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                                                                        Chapter 13
                                                        Forming Associations
                                                        with Two-Way Tables
                                         In This Chapter
                                           Reading and interpreting two-way tables
                                           Figuring probabilities and checking for independence
                                           Watching out for Simpson’s Paradox
                                                       ooking for relationships between two categorical (qualitative) variables
                                                    Lis a very common goal for researchers. For example, many medical stud-
                                                    ies center on how some characteristic about a person either raises or lowers
                                                    his chance of getting some disease. Marketers ask questions like, “Who is
                                                    more likely to buy our product: males or females?” Sports stat freaks wonder
                                                    about things like “Does winning the coin toss at the beginning of a football
                                                    game increase your team’s chance of winning the game?”
                                                    To answer each of the above questions, you must first collect data (from a
                                                    random sample) on the two categorical variables being compared — call
                                                    them x and y. Then you organize that data into a table that contains columns
                                                    and rows, showing how many individuals from the sample appear in each
                                                    combination of x and y. Finally, you use the information in the table to con-
                                                    duct a hypothesis test (called the Chi-square test). Using the Chi-square test,
                                                    you can determine whether you can see a relationship between x and y in the
                                                    population from which the data was drawn. This last step needs the machin-
                                                    ery from Chapter 14 to accomplish it. The goals of this chapter are to under-
                                                    stand what it means for two qualitative variables (x and y) to be associated
                                                    and to discover how to use percentages to determine whether a sample data
                                                    set appears to show a relationship between x and y.

                                                    Suppose you’re collecting data on cell-phone users, and you want to find out
                                                    whether more females use cell phones than males. A study of 508 randomly
                                                    selected male cell-phone users and 508 randomly selected female cell-phone
                                                    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.
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