Nonparametric Analysis of Data Set

The datasets consist of the following variables:

• Sub_num

• 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…