### 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 Ha: The regression coefficient for each variable is significant Note: technically we have one for each input variable. Listing it this way to save space. Sal SUMMARY OUTPUT Regression Statistics Multiple R 0.99155907 R Square 0.9831894 Adjusted R Square 0.98084373 Standard Error 2.65759257 Observations 50 ANOVA df Regression Residual Total SS MS F Significance F 6 17762.2997 2960.38328 419.151611 1.8122E-036 43 303.700326 7.06279828 49 18066 Standard Coefficients Error t Stat P-value Lower 95% Upper 95% Lower 95.0%Upper 95.0% Intercept -1.74962121 3.61836766 -0.48353882 0.63116649 -9.04675504 5.54751262 -9.04675504 5.54751262 Midpoint 1.21670105 0.03190235 38.1382881 8.6642E-035 1.15236383 1.28103827 1.15236383 1.28103827 Age -0.00462801 0.06519721 -0.07098479 0.94373899 -0.13611072 0.1268547 -0.13611072 0.1268547 Performace Rating -0.05659644 0.03449507 -1.6407111 0.10815318 -0.12616237 0.01296949 -0.12616237 0.01296949 Service -0.04250036 0.08433698 -0.503935 0.61687935 -0.21258209 0.12758138 -0.21258209 0.12758138 Gender 2.42033721 0.86084432 2.81158528 0.00739662 0.68427919 4.15639523 0.68427919 4.15639523 Degree 0.27553341 0.7998023 0.3445019 0.732148119 -1.33742165 1.88848848 -1.33742165 1.88848848 Note: since Gender and Degree are expressed as 0 and 1, they are considered dummy variables and can be used in a multiple regression equation. Interpretation: For the Regression as a whole: What is the value of the F statistic: What is the p-value associated with this value: Is the p-value <0.05? Do you reject or not reject the null hypothesis: What does this decision mean for our equal pay question: For each of the coefficients: What is the coefficient's p-value for each of the variables: Is the p-value < 0.05? Do you reject or not reject each null hypothesis: What are the coefficients for the significant variables? Using only the significant variables, what is the equation? Is gender a significant factor in salary: If so, who gets paid more with all other things being equal? How do we know? Intercept Salary = Midpoint Age Perf. Rat. Service Gender Degree