 25, 62, 75.

The data given is called the raw data.

First, arrange the data in the ascending order : 25, 36, 42, 55, 60, 62, 73, 75, 78, 95.

Therefore, the lowest mark is 25 and the highest mark is 95.

We know that the range of the data is the difference between the highest and the lowest value in the dataset.

Therefore, Range = 95-25 = 70.

Note: Presentation of data in ascending or descending order can be time-consuming if we have a larger number of observations in an experiment.

Now, let us discuss how to present the data if we have a comparatively more number of observations in an experiment.

Consider the marks obtained by 30 students in Mathematics subject (out of 100 marks)

10, 20, 36, 92, 95, 40, 50, 56, 60, 70, 92, 88, 80, 70, 72, 70, 36, 40, 36, 40, 92, 40, 50, 50, 56, 60, 70, 60, 60, 88.

In this example, the number of observations is larger compared to example 1. So, the presentation of data in ascending or descending order is a bit time-consuming. Hence, we can go for the method called ungrouped frequency distribution table or simply frequency distribution table . In this method, we can arrange the data in tabular form in terms of frequency.

For example, 3 students scored 50 marks. Hence, the frequency of 50 marks is 3. Now, let us construct the frequency distribution table for the given data.

Therefore, the presentation of data is given as below:

The following example shows the presentation of data for the larger number of observations in an experiment.

Consider the marks obtained by 100 students in a Mathematics subject (out of 100 marks)

95, 67, 28, 32, 65, 65, 69, 33, 98, 96,76, 42, 32, 38, 42, 40, 40, 69, 95, 92, 75, 83, 76, 83, 85, 62, 37, 65, 63, 42, 89, 65, 73, 81, 49, 52, 64, 76, 83, 92, 93, 68, 52, 79, 81, 83, 59, 82, 75, 82, 86, 90, 44, 62, 31, 36, 38, 42, 39, 83, 87, 56, 58, 23, 35, 76, 83, 85, 30, 68, 69, 83, 86, 43, 45, 39, 83, 75, 66, 83, 92, 75, 89, 66, 91, 27, 88, 89, 93, 42, 53, 69, 90, 55, 66, 49, 52, 83, 34, 36.

Now, we have 100 observations to present the data. In this case, we have more data when compared to example 1 and example 2. So, these data can be arranged in the tabular form called the grouped frequency table. Hence, we group the given data like 20-29, 30-39, 40-49, ….,90-99 (As our data is from 23 to 98). The grouping of data is called the “class interval” or “classes”, and the size of the class is called “class-size” or “class-width”.

In this case, the class size is 10. In each class, we have a lower-class limit and an upper-class limit. For example, if the class interval is 30-39, the lower-class limit is 30, and the upper-class limit is 39. Therefore, the least number in the class interval is called the lower-class limit and the greatest limit in the class interval is called upper-class limit.

Hence, the presentation of data in the grouped frequency table is given below:

Hence, the presentation of data in this form simplifies the data and it helps to enable the observer to understand the main feature of data at a glance.

Practice Problems

The heights of 50 students (in cms) are given below. Present the data using the grouped frequency table by taking the class intervals as 160 -165, 165 -170, and so on. Data: 161, 150, 154, 165, 168, 161, 154, 162, 150, 151, 162, 164, 171, 165, 158, 154, 156, 172, 160, 170, 153, 159, 161, 170, 162, 165, 166, 168, 165, 164, 154, 152, 153, 156, 158, 162, 160, 161, 173, 166, 161, 159, 162, 167, 168, 159, 158, 153, 154, 159.
Three coins are tossed simultaneously and each time the number of heads occurring is noted and it is given below. Present the data using the frequency distribution table. Data: 0, 1, 2, 2, 1, 2, 3, 1, 3, 0, 1, 3, 1, 1, 2, 2, 0, 1, 2, 1, 3, 0, 0, 1, 1, 2, 3, 2, 2, 0.

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Question and Answer forum for K12 Students

Textual and Tabular Presentation of Data: Classification, Data Tables etc

The compilation of these Presentation of Data Notes makes students exam preparation simpler and organised.

Textual and Tabular Presentation of Data

Presentation of Data

Presentation of data is of utter importance nowadays. After all, everything that’s pleasing to our eyes never fails to grab our attention. Presentation of data refers to an exhibition or putting up data in an attractive and useful manner such that it can be easily interpreted. The three main forms of presentation of data are:

Textual presentation
Data tables
Diagrammatic presentation

Here we will be studying only the textual and tabular presentation, i.e. data tables in some detail.

Textual Presentation

The discussion about the presentation of data starts off with its most raw and vague form which is the textual presentation. In such a form of presentation, data is simply mentioned as mere text, which is generally in a paragraph. This is commonly used when the data is not very large.

This kind of representation is useful when we are looking to supplement qualitative statements with some data. For this purpose, the data should not be voluminously represented in tables or diagrams. It just has to be a statement that serves as fitting evidence to our qualitative evidence and helps the reader to get an idea of the scale of a phenomenon.

For example, “the 2002 earthquake proved to be a mass murderer of humans. As many as 10,000 citizens have been reported dead”. The textual representation of data simply requires some intensive reading. This is because the quantitative statement just serves as evidence of the qualitative statements and one has to go through the entire text before concluding anything.

Further, if the data under consideration is large then the text matter increases substantially. As a result, the reading process becomes more intensive, time-consuming, and cumbersome.

Data Tables or Tabular Presentation

A table facilitates the representation of even large amounts of data in an attractive, easy to read, and organized manner. The data is organized in rows and columns. This is one of the most widely used forms of presentation of data since data tables are easy to construct and read.

Components of Data Tables Table Number: Each table should have a specific table number for ease of access and locating. This number can be readily mentioned anywhere which serves as a reference and leads us directly to the data mentioned in that particular table.

Title: A table must contain a title that clearly tells the readers about the data it contains, time period of study, place of study, and the nature of the classification of data.

Headnotes: A headnote further aids in the purpose of a title and displays more information about the table. Generally, headnotes present the units of data in brackets at the end of a table title.

Stubs: These are titles of the rows in a table. Thus a stub display information about the data contained in a particular row.

Caption: A caption is the title of a column in the data table. In fact, it is a counterpart if a stub and indicates the information contained in a column.

Body or field: The body of a table is the content of a table in its entirety. Each item in a body is known as a ‘cell’.

