Every graph tells a story
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This document discusses the importance of being able to critically interpret graphs and data visualizations. It notes that the ability to do so is a key indicator of college readiness, while an inability can lead to easy manipulation. The document cautions that graphs only represent interpretations of data, not the data itself, and provides examples of graphs depicting the same data in different ways to show different stories. It discusses why scientists use graphs to organize and emphasize patterns in data in a visual and easier to understand representation.
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STA-O Discussion Question Four - Statistics.pdf
Discussion Question Four – Statistics
For this week’s discussion read “Imagine a Pie Chart Stomping on an Infographic Forever” by Eronarn
(below).
• Discuss each of the visualizations/infographs.
• Talk about why each infograph (A-M) is ineffective, and what changes could be made to better
represent the data visually.
• Go through the information learned in this article and how you can apply it to your future in design.
Note: Images are linked for a clearer view, and certain portions have been omitted for brevity’s sake.
Your submission is due by Friday at 11:59 p.m EST.
Reference the discussion-grading rubric to understand the expectations for your posts. Use specific
examples, find other sources of information (cite any sources you use), and tell a story. Be as detailed as
possible. All discussion posts are expected to be at least 250 words long.
Imagine A Pie Chart Stomping On An Infographic Forever
By Eronarn May 10th, 2010
http://www.smashingmagazine.com/2010/05/10/imagine-a-pie-chart-stomping-on-an-infographic-forever/
A certain category of design gaffes can be boiled down to violations of audience expectations. Websites
that don’t work in Internet Explorer are a heck of a nasty surprise for users who, bless their souls, want the
same Internet experience as everyone else. Websites that prevent copying, whether through careless text-
as-image conversions or those wretched copyright pop-ups from the turn of the century, cripple a feature
that works nearly everywhere else on the Internet. Avoiding this category of blunders is crucial to good
design, which is why I am upset that one particular pitfall has been overlooked with extreme frequency.
According to statlit.org, statistical literacy is the ability to read and interpret summary statistics in the
everyday media: in graphs, tables, statements, surveys and studies. Statistical literacy is needed by data
consumers.
The importance of statistical literacy in the Internet age is clear, but the concept is not exclusive to
designers. I’d like to focus on it because designers must consider it in a way that most people do not have
to: statistical literacy is more than learning the laws of statistics; it is about representations that the human
mind can understand and remember (source: Psychological Science in the Public Interest).
(A) Can you notice what’s wrong with this infographics? You will find a detailed answer below, in the
showcase of bad infographics.
As a designer, you get to choose those representations. Most of the time this is a positive aspect. Visual
representations allow you to quickly summarize a data set or make connections that might be difficult to
perceive otherwise. Unfortunately, designers too often forget that data exists for more than entertainment
or aesthetics. If you design a visualization before correctly understanding the data on wh.
Alt Story Forms.AMA News
This document provides guidance on creating effective graphics to accompany written articles. It discusses the importance of understanding the data and context before presenting it visually. Key tips include having a clear purpose for the graphic, asking questions to ensure the appropriate comparisons or explanations are shown, vetting the data thoroughly, and collaborating with editors and designers. The document also lists seven important questions graphic creators should ask themselves to determine the best way to visually convey essential information to readers.
Design for Delight
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Data science and the art of persuasion
The presentation of data science to lay audiences—the last mile—hasn’t evolved as rapidly or as fully as the science’s technical part. It must catch up, and that means rethinking how data science teams are put together, how they’re managed, and who’s involved at every point in the process, from the first data stream to the final chart shown to the board. Until companies can successfully traverse that last mile, data science teams will under deliver. They will provide, in Willard Brinton’s words, foundations without cathedrals.
