Week 4Confidence Intervals and Chi Square (Chs 11 - 12)For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05For full credit, you need to also show the statistical outcomes - either the Excel test result or the 1Using our sample data, construct a 95% confidence interval for the popInterpret the results. How do they compare with the findings in the weMeanSt error t valueLow to Males52 3.555278 2.06389944.66227Females38 3.658779 2.06389930.44865<Reminder: standard error is the sample standard deviation divided by Interpretation:Population mean salary for males will lie between 44.66 to 59.34 while2Using our sample data, construct a 95% confidence interval for the meaHow does this compare to the findings in week 2, question 2?Difference St Err.T valueLow to 145.12.0106353.745763Yes/NoCan the means be equal?NoWhy?0 is not included in tHow does this compare to the week 2, question 2 result (2 sampe t-testa. Why is using a two sample tool (t-test, confidence interval) a better choConfidence interval is a better choice because it gives us the result in l3
Week 5 Correlation and Regression Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function Correlation.) a. Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table (which is what Excel produces)? b. Place table here (C8 in Output range box): Salary Compa Midpoint Age Performance Rating Service Raise Salary 1 Compa 0.61647174 1 Midpoint 0.98897178 0.5006577 1 Age 0.54357969 0.195218 0.567111 1 Performance Rating 0.15130696-0.101271 0.191751 0.1392384 1 Service 0.45170496 0.1820746 0.471147 0.5651332 0.2257007594 1 Raise-0.04142104-0.042731 -0.028913-0.180427 0.673659763 0.1027869 1 c. Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, what variables are significantly related to Salary? Compa, Midpoint, Age, Service To compa? Salary, Midpoint d. Looking at the above correlations - both significant or not - are there any surprises -by that I mean any relationships you expected to be meaningful and are not and vice-versa? I expected a strong positive correlation between Performance Rating and Salary, but the correlation between the two variables ended up being weak. It was surprising that the correlation between Performance Rating and Compa was negative. I also expected the variable Raise to have positive correlations with Salary, Compa, Midpoint and Age, but Raise has negative correlations with all these variables. e. Does this help us answer our equal pay for equal work question? 2 Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint, age, performance rating, service, gender, and degree variables. (Note: since salary and compa are different ways of expressing an employee’s salary, we do not want to have both used in the same regression.) Plase interpret the findings. Ho: The regression equation is not significant. Ha: The regression equation is significant. Ho: The regression coefficient for each variable is not significant Note: technically we have one for each input variable. Ha: The regression coefficient for each variable is significant Listing it this way to save space.