Footnotes: Footnotes are rarely used. In effect, they supplement the title of a table if required.

Source: When using data obtained from a secondary source, this source has to be mentioned below the footnote.

Construction of Data Tables There are many ways to construct a good table. However, some basic ideas are:

The title should be in accordance with the objective of the study: The title of a table should provide a quick insight into the table.

Comparison: If there might arise a need to compare any two rows or columns then these might be kept close to each other.

Alternative location of stubs: If the rows in a data table are lengthy, then the stubs can be placed on the right-hand side of the table.

Headings: Headings should be written in a singular form. For example, ‘good’ must be used instead of ‘goods’.

Footnote: A footnote should be given only if needed.

Size of columns: Size of columns must be uniform and symmetrical.

Use of abbreviations: Headings and sub-headings should be free of abbreviations.

Units: There should be a clear specification of units above the columns.

The Advantages of Tabular Presentation Ease of representation: A large amount of data can be easily confined in a data table. Evidently, it is the simplest form of data presentation.

Ease of analysis: Data tables are frequently used for statistical analysis like calculation of central tendency, dispersion, etc.

Helps in comparison: In a data table, the rows and columns which are required to be compared can be placed next to each other. To point out, this facilitates comparison as it becomes easy to compare each value.

Economical: Construction of a data table is fairly easy and presents the data in a manner which is really easy in the eyes of a reader. Moreover, it saves time as well as space.

Classification of Data and Tabular Presentation

Qualitative Classification In this classification, data in a table is classified on the basis of qualitative attributes. In other words, if the data contained attributes that cannot be quantified like rural-urban, boys-girls, etc. it can be identified as a qualitative classification of data.

Quantitative Classification In quantitative classification, data is classified on basis of quantitative attributes.

Temporal Classification Here data is classified according to time. Thus when data is mentioned with respect to different time frames, we term such a classification as temporal.

Spatial Classification When data is classified according to a location, it becomes a spatial classification.

Question: The classification in which data in a table is classified according to time is known as: 1. Qualitative 2. Quantitative 3. Temporal 4. Spatial Answer: The form of classification in which data is classified based on time frames is known as the temporal classification of data and tabular presentation.

Data Presentation

Josée Dupuis, PhD, Professor of Biostatistics, Boston University School of Public Health

Wayne LaMorte, MD, PhD, MPH, Professor of Epidemiology, Boston University School of Public Health

Introduction

While graphical summaries of data can certainly be powerful ways of communicating results clearly and unambiguously in a way that facilitates our ability to think about the information, poorly designed graphical displays can be ambiguous, confusing, and downright misleading. The keys to excellence in graphical design and communication are much like the keys to good writing. Adhere to fundamental principles of style and communicate as logically, accurately, and clearly as possible. Excellence in writing is generally achieved by avoiding unnecessary words and paragraphs; it is efficient. In a similar fashion, excellence in graphical presentation is generally achieved by efficient designs that avoid unnecessary ink.

Excellence in graphical presentation depends on:

Choosing the best medium for presenting the information
Designing the components of the graph in a way that communicates the information as clearly and accurately as possible.

Table or Graph?

Tables are generally best if you want to be able to look up specific information or if the values must be reported precisely.
Graphics are best for illustrating trends and making comparisons

The side by side illustrations below show the same information, first in table form and then in graphical form. While the information in the table is precise, the real goal is to compare a series of clinical outcomes in subjects taking either a drug or a placebo. The graphical presentation on the right makes it possible to quickly see that for each of the outcomes evaluated, the drug produced relief in a great proportion of subjects. Moreover, the viewer gets a clear sense of the magnitude of improvement, and the error bars provided a sense of the uncertainty in the data.

Principles for Table Display

Sort table rows in a meaningful way
Avoid alphabetical listing!
Use rates, proportions or ratios in addition (or instead of) totals
Show more than two time points if available
Multiple time points may be better presented in a Figure
Similar data should go down columns
Highlight important comparisons
Show the source of the data

Consider the data in the table below from http://www.cancer.gov/cancertopics/types/commoncancers

Our ability to quickly understand the relative frequency of these cancers is hampered by presenting them in alphabetical order. It is much easier for the reader to grasp the relative frequency by listing them from most frequent to least frequent as in the next table.

However, the same information might be presented more effectively with a dot plot, as shown below.

Data from http://www.cancer.gov/cancertopics/types/commoncancers

Principles of Graphical Excellence from E.R. Tufte

Pattern perception.

Pattern perception is done by

Detection: recognition of geometry encoding physical values
Assembly: grouping of detected symbol elements; discerning overall patterns in data
Estimation: assessment of relative magnitudes of two physical values

Geographic Variation in Cancer

As an example, Tufte offers a series of maps that summarize the age-adjusted mortality rates for various types of cancer in the 3,056 counties in the United States. The maps showing the geographic variation in stomach cancer are shown below.

These maps summarize an enormous amount of information and present it efficiently, coherently, and effectively.in a way that invites the viewer to make comparisons and to think about the substance of the findings. Consider, for example, that the region to the west of the Great Lakes was settled largely by immigrants from Germany and Scand anavia, where traditional methods of preserving food included pickling and curing of fish by smoking. Could these methods be associated with an increased risk of stomach cancer?

John Snow's Spot Map of Cholera Cases

Consider also the spot map that John Snow presented after the cholera outbreak in the Broad Street section of London in September 1854. Snow ascertained the place of residence or work of the victims and represented them on a map of the area using a small black disk to represent each victim and stacking them when more than one occurred at a particular location. Snow reasoned that cholera was probably caused by something that was ingested, because of the intense diarrhea and vomiting of the victims, and he noted that the vast majority of cholera deaths occurred in people who lived or worked in the immediate vicinity of the broad street pump (shown with a red dot that we added for clarity). He further ascertained that most of the victims drank water from the Broad Street pump, and it was this evidence that persuaded the authorities to remove the handle from the pump in order to prevent more deaths.

Map of the Broad Street area of London showing stacks of black disks to represent the number of cholera cases that occurred at various locations. The cases seem to be clustered around the Broad Street water pump.

Humans can readily perceive differences like this when presented effectively as in the two previous examples. However, humans are not good at estimating differences without directly seeing them (especially for steep curves), and we are particularly bad at perceiving relative angles (the principal perception task used in a pie chart).

