The Chi-Square Test procedure is typically used to test observed frequencies against a single expected value that is equal for all rows. However, the distribution of values may not follow that pattern. In genetics, for example, you might expect to see a trait dominant in 75% of the population and recessive in the other 25%. The Chi-Square Test procedure allows you to specify a customized set of expected values, thereby permitting a wide variety of models to be tested.

A clothing manufacturer tries first-class postage for direct mailings, hoping for faster responses than with bulk mail. Order-takers record how many weeks after the mailing each order is taken.

This information is collected in the file mailresponse.sav. Use Chi-Square Test to determine whether the percentage of orders by week between the two methods differs.