Chapter 4 Problem 31. For problem three in chapter four, a teac.docx
Chapter 4: Problem 3
1. For problem three in chapter four, a teacher wants to display her students number of responses for each day of the week. And she wants to do that with a bar chart. Since she hasn't taken a stats class, she comes to you for help. You first enter her data into SPSS and the results look like this-- When you look at your data set, you'll see that it actually has the wrong level of measurement. Notice that there's a little Venn diagram at the top of each column, which indicates that your data has been entered as nominal. That would be correct if you were noting which day of the week a student participated, but since you're noting how often a given student participated, the correct level of measurement is a scale. Go ahead and change that. Watch how I do that. Under variable view, under measure, you just want to click each one and turn it into a scale. You can also cut and paste these, and I can show you that in another video. Once you have them changed, go back to data view, and you'll see that at the top it has changed in two little rulers. The next question is, how do I get SPSS to display the average score per day rather the total number of individual scores, which might look like a mess, and it's why this question is a toughie. To do that we go under graphs, and you'll see that you have two options, you can do a Chart Builder or a Legacy Dialog. For this question we want to use the Legacy Dialog. We go to Bar and when we click that, there are two questions-- one, what type of bar chart? We want a simple one. And then, how do you want the data in their area displayed? Do we want to summarize for the groups? We really don't. We want summary of separate variables where each day of the week is a variable. We click on Define and then here you'll see every day of the week. You want to bring that over and you see your bar charts are going to represent the mean for every day of the week. As a good habit you want to make sure you title it, I called it "Students' Engagement During Group Discussion." The second one is by day of week. We hit Continue, and then when we hit OK, you're going to see your output pop up. And here is our bar chart-- every day of the week showing the average student engagement. And this is how you answer problem 3 in chapter 4. Good luck.
2. Identify whether these distributions are negatively skewed, positively skewed, or not skewed at all and explain why you describe them that way.
a. This talented group of athletes scored very high on the vertical jump task.
b. On this incredibly crummy test, everyone received the same score.
c. On the most difficult spelling test of the year, the third graders wept as the scores were delivered and then their parents complained.
3. Use the data available as Chapter 4 Data Set 3 on pie preference to create a pie chart ☺ using SPSS.
4. For each of the followin.
Data Visualization - A Brief Overview
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
Introduction to Data Visualization
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Diowane2003
The document discusses different types of graphs such as bar graphs, pie charts, pictographs, and line graphs. It defines what a graph is and provides guidelines for creating good graphs, including determining the target audience, main messages, and appropriate graph type. The purpose of using graphs is to visually represent relationships between variables in a way that facilitates understanding and convinces readers.
Questionnaire analysis using_spss
The document discusses key considerations for designing questionnaires, including:
1. The format of questions will affect the answers, so questions should be short (under 25 words), understandable, and avoid double negatives.
2. Choosing an appropriate question format is important so responses are understandable and analyzable. Questions types include single answers, multiple choices, scales, and grids.
3. Pilot testing the questionnaire is essential to check that the data can be analyzed as intended and to refine ambiguous, leading, or poorly structured questions. Feedback from pilot participants should be solicited.
4. Generally, questionnaires should be limited to around 20 likert-scale questions to maintain participant interest and engagement. A variety of
diseñando datos
This document provides an overview of the origins and early development of graphs and charts. It discusses how many common chart types, such as bar charts, line charts and pie charts, were first invented in the late 18th century by William Playfair to visualize large amounts of economic and social data. It also describes some other key contributors in the following decades who helped advance different chart types, such as Joseph Priestley inventing the timeline, Charles Dupin creating the first choropleth map, and John Snow using a map to trace the source of a cholera outbreak in London. Overall, the document traces how charts have evolved from their early inventions to become ubiquitous tools for visualizing data today.
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This document summarizes a lecture about effectively communicating data and research findings. It discusses 7 guidelines for research from a textbook by Anderson, including reviewing objectives, arranging information analyzably, and interpreting relationships for readers. It then covers using graphics effectively based on 5 additional guidelines from Anderson. The lecture concludes with discussing 4 ways to "cook" or prepare raw data so it is easily understandable: cutting unnecessary information, flavoring it appropriately for the audience, chopping data into relevant pieces, and making it easy to digest. Students are then assigned an activity comparing data sheets between original and new iPad models.
Focus on what you learned that made an impression, what may have s.docx
Focus on what you learned that made an impression, what may have surprised you, and what you found particularly beneficial and why. Specifically:
What did you find that was really useful, or that challenged your thinking?
What are you still mulling over?
Was there anything that you may take back to your classroom?
Is there anything you would like to have clarified?
ANSWER THE ABOVE QUESTIONS BASED ON THE DOCUMENTS BELOW
Introduction & Goals
This week, we will investigate the distribution of a variable and look at ways to best see the key features of a quantitative variable’s distribution. We will look at visualizations of data, including line plots, frequency tables, stemplots, and histograms. We will hone our ability to describe key features of a distribution from visualizations and use them to compare distributions. We will begin to think about ideas for the Comparative Study by brainstorming in our project groups.