The use of pie charts is generally discouraged. Consider the pie chart on the left below. It is difficult to accurately assess the relative size of the components in the pie chart, because the human eye has difficulty judging angles. The dot plot on the right shows the same data, but it is much easier to quickly assess the relative size of the components and how they changed from Fiscal Year 2000 to Fiscal Year 2007.

Consider the information in the two pie charts below (showing the same information).The 3-dimensional pie chart on the left distorts the relative proportions. In contrast the 2-dimensional pie chart on the right makes it much easier to compare the relative size of the varies components..

More Principles of Graphical Excellence

Exclude unneeded dimensions.

These 3-dimensional techniques distort the data and actually interfere with our ability to make accurate comparisons. The distortion caused by 3-dimensional elements can be particularly severe when the graphic is slanted at an angle or when the viewer tends to compare ends up unwittingly comparing the areas of the ink rather than the heights of the bars.

It is much easier to make comparisons with a chart like the one below.

Source: Huang, C, Guo C, Nichols C, Chen S, Martorell R. Elevated levels of protein in urine in adulthood after exposure to

the Chinese famine of 1959–61 during gestation and the early postnatal period. Int. J. Epidemiol. (2014) 43 (6): 1806-1814 .

Omit "Chart Junk"

Consider these two examples.

Here is a simple enumeration of the number of pets in a neighborhood. There is absolutely no reason to connect these counts with lines. This is, in fact, confusing and inappropriate and nothing more than "chart junk."

Source: http://www.go-education.com/free-graph-maker.html

Moiré Vibration

Moiré effects are sometimes used in modern art to produce the appearance of vibration and movement. However, when these effects are applied to statistical presentations, they are distracting and add clutter because the visual noise interferes with the interpretation of the data.

Tufte presents the example shown below from Instituto de Expansao Commercial, Brasil, Graphicos Estatisticas (Rio de Janeiro, 1929, p. 15).

While the intention is to present quantitative information about the textile industry, the moiré effects do not add anything, and they are distracting, if not visually annoying.

Present Data to Facilitate Comparisons

Here is an attempt to compare catches of cod fish and crab across regions and to relate the variation to changes in water temperature. The problem here is that the Y-axes are vastly different, making it hard to sort out what's really going on. Even the Y-axes for temperature are vastly different.

http://seananderson.ca/courses/11-multipanel/multipanel.pdf1

The ability to make comparisons is greatly facilitated by using the same scales for axes, as illustrated below.

Data source: Dawber TR, Meadors GF, Moore FE Jr. Epidemiological approaches to heart disease:

the Framingham Study. Am J Public Health Nations Health. 1951;41(3):279-81. PMID: 14819398

It is also important to avoid distorting the X-axis. Note in the example below that the space between 0.05 to 0.1 is the same as space between 0.1 and 0.2.

Source: Park JH, Gail MH, Weinberg CR, et al. Distribution of allele frequencies and effect sizes and

their interrelationships for common genetic susceptibility variants. Proc Natl Acad Sci U S A. 2011; 108:18026-31.

Consider the range of the Y-axis. In the examples below there is no relevant information below $40,000, so it is not necessary to begin the Y-axis at 0. The graph on the right makes more sense.

Also, consider using a log scale. this can be particularly useful when presenting ratios as in the example below.

Source: Broman KW, Murray JC, Sheffield VC, White RL, Weber JL (1998) Comprehensive human genetic maps:

Individual and sex-specific variation in recombination. American Journal of Human Genetics 63:861-869, Figure 1

We noted earlier that pie charts make it difficult to see differences within a single pie chart, but this is particularly difficult when data is presented with multiple pie charts, as in the example below.

Source: Bell ML, et al. (2007) Spatial and temporal variation in PM2.5 chemical composition in the United States

for health effects studies. Environmental Health Perspectives 115:989-995, Figure 3

When multiple comparisons are being made, it is essential to use colors and symbols in a consistent way, as in this example.

Source: Manning AK, LaValley M, Liu CT, et al. Meta-Analysis of Gene-Environment Interaction:

Joint Estimation of SNP and SNP x Environment Regression Coefficients. Genet Epidemiol 2011, 35(1):11-8.

Avoid putting too many lines on the same chart. In the example below, the only thing that is readily apparent is that 1980 was a very hot summer.

Data from National Weather Service Weather Forecast Office at

http://www.srh.noaa.gov/tsa/?n=climo_tulyeartemp

Make Efficient Use of Space

Reduce the ratio of ink to information.

This isn't efficient, because this graphic is totally uninformative.

Source: Mykland P, Tierney L, Yu B (1995) Regeneration in Markov chain samplers. Journal of the American Statistical Association 90:233-241, Figure 1

Bar graphs add ink without conveying any additional information, and they are distracting. The graph below on the left inappropriately uses bars which clutter the graph without adding anything. The graph on the right displays the same data, by does so more clearly and with less clutter.

Multiple Types of Information on the Same Figure

Choosing the best graph type, bar charts, error bars and dot plots.

As noted previously, bar charts can be problematic. Here is another one presenting means and error bars, but the error bars are misleading because they only extend in one direction. A better alternative would have been to to use full error bars with a scatter plot, as illustrated previously (right).

Consider the four graphs below presenting the incidence of cancer by type. The upper left graph unnecessary uses bars, which take up a lot of ink. This layout also ends up making the fonts for the types of cancer too small. Small font is also a problem for the dot plot at the upper right, and this one also has unnecessary grid lines across the entire width.

The graph at the lower left has more readable labels and uses a simple dot plot, but the rank order is difficult to figure out.

The graph at the lower right is clearly the best, since the labels are readable, the magnitude of incidence is shown clearly by the dot plots, and the cancers are sorted by frequency.

Single Continuous Numeric Variable

In this situation a cumulative distribution function conveys the most information and requires no grouping of the variable. A box plot will show selected quantiles effectively, and box plots are especially useful when stratifying by multiple categories of another variable.

Histograms are also possible. Consider the examples below.

Two Variables

The two graphs below summarize BMI (Body Mass Index) measurements in four categories, i.e., younger and older men and women. The graph on the left shows the means and 95% confidence interval for the mean in each of the four groups. This is easy to interpret, but the viewer cannot see that the data is actually quite skewed. The graph on the right shows the same information presented as a box plot. With this presentation method one gets a better understanding of the skewed distribution and how the groups compare.

