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Chapter 1: Exploring Data

Chapter 1: Exploring Data. Section 1.1 Analyzing Categorical Data. The Practice of Statistics, 4 th edition - For AP* STARNES, YATES, MOORE. Chapter 1 Exploring Data. Introduction : Data Analysis: Making Sense of Data 1.1 Analyzing Categorical Data

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Chapter 1: Exploring Data

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  1. Chapter 1: Exploring Data Section 1.1 Analyzing Categorical Data The Practice of Statistics, 4th edition - For AP* STARNES, YATES, MOORE

  2. Chapter 1Exploring Data • Introduction:Data Analysis: Making Sense of Data • 1.1Analyzing Categorical Data • 1.2Displaying Quantitative Data with Graphs • 1.3Describing Quantitative Data with Numbers

  3. Section 1.1Analyzing Categorical Data Learning Objectives After this section, you should be able to… • CONSTRUCT and INTERPRET bar graphs and pie charts • RECOGNIZE “good” and “bad” graphs • CONSTRUCT and INTERPRET two-way tables • DESCRIBE relationships between two categorical variables • ORGANIZE statistical problems

  4. Analyzing Categorical Data • Categorical Variables place individuals into one of several groups or categories • The values of a categorical variable are labels for the different categories • The distribution of a categorical variable lists the count or percent of individuals who fall into each category. Variable Values Count Round-off error Percent

  5. Analyzing Categorical Data • Additional Example Variable Values Count Percent

  6. Analyzing Categorical Data • Displaying categorical data Frequency tables can be difficult to read. Sometimes is is easier to analyze a distribution by displaying it with a bar graph or pie chart.

  7. Discrete and Continuous Random Variables • AP ERROR ALERT! Many students spend lots of time constructing graphs only to forget the labels. This will always cause a deduction in your score. It is imperative to communicate the data with the proper labels and scaling.

  8. Discrete and Continuous Random Variables • AP ERROR ALERT! Unless specifically directed to do so, do not create a pie chart.

  9. Graphs: Good and Bad Analyzing Categorical Data Bar graphs compare several quantities by comparing the heights of bars that represent those quantities. Our eyes react to the area of the bars as well as height. Be sure to make your bars equally wide. Avoid the temptation to replace the bars with pictures for greater appeal…this can be misleading! Example This ad for DIRECTV has multiple problems. How many can you point out?

  10. Graphs: Good and Bad Analyzing Categorical Data Compare these two bar graphs of the tax rates if the Bush tax cuts expire. Stacked bar graph example: http://www.chcf.org/publications/2012/08/data-viz-hcc-national Source: Forbes

  11. Whose Responsibility is it? Analyzing Categorical Data Who is responsible for the information? The advertiser or the consumer?

  12. Example: What personal media do you own? Analyzing Categorical Data Here are the percents of 15- to 18-year-olds who own the following personal media devices, according to the Kaiser Family Foundation. Make a well-labeled bar graph to display the data. Describe what you see. Would it be appropriate to make a pie chart for these data? Why or why not?

  13. See you at lunch! Class starts at 12:15.

  14. Analyzing Categorical Data • Two-Way Tables and Marginal Distributions When a dataset involves two categorical variables, we begin by examining the counts or percents in various categories for one of the variables. Definition: Two-way Table – describes two categorical variables, organizing counts according to a row variable and a column variable. Example What are the variables described by this two-way table? How many young adults were surveyed?

  15. Analyzing Categorical Data • Two-Way Tables and Marginal Distributions A sample of 200 children from the United Kingdom ages 9 – 17 was selected from the CensusAtSchool website. The gender of each student was recorded along with which super power they would most like to have: invisibility, super strength, telepathy, ability to fly, or ability to freeze time. Here are the results. Example What are the variables described by this two-way table?

