| Week 4 | Confidence Intervals and Chi Square (Chs 11 - 12) | | | | | Let's look at some other factors that might influence pay. | | | | | | | | | Q1 | | | Q2 | | | | | | <Note: use right click on row numbers to insert rows to perform analysis below any question> |
| For question 3 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions. | | | | | | | | | | | | | | | Gr | Deg | | Gen1 | Sal |
| For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed. | | | | | | | | | | | | | | | A | 0 | | F | 34 |
| 1 | One question we might have is if the distribution of graduate and undergraduate degrees independent of the grade the employee? | | | | | | | | | | | | | | A | 0 | | F | 41 |
| | (Note: this is the same as asking if the degrees are distributed the same way.) |
| | Based on the analysis of our sample data (shown below), what is your answer? |
| | Ho: The populaton correlation between grade and degree is 0. | | | | | | | | | | | | | | C | 0 | | F | 77 |
| | Ha: The population correlation between grade and degree is > 0 |
| | Perform analysis: |
| OBSERVED | A | B | C | D | E | F | Total |
| COUNT - M or 0 | 7 | 5 | 3 | 2 | 5 | 3 | 25 |
| COUNT - F or 1 | 8 | 2 | 2 | 3 | 7 | 3 | 25 |
| total | 15 | 7 | 5 | 5 | 12 | 6 | 50 |
| EXPECTED |
| | 7.5 | 3.5 | 2.5 | 2.5 | 6 | 3 | 25 | | | | | | | | | | | | | | <Highlighting each cell with show how the value |
| | 7.5 | 3.5 | 2.5 | 2.5 | 6 | 3 | 25 | | | | | | | | | | | | | | is found: row total times column total divided by |
| | 15 | 7 | 5 | 5 | 12 | 6 | 50 | | | | | | | | | | | | | | grand total.> |
| | By using either the Excel Chi Square functions or calculating the results directly as the text shows, do we |
| | reject or not reject the null hypothesis? What does your conclusion mean? |
| Interpretation: |
| 2 | Using our sample data, we can construct a 95% confidence interval for the population's mean salary for each gender. |
| | Interpret the results. How do they compare with the findings in the week 2 one sample t-test outcomes (Question 1)? |
| | Males | Mean | St error | | | Low | to | High |
| | | 52 | 3.6587793957 | | | 44.4482793272 | | 59.5517206728 | | | | | | | | | | | | | | Results are mean +/-2.064*standard error |
| | Females | 38 | 3.6227541769 | | | 30.5226353789 | | 45.4773646211 | | | | | | | | | | | | | | 2.064 is t value for 95% interval |
| | <Reminder: standard error is the sample standard deviation divided by the square root of the sample size.> |
| Interpretation: |
| | | | | | | | | | | | | | | | C | 0 | | F | 55 |
| | | | | | | | | | | | | | | | D | 1 | | M | 77 |
| 3 | Based on our sample data, can we conclude that males and females are distributed across grades in a similar pattern within the population? | | | | | | | | | | | | | | D | 1 | | M | 60 |
| 4 | Using our sample data, construct a 95% confidence interval for the population's mean salary difference for each gender. |
| | Do they intersect or overlap? How do these results compare to the findings in week 2, question 2? |
| 5 | How do you interpret these results in light of our question about equal pay for equal work? |
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bus_308_week_4_assignment.xlsx