The next example is a scatter plot with a superimposed smoothed line of prediction. The shaded region embracing the blue line is a representation of the 95% confidence limits for the estimated prediction. This was created using "ggplot" in the R programming language.

Source: Frank E. Harrell Jr. on graphics: http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatGraphCourse/graphscourse.pdf (page 121)

Multivariate Data

The example below shows the use of multiple panels.

Source: Cleveland S. The Elements of Graphing Data. Hobart Press, Summit, NJ, 1994.

Displaying Uncertainty

Error bars showing confidence limits
Confidence bands drawn using two lines
Shaded confidence bands
Bayesian credible intervals
Bayesian posterior densities

Confidence Limits

Shaded Confidence Bands

Source: Frank E. Harrell Jr. on graphics: http://biostat.mc.vanderbilt.edu/twiki/pub/Main/StatGraphCourse/graphscourse.pdf

Source: Tweedie RL and Mengersen KL. (1992) Br. J. Cancer 66: 700-705

Forest Plot

This is a Forest plot summarizing 26 studies of cigarette smoke exposure on risk of lung cancer. The sizes of the black boxes indicating the estimated odds ratio are proportional to the sample size in each study.

Data from Tweedie RL and Mengersen KL. (1992) Br. J. Cancer 66: 700-705

Summary Recommendations

In general, avoid bar plots
Avoid chart junk and the use of too much ink relative to the information you are displaying. Keep it simple and clear.
Avoid pie charts, because humans have difficulty perceiving relative angles.
Pay attention to scale, and make scales consistent.
Explore several ways to display the data!

12 Tips on How to Display Data Badly

Adapted from Wainer H. How to Display Data Badly. The American Statistician 1984; 38: 137-147.

Show as few data as possible
Hide what data you do show; minimize the data-ink ratio
Ignore the visual metaphor altogether
Only order matters
Graph data out of context
Change scales in mid-axis
Emphasize the trivial; ignore the important
Jiggle the baseline
Alphabetize everything.
Make your labels illegible, incomplete, incorrect, and ambiguous.
More is murkier: use a lot of decimal places and make your graphs three dimensional whenever possible.
If it has been done well in the past, think of another way to do it

Additional Resources

Stephen Few: Designing Effective Tables and Graphs. http://www.perceptualedge.com/images/Effective_Chart_Design.pdf
Gary Klaas: Presenting Data: Tabular and graphic display of social indicators. Illinois State University, 2002. http://lilt.ilstu.edu/gmklass/pos138/datadisplay/sections/goodcharts.htm (Note: The web site will be discontinued to be replaced by the Just Plain Data Analysis site).

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Tabular Presentation of Data

What is Tabular Presentation of Data in Detail

The presentation of data is essential. A tabular presentation of data helps the viewer to understand and to interpret the information better. Take, for example, your annual report card that is presented in a tabular format. You have your subjects written in one column of the table and your grades on the other. The third column mentions any teachers’ remarks. A single glance at your report card lets you read through the grades and subjects as well as the remarks with ease.

Now think, what would have happened if the same information was presented to you in the form of a paragraph. You would have to go through each line to know the grade that you got and the teachers’ remarks on a particular subject. This would make it tedious and also confusing to understand the report card.

Presentation of Data

Data must be presented properly. If the information is pleasing to the eyes, then it immediately gets attention. Data presentation is about using the same information to exhibit it in an attractive and useful way that can be read and interpreted easily. Data presentation is of three broad kinds. These are:

Textual presentation.

Data tables.

Diagrammatic presentation.

On this presentation of data Class 11 page, you will get to understand the textual and tabular data presentation or the data tables.

Textual Presentation

Data is first obtained in a textual format. It is a vague and raw format of the data. The data is mentioned in the text form, which is usually written in a paragraph. The textual presentation of data is used when the data is not large and can be easily comprehended by the reader just when he reads the paragraph.

This data format is useful when some qualitative statement is to be supplemented with data. The reader does not want to read volumes of data to be represented in the tabular format. Does he want to understand the data in a diagrammatic form? All that the reader wants to know is the data that provides evidence to the statement written. This is enough to let the reader gauge the intensity of the statement.

The textual data is evidence of the qualitative statement, and one needs to go through the complete text before he concludes anything.

For example, the coronavirus death toll in India today is 447. The reader does not need a lot of data here. The entire text of the state-wise breakup is accumulated to arrive at the national death figure. This is enough information for the reader.

Data Tables or Tabular Presentation

Data Tables or Tabular presentation of data is known to be the arrangement of certain values recorded in tables such that they are easy to manage and read. It is mostly done for a reader to gain the idea about the data without making it too complicated. The data presentation can be used for proper matter which is informative and creative at the same time.

What is Data Presentation?

If the reader has to interpret a lot of data, then this has to be organized in an easy to read format. The data should be laid out in rows and columns so that the reader can get what he wants at a single glance. Data tables are easy to construct and also easy to read, which makes them popular.

Components of Data Tables

Below are the key components of the data table.

Table Number - Each table has a table number that makes it easy to locate it. This number serves as a reference and leads one to a particular table.

Title - The table should also have a title that lets the reader understand what information the table provides. The place of study, the period, and the nature of data classification are also mentioned in the title.

Headnotes - The headnotes give further information. It provides the unit of data in brackets which is mentioned at the end of the title. The headnote aids the title to offer more information that the reader would need to interpret the data.

Stubs - These are the titles that tell you what the row represents. In other words, the stubs give information about what data is contained in each row.

Caption - The caption is the column title in the data table. It gives information about what is contained in each column.

Body or Field - The body or the field is the entire content in the table. Each item that is present in the body is the cell.

Footnotes - Footnotes are not commonly used, but these are used to supplement the table title if needed.

Source - If the data used in the table is taken from a secondary source, then that has to be mentioned in the footnote.

Construction of Data Tables

Tabular presentation can be constructed in many ways. Here are some ways that are commonly followed.

The title of the table should be able to reflect on the table content.

If two rows or columns have to be compared, then these should be placed adjacent to each other.

If the rows in the table are lengthy, then the stub can be placed on the right-hand part of the table.

Headings should always be in the singular.

Footnotes are not compulsory and should be provided only if required.

The column size should be symmetrical and uniform.

There should be no abbreviations in the headings and the subheadings.

The units should be specified above the column.

