Use the data set in the Problem Set Data document.
 First we are going to see if males and females differ in their choice of college major. Since both variables are measured on a categorical scale, we have to use a chisquare test to address this question. You can learn about chisquares on pp. 73546 of the textbook.
Using college major and sex as your variables, perform a chisquare analysis. To do this, go to Analyze—Descriptive Statistics—Crosstabs. Enter Gender in the box labeled “Row(s)” and Major in the box labeled “Column(s).” Click on Statistics, and then on ChiSquare. Click “Continue,” then hit “OK” to run the analysis.
Paste the tables labeled “Gender*Major Crosstabulation” and “ChiSquare Tests” here.
Major Crosstabulation Table
Gender * Major Crosstabulation 

Count  
Major 
Total 

1 
2 
3 
4 
5 

Gender  1 
19 
11 
11 
12 
4 
57 
2 
10 
14 
10 
3 
6 
43 

Total 
29 
25 
21 
15 
10 
100 
ChiSquare Test Table
ChiSquare Tests 

Value 
df 
Asymp. Sig. (2sided) 

Pearson ChiSquare 
7.181^{a} 
4 
.127 
Likelihood Ratio 
7.467 
4 
.113 
LinearbyLinear Association 
.063 
1 
.802 
N of Valid Cases 
100 

a. 1 cells (10.0%) have expected count less than 5. The minimum expected count is 4.30. 
The results of the chisquare analysis will be listed in the row labeled “Pearson chisquare” in the “ChiSquare Tests” table. Explain your results using APA formatting. See section 18.5.7 on p. 746 of the SPSS book for how to do this
 Next, we are going to compare coffee drinkers and noncoffee drinkers on the amount of coffee they consume. The grouping variable is labeled Coffee (1 = coffee drinker, 2 = noncoffee drinker), and the outcome variable is labeled Num_cups.
 First state the name of the parametric test that would typically be used to compare the results of two groups.
The parametric test that could be used to compare two groups is a ttest
 Then look at the distribution of the data to determine if the assumptions of this test are met. An easy way to do this would be to go to Analyze—Descriptive Statistics—Frequencies. Enter the outcome variable (Num_cups) in the “Variable(s)” box. Then click on Charts and then on Histograms. Click Continue, then OK to run the analysis. Paste the histogram here
c) After taking a look at the distribution of the data, state why these data violate a key assumption of parametric tests.
d) Find the medians of the 2 groups. One way to do this is to go to AnalyzeDescriptive Statistics–Explore. Enter Coffee under Factor List and Num_cups under Dependent List. Click OK to run the analysis. List the medians of the coffee and noncoffee groups here.
e) Now conduct a MannWhitney U test to see if the two groups differ in the amount of coffee consumed. You can learn more about the MannWhitney U test on pp. 217228 of the SPSS book.
To conduct the test, go to Analyze—Nonparametric Tests—Independent Samples. Under the Objective tab, click on “Customize Analysis.” Under the Fields tab, drag Coffee to the box labeled Groups and Num_cups to the box labeled Test Fields. Under the Settings tab, click on Customize Tests, then on MannWhitney U
Click Run to run the analysis. Paste here the table labeled “Hypothesis Test Summary.”
f) Explain your results using APA formatting. See section 6.4.6 in the SPSS book for an example, but don’t worry about effect sizes (r).
To write up the results, you’ll need to double click on the Hypothesis Test Summary table to see the value of the MannWhitney U statistic and the Standardized Test Statistic (z), but you don’t need to paste these detailed results here.
If the groups differed, please don’t just state that they differed significantly; instead, explain which group drank significantly more coffee than which other groups