Goals
:
Reinforce the idea that data will vary
Explain what the distribution of variable is
Identify five key features of a distribution: center, spread, shape, clusters & outliers
Identify and create appropriate displays for categorical and quantitative data in one variable, including bar graphs, line plots, frequency tables, and histograms
Analyze distributions using stemplots and histograms
Recognize advantages and limitations of histograms
Begin to explore technology for use in statistics
Begin work on Comparative Study Final Project
DOW #2: How Long Is A Minute?
In week 1, we gathered data for this week’s DoW, addressing the question:
“How long is a minute to an adult?”
This week we'll:
In investigations 1 & 2, you will analyze the data with dot plots, frequency tables, stemplots, and histograms.
In Exercise B2, you will post your initial analysis and interpretation to the discussion board by Wednesday, 10 PM EST and create at least three follow-up posts by Friday, 10 PM EST.
In Exercise D2 & E2, you will post your best histogram to the discussion board by Friday, 10 PM EST. Compare the histograms and choose the one you think best represents the distribution by Sunday, 10 PM EST
Investigation 1: Seeing the Distribution
As we emphasized in Week 1,
data varies
. This point may seem trivial, but it encapsulates one of the most fundamental concepts of statistics:
variability
. Statistical Analysis is really a study of the patterns we find within this variation in the data. The pattern(s) in the variation is called the
distribution
of the variable. Much of statistics focuses on ways to represent and describe the distribution of a variable.
Activities A & B in this investigation focus on representing and describing the distribution.
Activity C introduces Excel as a tool for looking at a distribution.
Inv 1, Activity A: Patterns in the Variation
As we emphasized in Week 1,
data varies
. This point may seem trivial, but it encapsulates one of the most fundamental concepts of statistics:
variability
. Statistical Analy.
Graphs
This document provides information about graphs, including their definition, purpose, types, and guidelines for creating effective graphs. A graph is a visual representation of the relationship between two variables plotted on x and y axes. Good graphs accurately portray data in a clear, simple manner to help readers understand relationships and trends. Common graph types include bar graphs, pie charts, pictographs, and line graphs, each suited for certain types of data. Guidelines for graphing include understanding the audience and message, and experimenting with different graph styles to pick the most appropriate one.
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STA-O Discussion Question Four - Statistics.pdf Discussi.docx
STA-O Discussion Question Four - Statistics.pdf
Discussion Question Four – Statistics
For this week’s discussion read “Imagine a Pie Chart Stomping on an Infographic Forever” by Eronarn
(below).
• Discuss each of the visualizations/infographs.
• Talk about why each infograph (A-M) is ineffective, and what changes could be made to better
represent the data visually.
• Go through the information learned in this article and how you can apply it to your future in design.
Note: Images are linked for a clearer view, and certain portions have been omitted for brevity’s sake.
Your submission is due by Friday at 11:59 p.m EST.
Reference the discussion-grading rubric to understand the expectations for your posts. Use specific
examples, find other sources of information (cite any sources you use), and tell a story. Be as detailed as
possible. All discussion posts are expected to be at least 250 words long.
Imagine A Pie Chart Stomping On An Infographic Forever
By Eronarn May 10th, 2010
http://www.smashingmagazine.com/2010/05/10/imagine-a-pie-chart-stomping-on-an-infographic-forever/
A certain category of design gaffes can be boiled down to violations of audience expectations. Websites
that don’t work in Internet Explorer are a heck of a nasty surprise for users who, bless their souls, want the
same Internet experience as everyone else. Websites that prevent copying, whether through careless text-
as-image conversions or those wretched copyright pop-ups from the turn of the century, cripple a feature
that works nearly everywhere else on the Internet. Avoiding this category of blunders is crucial to good
design, which is why I am upset that one particular pitfall has been overlooked with extreme frequency.
According to statlit.org, statistical literacy is the ability to read and interpret summary statistics in the
everyday media: in graphs, tables, statements, surveys and studies. Statistical literacy is needed by data
consumers.
The importance of statistical literacy in the Internet age is clear, but the concept is not exclusive to
designers. I’d like to focus on it because designers must consider it in a way that most people do not have
to: statistical literacy is more than learning the laws of statistics; it is about representations that the human
mind can understand and remember (source: Psychological Science in the Public Interest).
(A) Can you notice what’s wrong with this infographics? You will find a detailed answer below, in the
showcase of bad infographics.
As a designer, you get to choose those representations. Most of the time this is a positive aspect. Visual
representations allow you to quickly summarize a data set or make connections that might be difficult to
perceive otherwise. Unfortunately, designers too often forget that data exists for more than entertainment
or aesthetics. If you design a visualization before correctly understanding the data on wh.
