| Score: | Week 3 | ANOVA and Paired T-test |
| At this point we know the following about male and female salaries. |
| a. | Male and female overall average salaries are not equal in the population. |
| b. | Male and female overall average compas are equal in the population, but males are a bit more spread out. |
| c. | The male and female salary range are almost the same, as is their age and service. |
| d. | Average performance ratings per gender are equal. |
| Let's look at some other factors that might influence pay - education(degree) and performance ratings. |
| <1 point> | 1 | Last week, we found that average performance ratings do not differ between males and females in the population. |
| Now we need to see if they differ among the grades. Is the average performace rating the same for all grades? |
| (Assume variances are equal across the grades for this ANOVA.) | You can use these columns to place grade Perf Ratings if desired. |
| A | B | C | D | E | F |
| Null Hypothesis: |
| Alt. Hypothesis: |
| Place B17 in Outcome range box. |
| Interpretation: |
| 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 (eta squared): |
| Meaning of effect size measure: |
| What does that decision mean in terms of our equal pay question: |
| <1 point> | 2 | While it appears that average salaries per each grade differ, we need to test this assumption. |
| Is the average salary the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.) |
| Use the input table to the right to list salaries under each grade level. |
| Null Hypothesis: | If desired, place salaries per grade in these columns |
| Alt. Hypothesis: | A | B | C | D | E | F |
| Place B55 in Outcome range box. |
| What is the p-value: |
| Is P-value < 0.05? |
| Do you reject or not reject the null hypothesis: |
| If the null hypothesis was rejected, what is the effect size value (eta squared): |
| Meaning of effect size measure: |
| Interpretation: |
| <1 point> | 3 | The table and analysis below demonstrate a 2-way ANOVA with replication. Please interpret the results. |
| BA | MA | Ho: Average compas by gender are equal |
| Male | 1.017 | 1.157 | Ha: Average compas by gender are not equal |
| 0.870 | 0.979 | Ho: Average compas are equal for each degree |
| 1.052 | 1.134 | Ha: Average compas are not equal for each degree |
| 1.175 | 1.149 | Ho: Interaction is not significant |
| 1.043 | 1.043 | Ha: Interaction is significant |
| 1.074 | 1.134 |
| 1.020 | 1.000 | Perform analysis: |
| 0.903 | 1.122 |
| 0.982 | 0.903 | Anova: Two-Factor With Replication |
| 1.086 | 1.052 |
| 1.075 | 1.140 | SUMMARY | BA | MA | Total |
| 1.052 | 1.087 | Male |
| Female | 1.096 | 1.050 | Count | 12 | 12 | 24 |
| 1.025 | 1.161 | Sum | 12.349 | 12.9 | 25.249 |
| 1.000 | 1.096 | Average | 1.0290833333 | 1.075 | 1.0520416667 |
| 0.956 | 1.000 | Variance | 0.006686447 | 0.0065198182 | 0.0068660417 |
| 1.000 | 1.041 |
| 1.043 | 1.043 | Female |
| 1.043 | 1.119 | Count | 12 | 12 | 24 |
| 1.210 | 1.043 | Sum | 12.791 | 12.787 | 25.578 |
| 1.187 | 1.000 | Average | 1.0659166667 | 1.0655833333 | 1.06575 |
| 1.043 | 0.956 | Variance | 0.006102447 | 0.0042128106 | 0.004933413 |
| 1.043 | 1.129 |
| 1.145 | 1.149 | Total |
| Count | 24 | 24 |
| Sum | 25.14 | 25.687 |
| Average | 1.0475 | 1.0702916667 |
| Variance | 0.0064703478 | 0.0051561286 |
| ANOVA |
| Source of Variation | SS | df | MS | F | P-value | F crit |
| Sample | 0.0022550208 | 1 | 0.0022550208 | 0.3834821171 | 0.5389389507 | 4.0617064601 | (This is the row variable or gender.) |
| Columns | 0.0062335208 | 1 | 0.0062335208 | 1.0600539609 | 0.3088295633 | 4.0617064601 | (This is the column variable or Degree.) |
| Interaction | 0.0064171875 | 1 | 0.0064171875 | 1.0912877664 | 0.3018915062 | 4.0617064601 |
| Within | 0.25873675 | 44 | 0.0058803807 |
| Total | 0.2736424792 | 47 |
| Interpretation: |
| For Ho: Average compas by gender are equal | Ha: Average compas by gender are not equal |
| What is the p-value: |
| Is P-value < 0.05? |
| Do you reject or not reject the null hypothesis: |
| If the null hypothesis was rejected, what is the effect size value (eta squared): |
| Meaning of effect size measure: |
| For Ho: Average compas are equal for all degrees | Ha: Average compas are not equal for all grades |
| What is the p-value: |
| Is P-value < 0.05? |
| Do you reject or not reject the null hypothesis: |
| If the null hypothesis was rejected, what is the effect size value (eta squared): |
| Meaning of effect size measure: |
| For: Ho: Interaction is not significant | Ha: Interaction is significant |
| What is the p-value: |
| Is P-value < 0.05? |
| Do you reject or not reject the null hypothesis: |
| If the null hypothesis was rejected, what is the effect size value (eta squared): |
| Meaning of effect size measure: |
| What do these decisions mean in terms of our equal pay question: |
| Place data values in these columns |
| <1 point> | 4 | Many companies consider the grade midpoint to be the "market rate" - what is needed to hire a new employee. | Salary | Midpoint |
| Does the company, on average, pay its existing employees at or above the market rate? |
| Null Hypothesis: |
| Alt. Hypothesis: |
| Statistical test to use: |
| Place the cursor in B160 for test. |
| What is the p-value: |
| Is P-value < 0.05? |
| What else needs to be checked on a 1-tail in order to reject the null? |
| Do we REJ or Not reject the null? |
| If the null hypothesis was rejected, what is the effect size value: | NA |
| Meaning of effect size measure: | NA |
| Interpretation: |
| <2 points> | 5. | Using the results up thru this week, what are your conclusions about gender equal pay for equal work at this point? |
WEAONE
week_3_data_assignment.xlsx