The Advantages of Tabular Presentation

Makes representation of data easy.

Makes it easy to analyze the data.

Makes it easy to compare data.

The data is represented in a readable manner which saves space and the reader’s time.

Classification of Data and Tabular Presentation

Classification of data and Tabular presentation is needed to arrange complex, heterogeneous data into a more simple and sophisticated manner. This is done for the convenience of the audience studying the data so the values are easy to distinguish. There are four ways in which one can classify the data and Tabular presentation. These are as follows.

Qualitative Classification

In qualitative classification, the data is classified based on its qualitative attributes. This is when the data has attributes that cannot be quantified. These could be boys-girls, rural-urban, etc.

Quantitative Classification

In quantitative classification, the data is classified based on the quantitative attributes. These could be marks where the data is categorized into 0-50, 51-100, etc.

Temporal Classification

In this tabular presentation, the data is classified according to the time. Here the data is represented in varied time frames like in the year 2016, 2018, etc.

Spatial Classification

In this method of classification, the data is classified according to location, like India, Pakistan, Russia, etc.

FAQs on Tabular Presentation of Data

1. What do you Mean by the Tabular Presentation of Data?

When data is presented in a tabular form, it makes the information easy to read and to engage. The data is arranged in rows and columns. The tabular method of presenting data is the most widely used. The tabular representation of data coordinates the information for decision making, and any presentation of data in statistics use. Data in the tabular format is divided into 4 kinds. These are the Qualitative (based on traits), Quantitative (based on quantitative features), Temporal (based on time), and spatial (based on location) presentation of data.

2. Explain the Difference Between the Tabular and Textual Presentation of Data ?

In the tabular representation of data, the data is presented in the form of tables and diagrams. The textual presentation uses words to present the data.Tabular data is self-explanatory as there are segments that depict what the data wants to convey. The textual data need to be explained with words.The key difference thus is that the textual representation of data is subjective. In a tabular format, the data is mentioned in the form of tables. This makes tabular data perfect for the vast amount of data which makes it easy for the reader to read and interpret the information.

3. Where can I get the most appropriate Textual and Tabular Presentation of Data - Advantages, Classification and FAQs?

At Vedantu, the students can find different types of study material which help them ace their exams. Whether it is sample tests, mock tests, important questions, notes you want, Vedantu has it all. All of these are curated by our master teachers who make sure that you score the highest of marks. For finding the Textual and Tabular Presentation of data - Advantages, Classification and FAQs, all students have to do is sign in Vedantu.com using the Vedantu app or website.

4. What is meant by textual and Tabular Presentation?

Data around us is represented in different ways to us on an everyday basis. Two of these methods are either presenting it via texts which are known as textual presentation and the other one is known as Tabular Presentation by which the data is presented using tables. The tabular presentation is attractive and helps one to visualize the given data, although some may consider textual presentation for a detailed and proper explanation. It depends entirely on the individual how they want their data to be produced, however, most people consider the tabular presentation.

5. Why should I know about textual and Tabular Presentation?

We need data to share information with others, for this, it is important for the students to know how to use the different ways of data presentation. Knowing about Textual and Tabular presentation of data helps an individual to choose how they need their information to be conveyed. Textual data representation is basic and it is important that a student already knows about it completely when they move on to studying the tabular presentation of data. This makes sure that you have your concepts clear and for your progress to attain great heights.

Refer to Vedantu for free solutions chapter wise and get free access to other online resources to improve your learning in several folds.

TEXTUAL PRESENTATION OF DATA

This method comprises presenting data with the help of a paragraph or a number of paragraphs. The official report of an inquiry commission is usually made by textual presentation.

In 1999, out of a total of five thousand workers of a factory, four thousand and two hundred were members of a Trade Union. The number of female workers was twenty per cent of the total workers out of which thirty per cent were members of the Trade Union.

In 2000, the number of workers belonging to the trade union was increased by twenty per cent as compared to 1999 of which four thousand and two hundred were male.

The number of workers not belonging to trade union was nine hundred and fifty of which four hundred and fifty were females. The merit of this mode of presentation lies in its simplicity and even a layman can present data by this method.

The observations with exact magnitude can be presented with the help of textual presentation. Furthermore, this type of presentation can be taken as the first step towards the other methods of presentation.

Textual presentation, however, is not preferred by a statistician simply because, it is dull, monotonous and comparison between different observations is not possible in this method.

For manifold classification, this method cannot be recommended and tabulation is usually preferred.

Now, let us see, how the same data can be represented using tabular representation.

Status of the workers of the factory on the basis of their trade union membership for 1999 and 2000.

Here, we have to write the source through which we got the above data.

TU, M, F and T stand for trade union, male, female and total respectively.

The tabulation method is usually preferred to textual- presentation as

(i) It facilitates comparison between rows and columns.

(ii) Complicated data can also be represented using tabulation.

(iii) It is a must for diagrammatic representation.

(iv) Without tabulation, statistical analysis of data is not possible.

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Activity Sheet-Organization and Presentation of Data in Textual, Tabular and Graphical Form

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This activity sheet about the Organization and Presentation of Data in Textual, Tabular and Graphical Form for Basic Statistics Class.

Data Presentation: Use of Tables and Graphics

First Online: 08 June 2022

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Anand J. Thakur 2

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In an era of evidence based medicine, data is an important factor and needs to be presented in a way that is quickly understood by the audience. People are generally weak at understanding data. Visualization makes it easier for the audience to grasp and recall data. It should be presented in an attractive style to create interest in audience’s mind; Data may be presented as a table or graphic. There are 3 types of tables: Formal (numbers), textural, and matrix; each one has its peculiarities and usefulness. Rules for construction of these table are described. There are several graphics to present data and one that is most suitable for the variables under consideration is chosen. Numerical (quantitative) data is presented in a bar chart, pictogram, pie chart, or choropleth map. Descriptive, categorical, and frequency data (qualitative) are presented in histogram, frequency polygon, frequency curve, line chart, and scatter diagram. Decision trees and genealogical charts are used in special circumstances. Example of each of these graphics is discussed with its usefulness and shortcomings. Recommendations on use of tables and graphics for a specific data are made for expediency and usability.

