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Week6Project2.docx

Project Week 6

Using the ROI data set:

1. For each of the 2 majors test the hypothesis at the 5% significance level:

. The mean ‘Cost’ for a college is $160,000. Be sure to interpret your results.

Business major:

Since P(T<=t) two-tail = 0.020173879 < 0.05, so we should reject the null hypothesis, that is to say, the mean ‘Cost’ for Business major is not $160,000.

Engineering major:

Since P(T<=t) two-tail = 0.75598907>0.05, so we should cannot reject the null hypothesis, that is to say, the mean ‘Cost’ for Engineering major is $160,000.

· For Business versus Engineering majors conduct a two sample test of the hypothesis at the 10% significance level (assume the variances are not equal):

. The average ’30-Year ROI’ for Business majors is less than for Engineering Majors.  Be sure to interpret your results.

t-Test: Two-Sample Assuming Unequal Variances

 

30 Year ROI Business

30 Year ROI Engineering

Mean

1477800

1838000

Variance

17673957895

32327578947

Observations

20

20

Hypothesized Mean Difference

0

df

35

t Stat

-7.203889288

P(T<=t) one-tail

1.04423E-08

t Critical one-tail

1.306211802

P(T<=t) two-tail

2.08847E-08

t Critical two-tail

1.689572458

 

Since P(T<=t) one-tail = 1.04423E-08<0.1, so we cannot reject the null hypothesis, that is to say, the average ’30-Year ROI’ for Business majors is less than for Engineering Majors.

Costtest

Mean188632160000

Variance25505963430

Observations2020

Hypothesized Mean Difference0

df19

t Stat2.535396094

P(T<=t) one-tail0.01008694

t Critical one-tail1.729132812

P(T<=t) two-tail0.020173879

t Critical two-tail2.093024054

Costtest

Mean164680160000

Variance44069844110

Observations2020

Hypothesized Mean Difference0

df19

t Stat0.31527541

P(T<=t) one-tail0.37799454

t Critical one-tail1.72913281

P(T<=t) two-tail0.75598907

t Critical two-tail2.09302405