BUS 308 Week 2 Assignment
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BUS 308 Week 2 Assignment in EXCEL (A+ Grade Assured, Latest Data)
Week 2 | Testing means - T-tests | |||||||
In questions 2, 3, and 4 be sure to include the null and alternate hypotheses you will be testing. | ||||||||
In the first 4 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis. | ||||||||
1 | Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean. | |||||||
(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 - a constant.) | ||||||||
Note: These values are not the same as the data the assignment uses. The purpose is to analyze the results of t-tests rather than directly answer our equal pay question. | ||||||||
Based on these results, how do you interpret the results and what do these results suggest about the population means for male and female average salaries? | ||||||||
Males | Females | |||||||
Ho: Mean salary = | 45.00 | Ho: Mean salary = | 45.00 | |||||
Ha: Mean salary =/= | 45.00 | Ha: Mean salary =/= | 45.00 | |||||
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. | ||||||||
Male | Ho | Female | Ho | |||||
Mean | 52 | 45 | Mean | 38 | 45 | |||
Variance | 316 | 0 | Variance | 334.667 | 0 | |||
Observations | 25 | 25 | Observations | 25 | 25 | |||
Hypothesized Mean Difference | 0 | Hypothesized Mean Difference | 0 | |||||
df | 24 | df | 24 | |||||
t Stat | 1.968903827 | t Stat | -1.91321 | |||||
P(T<=t) one-tail | 0.03030785 | P(T<=t) one-tail | 0.03386 | |||||
t Critical one-tail | 1.71088208 | t Critical one-tail | 1.71088 | |||||
P(T<=t) two-tail | 0.060615701 | P(T<=t) two-tail | 0.06772 | |||||
t Critical two-tail | 2.063898562 | t Critical two-tail | 2.0639 | |||||
Conclusion: Do not reject Ho; mean equals 45 | Conclusion: Do not reject Ho; mean equals 45 | |||||||
Note: the Female results are done for you, please complete the male results. | ||||||||
Is this a 1 or 2 tail test? | Is this a 1 or 2 tail test? | 2 tail | ||||||
- why? | - why? | Ho contains = | ||||||
P-value is: | P-value is: | 0.06772 | ||||||
Is P-value < 0.05 (one tail test) or 0.25 (two tail test)? | Is P-value < 0.05 (one tail test) or 0.25 (two tail test)? | No | ||||||
Why do we not reject the null hypothesis? | Why do we not reject the null hypothesis? | P-value greater than (>) rejection alpha | ||||||
Interpretation of test outcomes: | ||||||||
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: | Male salary mean = Female salary mean | |||||||
Ha: | Male salary mean =/= Female salary mean | |||||||
Test to use: | t-Test: Two-Sample Assuming Equal Variances | |||||||
P-value is: | ||||||||
Is P-value < 0.05 (one tail test) or 0.25 (two tail test)? | ||||||||
Reject or do not reject Ho: | ||||||||
If the null hypothesis was rejected, calculate the effect size value: | ||||||||
If calculated, what is the meaning of effect size measure: | ||||||||
Interpretation: | ||||||||
b. | Is the one or two sample t-test the proper/correct apporach to comparing salary equality? Why? | |||||||
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.) | |||||||
Again, please assume equal variances for these groups. | ||||||||
Ho: | ||||||||
Ha: | ||||||||
Statistical test to use: | ||||||||
What is the p-value: | ||||||||
Is P-value < 0.05 (one tail test) or 0.25 (two tail test)? | ||||||||
Reject or do not reject Ho: | ||||||||
If the null hypothesis was rejected, calculate the effect size value: | ||||||||
If calculated, what is the meaning of effect size measure: | ||||||||
Interpretation: | ||||||||
4 | Since performance is often a factor in pay levels, is the average Performance Rating the same for both genders? | |||||||
NOTE: do NOT assume variances are equal in this situation. | ||||||||
Ho: | ||||||||
Ha: | ||||||||
Test to use: | t-Test: Two-Sample Assuming Unequal Variances | |||||||
What is the p-value: | ||||||||
Is P-value < 0.05 (one tail test) or 0.25 (two tail test)? | ||||||||
Do we REJ or Not reject the null? | ||||||||
If the null hypothesis was rejected, calculate the effect size value: | ||||||||
If calculated, what is the meaning of effect size measure: | ||||||||
Interpretation: | ||||||||
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? |
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