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Thakur, A.J. (2022). Data Presentation: Use of Tables and Graphics. In: Tapping the Power of PowerPoint for Medical Posters and Presentations. Springer, Singapore. https://doi.org/10.1007/978-981-19-1816-2_8

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Review Article
Published: 16 June 2023

Antigen presentation in cancer — mechanisms and clinical implications for immunotherapy

Kailin Yang 1 ,
Ahmed Halima 1 &
Timothy A. Chan ORCID: orcid.org/0000-0002-9265-0283 1 , 2 , 3 , 4

Nature Reviews Clinical Oncology volume 20 , pages 604–623 ( 2023 ) Cite this article

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Immunotherapy
Predictive markers
Tumour immunology

Over the past decade, the emergence of effective immunotherapies has revolutionized the clinical management of many types of cancers. However, long-term durable tumour control is only achieved in a fraction of patients who receive these therapies. Understanding the mechanisms underlying clinical response and resistance to treatment is therefore essential to expanding the level of clinical benefit obtained from immunotherapies. In this Review, we describe the molecular mechanisms of antigen processing and presentation in tumours and their clinical consequences. We examine how various aspects of the antigen-presentation machinery (APM) shape tumour immunity. In particular, we discuss genomic variants in HLA alleles and other APM components, highlighting their influence on the immunopeptidomes of both malignant cells and immune cells. Understanding the APM, how it is regulated and how it changes in tumour cells is crucial for determining which patients will respond to immunotherapy and why some patients develop resistance. We focus on recently discovered molecular and genomic alterations that drive the clinical outcomes of patients receiving immune-checkpoint inhibitors. An improved understanding of how these variables mediate tumour–immune interactions is expected to guide the more precise administration of immunotherapies and reveal potentially promising directions for the development of new immunotherapeutic approaches.

The clinical success of immune-checkpoint inhibitors has improved cancer care, although long-term durable remission is only achieved in a subset of patients.

Antigen processing and presentation by tumour cells are essential for long-lasting immune surveillance.

Alterations in the genes encoding MHC components and other parts of the antigen-presentation machinery are frequently found across several cancer types and are associated with both tumour development and the effectiveness of immunotherapies.

MHC-based antigen presentation exerts strong evolutionary pressure on the immunopeptidome, which in turn shapes the mutational landscape of the tumour genome.

Germline human leukocyte antigen diversity and somatic aberrations in the antigen-presentation machinery inform the therapeutic response to immune-checkpoint inhibitors.

Development of novel therapies based on an accurate understanding of antigen presentation in the setting of tumour–immune dynamics is crucial to the development of improved therapeutic approaches.

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Antigen presentation in cancer: insights into tumour immunogenicity and immune evasion

Towards new horizons: characterization, classification and implications of the tumour antigenic repertoire

Therapeutic cancer vaccines: advancements, challenges, and prospects

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Acknowledgements

The authors are grateful for the support from NIH grants R35CA232097 (T.A.C.), R01CA205426 (T.A.C.), U54CA274513 (T.A.C.) and T32CA094186 (K.Y.), a Young Investigator Award from ASCO Conquer Cancer Foundation (K.Y.), a RSNA Research Resident Grant (K.Y.), and a Cleveland Clinic VeloSano Impact Award (K.Y.).

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Kailin Yang, Ahmed Halima & Timothy A. Chan

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All authors made a substantial contribution to all aspects of the preparation of this manuscript.

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Competing interests.

T.A.C. is a co-founder of and holds equity in Gritstone Oncology; holds equity in An2H; acknowledges grant funding from An2H, AstraZeneca, Bristol Myers Squibb, Eisai, Illumina and Pfizer; has served as an advisor for An2H, AstraZeneca, Bristol Myers Squibb, Eisai, Illumina and MedImmune; and holds ownership of intellectual property on using tumour mutational burden to predict immunotherapy response, which has been licensed to PGDx. K.Y. and A.H. declare no competing interests.

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Yang, K., Halima, A. & Chan, T.A. Antigen presentation in cancer — mechanisms and clinical implications for immunotherapy. Nat Rev Clin Oncol 20 , 604–623 (2023). https://doi.org/10.1038/s41571-023-00789-4

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Computer Science > Computer Vision and Pattern Recognition

Title: beyond human vision: the role of large vision language models in microscope image analysis.

Abstract: Vision language models (VLMs) have recently emerged and gained the spotlight for their ability to comprehend the dual modality of image and textual data. VLMs such as LLaVA, ChatGPT-4, and Gemini have recently shown impressive performance on tasks such as natural image captioning, visual question answering (VQA), and spatial reasoning. Additionally, a universal segmentation model by Meta AI, Segment Anything Model (SAM) shows unprecedented performance at isolating objects from unforeseen images. Since medical experts, biologists, and materials scientists routinely examine microscopy or medical images in conjunction with textual information in the form of captions, literature, or reports, and draw conclusions of great importance and merit, it is indubitably essential to test the performance of VLMs and foundation models such as SAM, on these images. In this study, we charge ChatGPT, LLaVA, Gemini, and SAM with classification, segmentation, counting, and VQA tasks on a variety of microscopy images. We observe that ChatGPT and Gemini are impressively able to comprehend the visual features in microscopy images, while SAM is quite capable at isolating artefacts in a general sense. However, the performance is not close to that of a domain expert - the models are readily encumbered by the introduction of impurities, defects, artefact overlaps and diversity present in the images.

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Gross Domestic Product, First Quarter 2024 (Advance Estimate)

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Additional Information

Real gross domestic product (GDP) increased at an annual rate of 1.6 percent in the first quarter of 2024 (table 1), according to the "advance" estimate released by the Bureau of Economic Analysis. In the fourth quarter of 2023, real GDP increased 3.4 percent.

The GDP estimate released today is based on source data that are incomplete or subject to further revision by the source agency (refer to “Source Data for the Advance Estimate” on page 3). The “second” estimate for the first quarter, based on more complete source data, will be released on May 30, 2024.

Real GDP: Percent change from preceding quarter

The increase in real GDP primarily reflected increases in consumer spending, residential fixed investment, nonresidential fixed investment, and state and local government spending that were partly offset by a decrease in private inventory investment. Imports, which are a subtraction in the calculation of GDP, increased (table 2).

