Week 3 ANOVA and Paired T-test (BUS308 Week 3 Data)

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.1Last 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.ABCDENull Hypothesis:Alt. Hypothesis:Place B17 in Outcome range box.FInterpretation:What is the p-value:Is P-value < 0.05?Do we REJ is Not reject the null?If the null hypothesis was rejected, what or the effect size value(eta squared):Meaning of effect size measure:What does that decision mean in terms of our equal pay question: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:Alt. Hypothesis:If desired, place salaries per grade in these columnsABCDEFPlace B55 in Outcome range box.What is the p-value:Is P-value < 0.05?Do you reject or not reject the effect size valueIf the null hypothesis was rejected, what is the null hypothesis:(eta squared):Meaning of effect size measure:Interpretation:FULL LIST OF QUESTIONS ATTACHED BELOW: