Create A Correlation Table For The Variables In O

Create A Correlation Table For The Variables In O

Create a correlation table for the variables in our Employee Salary Data Set. Using analysis ToolPak or StatPlus:mac LE function Correlation, review the data levels from week 1, and determine which variables can be used in a Pearson’s Correlation Table (which is what Excel produces). Place the correlation table here. Using r = approximately 0.28 as the significant correlation coefficient (at p = 0.05) for 50 values, identify which variables are significantly related to salary.

Review the correlations—both significant and not— and analyze whether there are any surprising relationships. Consider whether these findings help in addressing the question of equal pay for equal work. Next, interpret a provided regression analysis predicting salary based on Midpoint, age, performance rating, service, raise, and degree variables. Remember to exclude compa from this regression, as it is a different measure of salary.

Perform a regression analysis with compa as the dependent variable and the same independent variables used previously. Show and interpret the results, including hypotheses testing. Based on your analyses, assess whether gender influences the company's pay practices, and identify which gender is paid more. Justify your conclusion with appropriate reasoning.

Evaluate whether salary or compa serves as the better variable for analyzing pay practices, providing a rationale for your choice. Reflect on the most interesting or surprising aspect of your results over the past five weeks. Explain why single-factor tests (such as t-tests and ANOVA) did not fully answer the question of salary equality.

Finally, consider how a multiple regression approach might benefit the analysis of your own life or work scenarios compared to single-variable tests.