The increase in consumer spending reflected an increase in services that was partly offset by a decrease in goods. Within services, the increase primarily reflected increases in health care as well as financial services and insurance. Within goods, the decrease primarily reflected decreases in motor vehicles and parts as well as gasoline and other energy goods. Within residential fixed investment, the increase was led by brokers’ commissions and other ownership transfer costs as well as new single-family housing construction. The increase in nonresidential fixed investment mainly reflected an increase in intellectual property products. The increase in state and local government spending reflected an increase in compensation of state and local government employees. The decrease in inventory investment primarily reflected decreases in wholesale trade and manufacturing. Within imports, the increase reflected increases in both goods and services.

Compared to the fourth quarter, the deceleration in real GDP in the first quarter primarily reflected decelerations in consumer spending, exports, and state and local government spending and a downturn in federal government spending. These movements were partly offset by an acceleration in residential fixed investment. Imports accelerated.

Current‑dollar GDP increased 4.8 percent at an annual rate, or $327.5 billion, in the first quarter to a level of $28.28 trillion. In the fourth quarter, GDP increased 5.1 percent, or $346.9 billion (tables 1 and 3).

The price index for gross domestic purchases increased 3.1 percent in the first quarter, compared with an increase of 1.9 percent in the fourth quarter (table 4). The personal consumption expenditures (PCE) price index increased 3.4 percent, compared with an increase of 1.8 percent. Excluding food and energy prices, the PCE price index increased 3.7 percent, compared with an increase of 2.0 percent.

Personal Income

Current-dollar personal income increased $407.1 billion in the first quarter, compared with an increase of $230.2 billion in the fourth quarter. The increase primarily reflected increases in compensation and personal current transfer receipts (table 8).

Disposable personal income increased $226.2 billion, or 4.5 percent, in the first quarter, compared with an increase of $190.4 billion, or 3.8 percent, in the fourth quarter. Increases in compensation and personal current transfer receipts were partly offset by an increase in personal current taxes, which are a subtraction in the calculation of DPI. Real disposable personal income increased 1.1 percent, compared with an increase of 2.0 percent.

Personal saving was $755.7 billion in the first quarter, compared with $815.5 billion in the fourth quarter. The personal saving rate —personal saving as a percentage of disposable personal income—was 3.6 percent in the first quarter, compared with 4.0 percent in the fourth quarter.

Source Data for the Advance Estimate

The GDP estimate released today is based on source data that are incomplete or subject to further revision by the source agency. Information on the source data and key assumptions used in the advance estimate is provided in a Technical Note and a detailed " Key Source Data and Assumptions " file posted with the release. The second estimate for the first quarter, based on more complete data, will be released on May 30, 2024. For information on updates to GDP, refer to the "Additional Information" section that follows.

* * *

Next release, May 30, 2024, at 8:30 a.m. EDT Gross Domestic Product (Second Estimate) Corporate Profits (Preliminary Estimate) First Quarter 2024

Full Release & Tables (PDF)

Technical note (pdf), tables only (excel), release highlights (pdf), historical comparisons (pdf), key source data and assumptions (excel), revision information.

GDP Lisa Mataloni 301-278-9083 [email protected]
Corporate Profits Kate Pinard 301-278-9417 [email protected]
Media (BEA) Connie O’Connell 301-278-9003 [email protected]

Additional resources available at www.bea.gov :

Stay informed about BEA developments by reading the BEA blog , signing up for BEA's email subscription service , or following BEA on X, formerly known as Twitter @BEA_News .
Historical time series for these estimates can be accessed in BEA's interactive data application .
Access BEA data by registering for BEA's data Application Programming Interface (API).
For more on BEA's statistics, refer to our online journal, the Survey of Current Business .
BEA's news release schedule
NIPA Handbook : Concepts and Methods of the U.S. National Income and Product Accounts

Definitions

Gross domestic product (GDP), or value added , is the value of the goods and services produced by the nation's economy less the value of the goods and services used up in production. GDP is also equal to the sum of personal consumption expenditures, gross private domestic investment, net exports of goods and services, and government consumption expenditures and gross investment.

Gross domestic income (GDI) is the sum of incomes earned and costs incurred in the production of GDP. In national economic accounting, GDP and GDI are conceptually equal. In practice, GDP and GDI differ because they are constructed using largely independent source data.

Gross output is the value of the goods and services produced by the nation's economy. It is principally measured using industry sales or receipts, including sales to final users (GDP) and sales to other industries (intermediate inputs).

Current-dollar estimates are valued in the prices of the period when the transactions occurred—that is, at "market value." Also referred to as "nominal estimates" or as "current-price estimates."

Real values are inflation-adjusted estimates—that is, estimates that exclude the effects of price changes.

The gross domestic purchases price index measures the prices of final goods and services purchased by U.S. residents.

The personal consumption expenditure price index measures the prices paid for the goods and services purchased by, or on the behalf of, "persons."

Personal income is the income received by, or on behalf of, all persons from all sources: from participation as laborers in production, from owning a home or business, from the ownership of financial assets, and from government and business in the form of transfers. It includes income from domestic sources as well as the rest of world. It does not include realized or unrealized capital gains or losses.

Disposable personal income is the income available to persons for spending or saving. It is equal to personal income less personal current taxes.

Personal outlays is the sum of personal consumption expenditures, personal interest payments, and personal current transfer payments.

Personal saving is personal income less personal outlays and personal current taxes.

The personal saving rate is personal saving as a percentage of disposable personal income.

Profits from current production , referred to as corporate profits with inventory valuation adjustment (IVA) and capital consumption (CCAdj) adjustment in the National Income and Product Accounts (NIPAs), is a measure of the net income of corporations before deducting income taxes that is consistent with the value of goods and services measured in GDP. The IVA and CCAdj are adjustments that convert inventory withdrawals and depreciation of fixed assets reported on a tax-return, historical-cost basis to the current-cost economic measures used in the national income and product accounts. Profits for domestic industries reflect profits for all corporations located within the geographic borders of the United States. The rest-of-the-world (ROW) component of profits is measured as the difference between profits received from ROW and profits paid to ROW.

For more definitions, refer to the Glossary: National Income and Product Accounts .

Statistical conventions

Annual-vs-quarterly rates . Quarterly seasonally adjusted values are expressed at annual rates, unless otherwise specified. This convention is used for BEA's featured, seasonally adjusted measures to facilitate comparisons with related and historical data. For details, refer to the FAQ " Why does BEA publish estimates at annual rates? "

Quarterly not seasonally adjusted values are expressed only at quarterly rates.

