need help with BUS 308 week two

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wk_2_data_questions.xlsx
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Sheet1

Score: Week 2 Testing means - T-tests Q3
In questions 2 and 3, be sure to include the null and alternate hypotheses you will be testing. Ho Female Male Female
In the first 3 questions use alpha = 0.05 in making your decisions on rejecting or not rejecting the null hypothesis. 45 34 1.017 1.096
45 41 0.870 1.025
<1 point> 1 Below are 2 one-sample t-tests comparing male and female average salaries to the overall sample mean. 45 23 1.157 1.000
(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) 45 22 0.979 0.956
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? 45 23 1.134 1.000
Males Females 45 42 1.149 1.050
Ho: Mean salary = 45 Ho: Mean salary = 45 45 24 1.052 1.043
Ha: Mean salary =/= 45 Ha: Mean salary =/= 45 45 24 1.175 1.043
45 69 1.043 1.210
Note: While the results both below are actually from Excel's t-Test: Two-Sample Assuming Unequal Variances, 45 36 1.134 1.161
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. 45 34 1.043 1.096
Male Ho Female Ho 45 57 1.000 1.187
Mean 52 45 Mean 38 45 45 23 1.074 1.000
Variance 316 0 Variance 334.6666666667 0 45 50 1.020 1.041
Observations 25 25 Observations 25 25 45 24 0.903 1.043
Hypothesized Mean Difference 0 Hypothesized Mean Difference 0 45 75 1.122 1.119
df 24 df 24 45 24 0.903 1.043
t Stat 1.9689038266 t Stat -1.9132063573 45 24 0.982 1.043
P(T<=t) one-tail 0.0303078503 P(T<=t) one-tail 0.0338621184 45 23 1.086 1.000
t Critical one-tail 1.7108820799 t Critical one-tail 1.7108820799 45 22 1.075 0.956
P(T<=t) two-tail 0.0606157006 P(T<=t) two-tail 0.0677242369 45 35 1.052 1.129
t Critical two-tail 2.0638985616 t Critical two-tail 2.0638985616 45 24 1.140 1.043
Conclusion: Do not reject Ho; mean equals 45 Conclusion: Do not reject Ho; mean equals 45 45 77 1.087 1.149
Is this a 1 or 2 tail test? Is this a 1 or 2 tail test?
- why? - why?
P-value is: P-value is: 45 55 1.052 1.145
Is P-value > 0.05? Is P-value > 0.05? 45 65 1.157 1.140
Why do we not reject Ho? Why do we not reject Ho?
Interpretation:
<1 point> 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:
Interpretation:
b. Since the one and two sample t-test results provided different outcomes, which is the proper/correct apporach to comparing salary equality? Why?
<1 point> 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.)
Ho:
Ha:
Statistical test to use:
Place B75 in Outcome range box.
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:
Interpretation:
<1 point> 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:
Place B106 in Outcome range box.
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:
Interpretation:
<2 points> 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?

Sheet2

Sheet3

ID Salary Compa Midpoint Age Performance Rating Service Gender Raise Degree Gender1 Grade
26 24 1.043 23 22 95 2 1 6.2 1 F A
37 22 0.956 23 22 95 2 1 6.2 1 F A
35 24 1.043 23 23 90 4 1 5.3 1 F A
36 23 1.000 23 27 75 3 1 4.3 1 F A
39 35 1.129 31 27 90 6 1 5.5 1 F B
17 69 1.210 57 27 55 3 1 3 0 F E
31 24 1.043 23 29 60 4 1 3.9 0 F A
10 22 0.956 23 30 80 7 1 4.7 0 F A
3 34 1.096 31 30 75 5 1 3.6 0 F B
13 42 1.050 40 30 100 2 1 4.7 1 F C
24 50 1.041 48 30 75 9 1 3.8 1 F D
18 36 1.161 31 31 80 11 1 5.6 1 F B
8 23 1.000 23 32 90 9 1 5.8 0 F A
14 24 1.043 23 32 90 12 1 6 0 F A
15 24 1.043 23 32 80 8 1 4.9 0 F A
42 24 1.043 23 32 100 8 1 5.7 0 F A
7 41 1.025 40 32 100 8 1 5.7 0 F C
48 65 1.140 57 34 90 11 1 5.3 1 F E
23 23 1.000 23 36 65 6 1 3.3 1 F A
45 55 1.145 48 36 95 8 1 5.2 0 F D
11 23 1.000 23 41 100 19 1 4.8 0 F A
43 77 1.149 67 42 95 20 1 5.5 1 F F
20 34 1.096 31 44 70 16 1 4.8 1 F B
28 75 1.119 67 44 95 9 1 4.4 1 F F
22 57 1.187 48 48 65 6 1 3.8 0 F D
40 25 1.086 23 24 90 2 0 6.3 0 M A
32 28 0.903 31 25 95 4 0 5.6 0 M B
41 43 1.075 40 25 80 5 0 4.3 0 M C
34 28 0.903 31 26 80 2 0 4.9 1 M B
19 24 1.043 23 32 85 1 0 4.6 1 M A
1 58 1.017 57 34 85 8 0 5.7 0 M E
27 40 1.000 40 35 80 7 0 3.9 1 M C
33 64 1.122 57 35 90 9 0 5.5 1 M E
5 47 0.979 48 36 90 16 0 5.7 1 M D
6 76 1.134 67 36 70 12 0 4.5 1 M F
47 62 1.087 57 37 95 5 0 5.5 1 M E
50 66 1.157 57 38 80 12 0 4.6 0 M E
46 65 1.140 57 39 75 20 0 3.9 1 M E
25 24 1.043 23 41 70 4 0 4 0 M A
49 60 1.052 57 41 95 21 0 6.6 0 M E
4 66 1.157 57 42 100 16 0 5.5 1 M E
21 76 1.134 67 43 95 13 0 6.3 1 M F
16 47 1.175 40 44 90 4 0 5.7 0 M C
30 49 1.020 48 45 90 18 0 4.3 0 M D
38 56 0.982 57 45 95 11 0 4.5 0 M E
44 60 1.052 57 45 90 16 0 5.2 1 M E
9 77 1.149 67 49 100 10 0 4 1 M F
2 27 0.870 31 52 80 7 0 3.9 0 M B
12 60 1.052 57 52 95 22 0 4.5 0 M E
29 72 1.074 67 52 95 5 0 5.4 0 M F
The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equal work (under the Equal Pay Act)?
Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work.
The column labels in the table mean:
ID – Employee sample number Salary – Salary in thousands
Age – Age in years Performance Rating – Appraisal rating (Employee evaluation score)
Service – Years of service (rounded)
Midpoint – salary grade midpoint Gender: 0 = male, 1 = female
Grade – job/pay grade Raise – percent of last raise
Gender1 (Male or Female) Degree (0= BS\BA 1 = MS)
Compa - salary divided by midpoint