Nonparametric Analysis of Data Set
The datasets consist of the following variables:
• Sub_num
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• Gender
• Major
• Coffee, and • Num_cups.
The datasets are used for the nonparametric analysis to investigate whether the choices of college majors are different between males and female’s individuals. The analysis is carried out with the chi-square test and the document presents the output as follows:
Gender * Major Cross-tabulation
Count
Major
Total
Gender
Total
Chi-Square Tests
Value
Df
Asymp. Sig. (2-sided)
Pearson Chi-Square
Likelihood Ratio
Linear-by-Linear Association
N of Valid Cases
The minimum expected count is 4.30 and 1 cells (10.0%) have the expected count less than 5.
The analysis of the chi-square test is used to assess whether both males and females’ participants are different in the choice of colleges’ major. The frequencies are analyzed and the outcome of the tests show that they are significant because:
p = .127,
?2 (4, N=29) = 7.18.
Thus, both males and females are different in their college major choices since males have higher rate scores of 57 and females scores a total number of 43.
2. The document also compares coffee and non-coffee drinkers with reference to the amount of coffee each group consumes. The non-parametric test is selected for the analysis and dependent variable is measured using a small scale of measurement. The parameter test is able to meet the assumptions. The output of the analysis is as follows:
Num_cups
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
0
54
54.0
54.0
54.0
1
28
28.0
28.0
82.0
2
14
14.0
14.0
96.0
3
4
4.0
4.0
Total
Case Processing Summary
Coffee
Cases
Valid
Missing
Total
N
Percent
N
Percent
N
Percent
Num_cups
0
58
0
0.0%
58
1
42
0
0.0%
42
Descriptive
Coffee
Statistic
Std. Error
Num_cups
0
Mean
.24
.067
95% Confidence Interval for Mean
Lower Bound
.11
Upper Bound
.37
5% Trimmed…
