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Pearson Correlation Coefficient and the Chi-Square Test
Pearson Correlation Coefficient and the Chi-Square Test
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The purpose of this assignment is to practice calculating and interpreting the Pearson correlation coefficient and a chi-square test of independence.
For this assignment, complete Problems 13.132 and 15.88 in the textbook. Include your process for conducting the calculations. You can complete the calculations by hand or using Excel or SPSS. If you use Excel or SPSS, copy and paste your output results into a Word document.
When addressing each textbook problem, provide a response for each of the six steps of hypothesis testing listed below.
Pick a test.
Check the assumptions.
List the hypotheses.
Set the decision rule.
Calculate the test statistic.
Interpret the results. (What was done? What was found? What does it mean? What suggestions exist for future research?)
Submit a Word document with your problem answers to each of the six steps. If Excel or SPSS was used to complete the assignment, submit the second Word document containing the screenshots to the instructor.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the expectations for successful completion.
Pearson Correlation Coefficient and the Chi-Square Test
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15.88 A political scientist developed a theory that after an election, supporters of the losing candidate
removed the bumper stickers from their cars faster than did supporters of the winning
candidate. The day before a presidential election, he randomly selected parking lots,
and at each selected parking lot, he randomly selected one car with a bumper sticker and
recorded which candidate it supported. The day after the election, he followed the same
procedure with a new sample of randomly selected parking lots. For both days, he then
classified the bumper stickers as supporting the winning or losing candidate. Below are
the results. Use hypothesis testing to see if a difference exists between how winners and
losers behave.
Observed Frequencies
Winner Loser
Before 34 32
After 28 10
