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222 Part IV: Building Strong Connections with Chi-Square Tests
Suppose you’re collecting data on cellphone users, and you want to find out
whether more females use cellphones for personal use than males. A study
of 508 randomly selected male cellphone users and 508 randomly selected
female cellphone users conducted by a wireless company found that women
tend to use their phones for personal calls more than men (big shocker). The
survey showed that 427 of the women said they used their wireless phones
primarily to talk with friends and family, while only 325 of the men admitted
to doing so.
But you can’t stop there. You need to break down this information, calculate
some percentages, and compare those percentages to see how close they
really are. Sample results vary from sample to sample, and differences can
appear by chance.
In this chapter, you find out how to organize data from categorical variables
(that’s data based on categories rather than measurements) into a table
format. This skill is especially useful when you’re looking for relationships
between two categorical variables, such as using a cellphone for personal
calls (a yes or no category) and gender (male or female). You also summarize
the data to answer your questions. And, finally, you get to figure out, once
and for all, what’s going on with that Simpson’s Paradox thing.
Breaking Down a Two-Way Table
A two-way table is a table that contains rows and columns that helps you
organize data from categorical variables in the following ways:
✓ Rows represent the possible categories for one categorical variable,
such as males and females.
✓ Columns represent the possible categories for a second categorical vari-
able, such as using your cellphone for personal calls, or not.
Organizing data into a two-way table
To organize your data into a two-way table, first set up the rows and columns.
Table 13-1 shows the setup for the cellphone data example that I set up in the
chapter introduction.
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