Displaying & Describing Categorical Data

Displaying & Describing Categorical Data Chapter 3

Look at different types of data and checking a condition before plugging ahead. • Writing clear explanations in context. • Independence and the importance to statistics. • Simpson’s Paradox: think deeply or be mislead!! Objective

Number 1 Rule of Data Analysis? Draw a picture!!! Frequency Table: records totals and categories names!! Frequency Tables

Counts are useful but sometimes we want to know the fraction or proportion of data in each category. • These counts are expressed as percentages. • Relative Frequency Table: displays percentages rather than counts Frequency Tables

Both these types of tables describe the distribution across categories. Frequency Tables

A bar chart displays the distribution of a categorical variable, showing the counts for each category next to each other for easy comparison. Bar Charts

If we are interested in the relative proportion we could replace the counts with percentages and use a relative frequency bar chart. Bar Charts

Portable MP3 music players, such as the Apple iPod, are popular-but not equally popular with people of all ages. Here are the percents of people in various groups who own a portable MP3 player, according to an Arbitron survey of 1112 randomly selected people. Who Owns an MP3 Player?

The area principle says that the area occupied by a part of the graph should correspond to the magnitude of the value it represents. • Violations of this principle are a common way to lie with statistics. Area Principle

Who Buys iMacs?

Pie Charts show the whole group of cases as a circle . They slice the circle into pieces whose sizes are proportional to the fraction of the whole in each category. Pie Charts

Think before you draw!! • Always check the categorical condition. • If you want to make a relative frequency bar chart or a pie chart, make sure the categories do not overlap. • If they overlap you can still make the chart but your percentages will not add up to 100. What should you pick!!

To look at two categorical variables together, we often arrange the counts in a two-way table. • A contingency table displays counts and, sometimes percentages of individuals falling into named categories on two or more variables. The table categorizes the individuals on all variables at once to reveal possible patterns in one variable that may be contingent on the category of the other. Contingency Tables

In a contingency table, the distribution of either variable alone is called the marginal distribution. The counts or percentages are the totals found in the margins (last row or column) of the table. Contingency Tables

In a contingency table, the distribution of either variable alone is called the marginal distribution. The counts or percentages are the totals found in the margins (last row or column) of the table. Marginal Distribution

A survey of 4826 randomly selected young adults (aged 19-25) asked, “What do you think are the chances you will have much more than a middle-class income at age 30?” The table below shows the responses, omitting a few people who refused to respond or who said they were already rich. Example

Example

The distribution of a variable restricting the “Who” to consider only a smaller group of individuals is called a conditional distribution. • In a contingency table, when the distribution of one variable is the same for all categories of another, we say that the variables are independent. Conditional Distributions

Segmented Bar Charts treat each bar as the “whole” and divides it proportionally into segments corresponding to the percentage in each group. Segmented Bar Charts

When averages are taken across different groups, they can appear to contradict the overall averages. Simpson’s Paradox

It’s the last inning of the important game. Your team is a run down with bases loaded and two outs. The pitcher is due up, so you’ll be sending in a pinch hitter. There are 2 batters available on the bench. Whom should you send to bat? Example

Displaying & Describing Categorical Data