Alt Story Forms.AMA News
This document provides guidance on creating effective graphics to accompany written articles. It discusses the importance of understanding the data and context before presenting it visually. Key tips include having a clear purpose for the graphic, asking questions to ensure the appropriate comparisons or explanations are shown, vetting the data thoroughly, and collaborating with editors and designers. The document also lists seven important questions graphic creators should ask themselves to determine the best way to visually convey essential information to readers.
Design for Delight
A quick reference on designing data visualizations that delight and leverage best practices from the design world to ensure your data is presented in meaningful, usable, fun ways.
[DSC Europe 22] The Challenges of Product Analytics - Tamas Srancsik
You may find many articles about analyzing and visualizing data the best way. Unfortunately, there aren't generic guidelines for the steps you need to take to get to the point you have reliable data and metrics representing the needs of your business and stakeholders. There are many traps in this journey. Good-looking but not really actionable metrics, unclear ownership, and unscalable and expensive tooling are just a few among your enemies. Tamas will help you build the proper infrastructure, and choose the best metrics to deliver the best experience to the users of your software.
Data science and the art of persuasion
The presentation of data science to lay audiences—the last mile—hasn’t evolved as rapidly or as fully as the science’s technical part. It must catch up, and that means rethinking how data science teams are put together, how they’re managed, and who’s involved at every point in the process, from the first data stream to the final chart shown to the board. Until companies can successfully traverse that last mile, data science teams will under deliver. They will provide, in Willard Brinton’s words, foundations without cathedrals.
Chapter 4 Problem 31. For problem three in chapter four, a teac.docx
Chapter 4: Problem 3
1. For problem three in chapter four, a teacher wants to display her students number of responses for each day of the week. And she wants to do that with a bar chart. Since she hasn't taken a stats class, she comes to you for help. You first enter her data into SPSS and the results look like this-- When you look at your data set, you'll see that it actually has the wrong level of measurement. Notice that there's a little Venn diagram at the top of each column, which indicates that your data has been entered as nominal. That would be correct if you were noting which day of the week a student participated, but since you're noting how often a given student participated, the correct level of measurement is a scale. Go ahead and change that. Watch how I do that. Under variable view, under measure, you just want to click each one and turn it into a scale. You can also cut and paste these, and I can show you that in another video. Once you have them changed, go back to data view, and you'll see that at the top it has changed in two little rulers. The next question is, how do I get SPSS to display the average score per day rather the total number of individual scores, which might look like a mess, and it's why this question is a toughie. To do that we go under graphs, and you'll see that you have two options, you can do a Chart Builder or a Legacy Dialog. For this question we want to use the Legacy Dialog. We go to Bar and when we click that, there are two questions-- one, what type of bar chart? We want a simple one. And then, how do you want the data in their area displayed? Do we want to summarize for the groups? We really don't. We want summary of separate variables where each day of the week is a variable. We click on Define and then here you'll see every day of the week. You want to bring that over and you see your bar charts are going to represent the mean for every day of the week. As a good habit you want to make sure you title it, I called it "Students' Engagement During Group Discussion." The second one is by day of week. We hit Continue, and then when we hit OK, you're going to see your output pop up. And here is our bar chart-- every day of the week showing the average student engagement. And this is how you answer problem 3 in chapter 4. Good luck.
2. Identify whether these distributions are negatively skewed, positively skewed, or not skewed at all and explain why you describe them that way.
a. This talented group of athletes scored very high on the vertical jump task.
b. On this incredibly crummy test, everyone received the same score.
c. On the most difficult spelling test of the year, the third graders wept as the scores were delivered and then their parents complained.
3. Use the data available as Chapter 4 Data Set 3 on pie preference to create a pie chart ☺ using SPSS.
4. For each of the followin.
Data Visualization - A Brief Overview
North Raleigh Rotarian Katie Turnbull gave a great presentation at our Friday morning extension meeting about data visualization. Katie is a consultant at research and advisory firm, Gartner, Inc.
Introduction to Data Visualization
This is a presentation I gave on Data Visualization at a General Assembly event in Singapore, on January 22, 2016. The presso provides a brief history of dataviz as well as examples of common chart and visualization formatting mistakes that you should never make.
Diowane2003
The document discusses different types of graphs such as bar graphs, pie charts, pictographs, and line graphs. It defines what a graph is and provides guidelines for creating good graphs, including determining the target audience, main messages, and appropriate graph type. The purpose of using graphs is to visually represent relationships between variables in a way that facilitates understanding and convinces readers.