Percent changes . Percent changes in quarterly seasonally adjusted series are displayed at annual rates, unless otherwise specified. For details, refer to the FAQ " How is average annual growth calculated? " and " Why does BEA publish percent changes in quarterly series at annual rates? " Percent changes in quarterly not seasonally adjusted values are calculated from the same quarter one year ago. All published percent changes are calculated from unrounded data.

Calendar years and quarters . Unless noted otherwise, annual and quarterly data are presented on a calendar basis.

Quantities and prices . Quantities, or "real" volume measures, and prices are expressed as index numbers with a specified reference year equal to 100 (currently 2017). Quantity and price indexes are calculated using a Fisher-chained weighted formula that incorporates weights from two adjacent periods (quarters for quarterly data and annuals for annual data). For details on the calculation of quantity and price indexes, refer to Chapter 4: Estimating Methods in the NIPA Handbook .

Chained-dollar values are calculated by multiplying the quantity index by the current dollar value in the reference year (2017) and then dividing by 100. Percent changes calculated from real quantity indexes and chained-dollar levels are conceptually the same; any differences are due to rounding. Chained-dollar values are not additive because the relative weights for a given period differ from those of the reference year. In tables that display chained-dollar values, a "residual" line shows the difference between the sum of detailed chained-dollar series and its corresponding aggregate.

Updates to GDP

BEA releases three vintages of the current quarterly estimate for GDP. "Advance" estimates are released near the end of the first month following the end of the quarter and are based on source data that are incomplete or subject to further revision by the source agency. "Second" and "third" estimates are released near the end of the second and third months, respectively, and are based on more detailed and more comprehensive data as they become available.

The table below shows the average revisions to the quarterly percent changes in real GDP between different estimate vintages, without regard to sign.

Annual and comprehensive updates are released in late September. Annual updates generally cover at least the five most recent calendar years (and their associated quarters) and incorporate newly available major annual source data as well as some changes in methods and definitions to improve the accounts. Comprehensive (or benchmark) updates are carried out at about 5-year intervals and incorporate major periodic source data, as well as major conceptual improvements.

Unlike GDP, advance current quarterly estimates of GDI and corporate profits are not released because data on domestic profits and net interest of domestic industries are not available. For fourth quarter estimates, these data are not available until the third estimate.

GDP by industry and gross output estimates are released with the third estimate of GDP.

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Analysis and Interpretation of Assessment Result
Inserting Textual Evidence
Presentation of Data

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Textual Presentation of Data: Meaning, Suitability, and Drawbacks
Textual Presentation of Data has the following benefits: 1. It allows the researcher to make an elaborate interpretation of data during the presentation. 2. A researcher can easily present qualitative data that cannot be presented in tabular or graphical form using the textual presentation of data. 3. If the data is present in small sets, a ...
Textual And Tabular Presentation Of Data
Here we will be studying only the textual and tabular presentation, i.e. data tables in some detail. Textual Presentation. The discussion about the presentation of data starts off with it's most raw and vague form which is the textual presentation. In such form of presentation, data is simply mentioned as mere text, that is generally in a ...
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Data can be presented in three ways: 1. Textual Mode of presentation is layman's method of presentation of data. Anyone can prepare, anyone can understand. No specific skill (s) is/are required. 2. Tabular Mode of presentation is the most accurate mode of presentation of data. It requires a lot of skill to prepare, and some skill (s) to ...
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As soon as the data collection is over, the investigator needs to find a way of presenting the data in a meaningful, efficient and easily understood way to identify the main features of the data at a glance using a suitable presentation method. Generally, the data in the statistics can be presented in three different forms, such as textual ...
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Real gross domestic product (GDP) increased at an annual rate of 1.6 percent in the first quarter of 2024 (table 1), according to the "advance" estimate released by the Bureau of Economic Analysis. In the fourth quarter of 2023, real GDP increased 3.4 percent. The GDP estimate released today is based on source data that are incomplete or subject to further revision by the source agency (refer ...
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Chapter 1: Concept of Economics and Significance of Statistics in Economics

Chapter 2: Collection of Data

Chapter 3: Organisation of Data

Chapter 4: Presentation of Data: Textual and Tabular

Chapter 5: Diagrammatic Presentation of Data

Chapter 6: Measures of Central Tendency: Arithmetic Mean

Chapter 7: Measures of Central Tendency: Median and Mode

Chapter 8: Measures of Dispersion

Chapter 9: Correlation

Chapter 10: Index Number

Important Formulas in Statistics for Economics

Textual Presentation

Suitability

Advantages of Textual Presentation of Data

Disadvantages of Textual Presentation of Data

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Understanding Data Presentations (Guide + Examples)

What is a Data Presentation?

Real-Life Application of Bar Charts

Step 1: Selecting Data

Step 2: Choosing Orientation

Step 3: Colorful Insights

Real-life Application of Line Graphs

Step 3: Connecting Trends

Step 4: Adding Clarity with Color

Real-Life Application of a Dashboard

Step 1: Defining Key Metrics

Step 2: Choosing Visualization Widgets

Step 3: Dashboard Layout

Real-Life Application of a Treemap Chart

Step 1: Define Your Data Hierarchy

Step 2: Choose a Suitable Tool

Step 3: Make a Treemap Chart with PowerPoint

Step 5: Input Your Data

Step 6: Customize the Treemap

Real-Life Application of Pie Charts

Step 1: Define Your Data Structure

Step 2: Insert a Pie Chart

Step 03: Results Interpretation

Real-Life Application of a Histogram

Step 1: Gather Data

Step 2: Define Bins

Step 3: Count Frequency

Step 4: Create the Histogram

Real-Life Application of Scatter Plot

1. Fact Sheet Dashboard for Data Presentation

2. 3D Column Chart Infographic PPT Template

3. Data Circles Infographic PowerPoint Template

4. Colorful Metrics Dashboard for Data Presentation

5. Animated Data Presentation Tools for PowerPoint & Google Slides

6. Statistics Waffle Charts PPT Template for Data Presentations

7. Data Presentation Dashboard Template for Google Slides

8. Weather Dashboard for Data Presentation

9. Social Media Marketing Dashboard Data Presentation Template

10. Project Management Summary Dashboard Template

11. Profit & Loss Dashboard for PowerPoint and Google Slides

Overwhelming visuals

Inappropriate chart types

Lack of context

Inconsistency in design

Failure to provide details

Lack of focus

Visual accessibility issues

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