1Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.)      a. Interpret the results.  What…

1 Create a correlation table for the variables in our data set. (Use analysis ToolPak function Correlation.)          
  a. Interpret the results.  What variables seem to be important in seeing if we pay males and females equally for equal work?        
2 Below is a regression analysis for salary being predicted/explained by the other variables in our sample  (Mid,          
   age, ees, sr, raise, and deg 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.)            
  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                
  Sal     The analysis used Sal as the y (dependent variable) and              
  SUMMARY OUTPUT   mid, age, ees, sr, g, raise, and deg as the dependent               
        variables (entered as a range).                
  Regression Statistics                        
  Multiple R 0.99215498                        
  R Square 0.9843715                        
  Adjusted R Square 0.98176675                        
  Standard Error 2.59277631                        
  Observations 50                        
    df SS MS F Significance F                
  Regression 7 17783.7 2540.52 377.914 8.44043E-36                
  Residual 42 282.345 6.72249                    
  Total 49 18066                      
    Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%          
  Intercept -4.009 3.775 -1.062 0.294 -11.627 3.609 -11.627 3.609          
  Mid 1.220 0.030 40.674 0.000 1.159 1.280 1.159 1.280          
  Age 0.029 0.067 0.439 0.663 -0.105 0.164 -0.105 0.164          
  EES -0.096 0.047 -2.020 0.050 -0.191 0.000 -0.191 0.000          
  SR -0.074 0.084 -0.876 0.386 -0.244 0.096 -0.244 0.096          
  G 2.552 0.847 3.012 0.004 0.842 4.261 0.842 4.261          
  Raise 0.834 0.643 1.299 0.201 -0.462 2.131 -0.462 2.131          
  Deg 1.002 0.744 1.347 0.185 -0.500 2.504 -0.500 2.504          
Interpretation:  Do you reject or not reject the regression null hypothesis?                
  Do you reject or not reject the null hypothesis for each variable?                
  What is the regression equation, using only significant variables if any exist?              
  What does result tell us about equal pay for equal work for males and females?              
3 Perform a regression analysis using compa as the dependent variable and the same independent            
  variables as used in question 2.  Show the result, and interpret your findings by answering the same questions.          
  Note: be sure to include the appropriate hypothesis statements.                
4 Based on all of your results to date, is gender a factor in the pay practices of this company?  Why or why not?          
  Which is the best variable to use in analyzing pay practices – salary or compa?  Why?              
5 Why did the single factor tests and analysis (such as t and single factor ANOVA tests on salary equality) not provide a complete answer to our salary equality question?
  What outcomes in your life or work might benefit from a multiple regression examination rather than a simpler one variable test?        


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