  16. Analyzing Categorical Data • Two-Way Tables and Marginal Distributions Definition: The Marginal Distribution of one of the categorical variables in a two-way table of counts is the distribution of values of that variable among all individuals described by the table. • Note: Percents are often more informative than counts, especially when comparing groups of different sizes. • To examine a marginal distribution, • Use the data in the table to calculate the marginal distribution (in percents) of the row or column totals. • Make a graph to display the marginal distribution.

  17. Analyzing Categorical Data • Two-Way Tables and Marginal Distributions Example, p. 13 Examine the marginal distribution of chance of getting rich. What is the marginal distribution of gender? Calculate percentages and make a graph to show it. Now go back and find the marginal distributions of the data on super powers. Slide 12

  18. Analyzing Categorical Data • Relationships Between Categorical Variables • Marginal distributions tell us nothing about the relationship between two variables. Definition: A Conditional Distribution of a variable describes the values of that variable among individuals who have a specific value of another variable. • To examine or compare conditional distributions, • Select the row(s) or column(s) of interest. • Use the data in the table to calculate the conditional distribution (in percents) of the row(s) or column(s). • Make a graph to display the conditional distribution. • Use a side-by-side bar graph or segmented bar graph to compare distributions.

  19. Analyzing Categorical Data • Two-Way Tables and Conditional Distributions Example, p. 15 Calculate the conditional distribution of opinion among males. Examine the relationship between gender and opinion. Now find the conditional distribution of desired superpower among males and females. Calculate percentages and make a graph. Slide 12

  20. Analyzing Categorical Data • Association Between Categorical Variables • One type of relationship is an association. Definition: We say that there is an association between two variables if specific values of one variable tend to occur in common with specific values of another. • To examine data for an association, • Select the row(s) or column(s) of interest. • Use the data in the table to calculate the conditional distribution (in percents) of the row(s) or column(s). • Determine whether a specific value of one variable tends to occur in common with a specific value of another

  21. Analyzing Categorical Data • Association: A Titanic Disaster Example, p. 19 • The movie Titanic suggested the following: • First class passengers received special treatment in boarding the lifeboats while other passengers were prevented from doing so. • Women and children boarded the lifeboats first, followed by the men. • What do the data tell us about these two suggestions? • How does gender affect the relationship between class of travel and survival status? Explain. In 1912 the luxury liner Titanic, on its first voyage across the Atlantic, struck an iceberg and sank. Some passengers got off the ship in lifeboats, but many died. The tables at left give information about the adult passengers who lived and who died, by class of travel. If a question ever asks for a relationship, you must address whether or not an association exists.

  22. Analyzing Categorical Data • Organizing a Statistical Problem • As you learn more about statistics, you will be asked to solve more complex problems. • Here is a four-step process you can follow. How to Organize a Statistical Problem: A Four-Step Process State: What’s the question that you’re trying to answer? Plan: How will you go about answering the question? What statistical techniques does this problem call for? Do: Make graphs and carry out needed calculations. Conclude: Give your practical conclusion in the setting of the real-world problem.

  23. Section 1.1Analyzing Categorical Data Summary In this section, we learned that… • The distribution of a categorical variable lists the categories and gives the count or percent of individuals that fall into each category. • Pie charts and bar graphs display the distribution of a categorical variable. • A two-way table of counts organizes data about two categorical variables. • The row-totals and column-totals in a two-way table give the marginal distributions of the two individual variables. • There are two sets of conditional distributions for a two-way table.

  24. Section 1.1Analyzing Categorical Data Summary, continued In this section, we learned that… • We can use a side-by-side bar graph or a segmented bar graph to display conditional distributions. • To describe the association between the row and column variables, compare an appropriate set of conditional distributions. • Even a strong association between two categorical variables can be influenced by other variables lurking in the background. • You can organize many problems using the four steps state, plan, do, and conclude.

  25. Looking Ahead… In the next Section… • We’ll learn how to display quantitative data. • Dotplots • Stemplots • Histograms • We’ll also learn how to describe and compare distributions of quantitative data.

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