Questionnaire analysis using_spss
The document discusses key considerations for designing questionnaires, including:
1. The format of questions will affect the answers, so questions should be short (under 25 words), understandable, and avoid double negatives.
2. Choosing an appropriate question format is important so responses are understandable and analyzable. Questions types include single answers, multiple choices, scales, and grids.
3. Pilot testing the questionnaire is essential to check that the data can be analyzed as intended and to refine ambiguous, leading, or poorly structured questions. Feedback from pilot participants should be solicited.
4. Generally, questionnaires should be limited to around 20 likert-scale questions to maintain participant interest and engagement. A variety of
diseñando datos
This document provides an overview of the origins and early development of graphs and charts. It discusses how many common chart types, such as bar charts, line charts and pie charts, were first invented in the late 18th century by William Playfair to visualize large amounts of economic and social data. It also describes some other key contributors in the following decades who helped advance different chart types, such as Joseph Priestley inventing the timeline, Charles Dupin creating the first choropleth map, and John Snow using a map to trace the source of a cholera outbreak in London. Overall, the document traces how charts have evolved from their early inventions to become ubiquitous tools for visualizing data today.
Technical Writing, October 24th, 2013
This document summarizes a lecture about effectively communicating data and research findings. It discusses 7 guidelines for research from a textbook by Anderson, including reviewing objectives, arranging information analyzably, and interpreting relationships for readers. It then covers using graphics effectively based on 5 additional guidelines from Anderson. The lecture concludes with discussing 4 ways to "cook" or prepare raw data so it is easily understandable: cutting unnecessary information, flavoring it appropriately for the audience, chopping data into relevant pieces, and making it easy to digest. Students are then assigned an activity comparing data sheets between original and new iPad models.
Focus on what you learned that made an impression, what may have s.docx
Focus on what you learned that made an impression, what may have surprised you, and what you found particularly beneficial and why. Specifically:
What did you find that was really useful, or that challenged your thinking?
What are you still mulling over?
Was there anything that you may take back to your classroom?
Is there anything you would like to have clarified?
ANSWER THE ABOVE QUESTIONS BASED ON THE DOCUMENTS BELOW
Introduction & Goals
This week, we will investigate the distribution of a variable and look at ways to best see the key features of a quantitative variable’s distribution. We will look at visualizations of data, including line plots, frequency tables, stemplots, and histograms. We will hone our ability to describe key features of a distribution from visualizations and use them to compare distributions. We will begin to think about ideas for the Comparative Study by brainstorming in our project groups.
Goals
:
Reinforce the idea that data will vary
Explain what the distribution of variable is
Identify five key features of a distribution: center, spread, shape, clusters & outliers
Identify and create appropriate displays for categorical and quantitative data in one variable, including bar graphs, line plots, frequency tables, and histograms
Analyze distributions using stemplots and histograms
Recognize advantages and limitations of histograms
Begin to explore technology for use in statistics
Begin work on Comparative Study Final Project
DOW #2: How Long Is A Minute?
In week 1, we gathered data for this week’s DoW, addressing the question:
“How long is a minute to an adult?”
This week we'll:
In investigations 1 & 2, you will analyze the data with dot plots, frequency tables, stemplots, and histograms.
In Exercise B2, you will post your initial analysis and interpretation to the discussion board by Wednesday, 10 PM EST and create at least three follow-up posts by Friday, 10 PM EST.
In Exercise D2 & E2, you will post your best histogram to the discussion board by Friday, 10 PM EST. Compare the histograms and choose the one you think best represents the distribution by Sunday, 10 PM EST
Investigation 1: Seeing the Distribution
As we emphasized in Week 1,
data varies
. This point may seem trivial, but it encapsulates one of the most fundamental concepts of statistics:
variability
. Statistical Analysis is really a study of the patterns we find within this variation in the data. The pattern(s) in the variation is called the
distribution
of the variable. Much of statistics focuses on ways to represent and describe the distribution of a variable.
Activities A & B in this investigation focus on representing and describing the distribution.
Activity C introduces Excel as a tool for looking at a distribution.
Inv 1, Activity A: Patterns in the Variation
As we emphasized in Week 1,
data varies
. This point may seem trivial, but it encapsulates one of the most fundamental concepts of statistics:
variability
. Statistical Analy.
