Reference no: EM13938653

Question 1. Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean.

Males Females

Ho: Mean salary = 45 Ho: Mean salary = 45
Ha: Mean salary =/= 45 Ha: Mean salary =/= 45

(Note: a one-sample t-test in Excel can be performed by selecting the 2-sample unequal variance t-test and making the second variable = Ho value -- see column S)

	Male	Ho
Mean	52	45
Variance	316	0
Observations	25	25
Hypothesized Mean Difference	0
df	24
t Stat	1.96890383
P(T<=t) one-tail	0.03030785
t Critical one-tail	1.71088208
P(T<=t) two-tail	0.0606157
t Critical two-tail	2.06389856

Conclusion: Do not reject Ho; mean equals 45
Is this a 1 or 2 tail test?
- why?
P-value is:
Is P-value > 0.05?
Why do we not reject Ho?

	Female	Ho
Mean	38	45
Variance	334.667	0
Observations	25	25
Hypothesized Mean Difference	0
df	24
t Stat	-1.9132
P(T<=t) one-tail	0.03386
t Critical one-tail	1.71088
P(T<=t) two-tail	0.06772
t Critical two-tail	2.0639

Conclusion: Do not reject Ho; mean equals 45

Is this a 1 or 2 tail test?
- why?
P-value is:
Is P-value > 0.05?
Why do we not reject Ho?

Based on our sample, how do you interpret the results and what do these results suggest about the population means for male and female average salaries?

Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances,

having no variance in the Ho variable makes the calculations default to the one-sample t-test outcome - we are tricking Excel into doing a one sample test for us.

Question 2. Based on our sample data set, perform a 2-sample t-test to see if the population male and female average salaries could be equal to each other.

(Since we have not yet covered testing for variance equality, assume the data sets have statistically equal variances.)

Ho:
Ha:
Test to use:
Place B43 in Outcome range box.

P-value is:
Is P-value < 0.05?
Reject or do not reject Ho:
If the null hypothesis was rejected, what is the effect size value:
Meaning of effect size measure:

b. Since the one and two sample t-test results provided different outcomes, which is the proper/correct apporach to comparing salary equality? Why?

Question 3: Based on our sample data set, can the male and female compas in the population be equal to each other? (Another 2-sample t-test.)

What is the p-value:
Is P-value < 0.05?
Reject or do not reject Ho:
If the null hypothesis was rejected, what is the effect size value:
Meaning of effect size measure:

Question  4: Since performance is often a factor in pay levels, is the average Performance Rating the same for both genders?

Ho:
Ha:

Test to use:

What is the p-value:
Is P-value < 0.05?
Do we REJ or Not reject the null?
If the null hypothesis was rejected, what is the effect size value:
Meaning of effect size measure:

Question 5: If the salary and compa mean tests in questions 2 and 3 provide different results about male and female salary equality, which would be more appropriate to use in answering the question about salary equity? Why?

What are your conclusions about equal pay at this point?

Attachment:- 3.rar