Week 4 Confidence Intervals and Chi Square (Chs 11 - 12) For questions 3 and 4 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level in making your decisions. For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you performed. 1 Using our sample data, 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)? Mean St error t value Low to High Males 52 3.555278 2.063899 44.66227 59.3377326 Females 38 3.658779 2.063899 30.44865 45.5513495 <Reminder: standard error is the sample standard deviation divided by the square root of the sample size.> Interpretation: Population mean salary for males will lie between 44.66 to 59.34 while for females will lie between 30.45 and 45.55. 2 Using our sample data, construct a 95% confidence interval for the mean salary difference between the genders in the population. How does this compare to the findings in week 2, question 2?
Score: Week 5 Correlation and Regression <1 point> 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)? Interval/Ratio variable such as salary, compa, midpoint, age, performance rating, service, and raisecan be used in a Pearson's Correlatio b. Place table here (C8): Salary Compa Midpoint Age Performance Rating Service Raise Salary 1 Compa 0.616471740 1 Midpoint 0.988971783 0.500657748 1 Age 0.543579688 0.195218020 0.567110664 1 Performance Reating 0.151306964 -0.101270864 0.191750769 0.139238407 1 Service 0.451704959 0.182074620 0.471146700 0.565133209 0.225700759 1 Raise-0.041421039 -0.042731275 -0.028913405 -0.180426853 0.673659763 0.102786900 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, and Service To compa? Salary and 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 thought that the raise variable correlations to salary, compa, and midpoint would be positive. e. Does this help us answer our equal pay for equal work question? No this does not help us answer the equal pay for equal work question when it comes to gender The gender variable is insignificant and cannot be used in this matrix <1 point> 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.