Graphs
This document provides information about graphs, including their definition, purpose, types, and guidelines for creating effective graphs. A graph is a visual representation of the relationship between two variables plotted on x and y axes. Good graphs accurately portray data in a clear, simple manner to help readers understand relationships and trends. Common graph types include bar graphs, pie charts, pictographs, and line graphs, each suited for certain types of data. Guidelines for graphing include understanding the audience and message, and experimenting with different graph styles to pick the most appropriate one.
Dataviz 101: Data is beautiful, please don't ruin it by Anne-Marie Tousch, Se...
Dataviz 101: Data is beautiful, please don't ruin it by Anne-Marie Tousch, Se...Paris Women in Machine Learning and Data Science
Why is it suboptimal to visualize data as plain figures? What is the purpose of data visualization? Why should you care? What is the interplay between statistics, data analysis, and a good marketing story? In this talk, I'll give some answers and try to convince you to adopt best practices in dataviz.Aftros
This document provides a comprehensive guide to data visualization. It discusses the different types of visualization techniques available, how to choose the right type of visualization for your data and message, and tips for effective data visualization design. Specific visualization types covered include line graphs, bar charts, pie charts and dashboards/reports. The document also discusses common mistakes to avoid and strategies for labeling axes, handling color, mixing chart types, and using annotations to guide readers.
Insights From Data Visualization - Stephen Lett (Procter & Gamble)
Insights From Data Visualization - Stephen Lett (Procter & Gamble)BDPA Education and Technology Foundation
This month, we will dive into the world of data analysis and visualization. As data continues to proliferate our lives and work, the question of how to make sense of it and turn it into information and knowledge becomes more and more challenging. At the same time, powerful tools are becoming available to help analysts sift through data and present it in a way that draws attention to key bits of knowledge than can be derived. As such, the skills related to using these tools effectively have become highly sought-after as organizations seek to dig out the treasures hidden in their data troves.
Presentation by Stephen Lett (Procter & Gamble)Extension presentation
This document discusses different types of teaching aids used to enhance classroom instruction, including charts and posters. It defines charts and describes common chart types like pie charts, bar charts, and line charts. The key characteristics, advantages, and disadvantages of each chart type are outlined. Some additional teaching aids discussed include flip charts and strip tease charts. The document also defines posters and lists their purpose and characteristics, noting that posters should communicate information quickly and attract attention to be effective teaching aids.
Spss basics
This chapter introduces how to analyze simple questionnaires using SPSS. It will teach how to structure datasets for computer analysis, perform basic statistical techniques to describe data patterns, and display data visually. The chapter uses a lateralization questionnaire as a sample dataset to demonstrate simple analyses. It describes how to code questionnaire responses numerically and organize the data in a matrix format with rows for participants and columns for questions. This initial dataset captures patterns of right or left laterality preferences among the first six participants.
basic statistics
This document is an introduction to the textbook "Experimental Design and Analysis" by Howard J. Seltman. It provides an overview of the course content which teaches experimental design and statistical analysis. Key topics covered include experimental design principles, specific experimental designs and their corresponding analyses, and concepts like power, multiple comparisons, and model selection. The textbook contains examples from various fields but focuses on examples from behavioral and social sciences. It emphasizes learning statistical concepts and doing hands-on analysis to fully understand the material.
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Every graph tells a story
- 2. Why should you care about graphing? The ability to critically interpret graphs is one of the main indicators of college readiness. The inability to critically interpret graphs leads to people being being easily manipulated by advertisements and interest groups Remember, graphs are only interpretations of data, not the data itself.
- 4. These two graphs represent the same data (#1)
- 5. These two graphs represent the same data (#2)
- 6. Which graph do you think is “better” at displaying the data? Why? Why? Why?
- 7. What wrong with this picture???
- 8. Why is it unwise to draw final conclusions from graphs?
- 9. Why do scientists graph their data? to organize and emphasize the pertinent patterns from their data tables into a visual representation that is easier to understand. Although data tables themselves can hold all the experimental information, a graph usually minimizes the number of comparisons that can be made. Graphs help the viewer to visualize the results that answer the researchers original question. Otherwise your graph can be more confusing than the raw data.
- 10. Assignment: For each of the following graphs, discuss these questions with your partner. 1. What story is this graph telling you? 2. What is it NOT telling you? 3. What information would you like to see included?
- 12. Earnings and Unemployment versus Level of Education
- 13. 13
- 14. 14 Climate Change- The Hockey Stick 14