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Bus 308

Question Description

I’m studying for my class and need an explanation.

I need statistics help. The class is BUS 308: Statistics for Managers... I need help with the week 5 assignment. I have it in an excel spreadsheet with the data tables. Here are the questions: 

  1. Create a correlation table for the variables in our  equal_pay___student_rev_4_3__1_-2.xlsx. (Use analysis ToolPak or the StatPlus:mac LE software function Correlation.)
    1. 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.
  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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ID 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 Sal 58 27 34 66 47 76 41 23 77 22 23 60 42 24 24 47 69 36 24 34 76 57 23 50 24 24 40 75 72 49 24 28 64 28 24 23 22 56 35 25 43 Compa 1.017 0.870 1.096 1.157 0.979 1.134 1.025 1.000 1.149 0.956 1.000 1.052 1.050 1.043 1.043 1.175 1.210 1.161 1.043 1.096 1.134 1.187 1.000 1.041 1.043 1.043 1.000 1.119 1.074 1.020 1.043 0.903 1.122 0.903 1.043 1.000 0.956 0.982 1.129 1.086 1.075 Mid 57 31 31 57 48 67 40 23 67 23 23 57 40 23 23 40 57 31 23 31 67 48 23 48 23 23 40 67 67 48 23 31 57 31 23 23 23 57 31 23 40 Age 34 52 30 42 36 36 32 32 49 30 41 52 30 32 32 44 27 31 32 44 43 48 36 30 41 22 35 44 52 45 29 25 35 26 23 27 22 45 27 24 25 EES 85 80 75 100 90 70 100 90 100 80 100 95 100 90 80 90 55 80 85 70 95 65 65 75 70 95 80 95 95 90 60 95 90 80 90 75 95 95 90 90 80 SER 8 7 5 16 16 12 8 9 10 7 19 22 2 12 8 4 3 11 1 16 13 6 6 9 4 2 7 9 5 18 4 4 9 2 4 3 2 11 6 2 5 G 0 0 1 0 0 0 1 1 0 1 1 0 1 1 1 0 1 1 0 1 0 1 1 1 0 1 0 1 0 0 1 0 0 0 1 1 1 0 1 0 0 Raise 5.7 3.9 3.6 5.5 5.7 4.5 5.7 5.8 4 4.7 4.8 4.5 4.7 6 4.9 5.7 3 5.6 4.6 4.8 6.3 3.8 3.3 3.8 4 6.2 3.9 4.4 5.4 4.3 3.9 5.6 5.5 4.9 5.3 4.3 6.2 4.5 5.5 6.3 4.3 Deg 0 0 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0 1 0 1 1 0 0 0 0 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 Gen1 M M F M M M F F M F F M F F F M F F M F M F F F M F M F M M F M M M F F F M F M M Gr E B B E D F C A F A A E C A A C E B A B F D A D A A C F F D A B E B A A A E B A C 42 43 44 45 46 47 48 49 50 24 77 60 55 65 62 65 60 66 1.043 1.149 1.052 1.145 1.140 1.087 1.140 1.052 1.157 23 67 57 48 57 57 57 57 57 32 42 45 36 39 37 34 41 38 100 95 90 95 75 95 90 95 80 8 20 16 8 20 5 11 21 12 1 1 0 1 0 0 1 0 0 5.7 5.5 5.2 5.2 3.9 5.5 5.3 6.6 4.6 1 0 1 1 1 1 1 0 0 F F M F M M F M M A F E D E E E E E The ongoing question that the weekly assignments will focus on is: Are males and females paid the same for equa Note: to simplfy the analysis, we will assume that jobs within each grade comprise equal work. The column labels in the table mean: ID – Employee sample number Sal – Salary in thousands Age – Age in years EES – Appraisal rating (Employee evaluation score) SER – Years of service G – Gender (0 = male, 1 = female) Mid – salary grade midpoint Raise – percent of last raise Grade – job/pay grade Deg (0= BS\BA 1 = MS) Gen1 (Male or Female) Compa - salary divided by midpoint, a measure of salary that removes th This data should be treated as a sample of employees taken from a company that has about 1,000 employees using a random sampling approach. Mac Users: The homework in this course assumes students have Windows Excel, and can load the Analysis ToolPak into their version of Excel. The analysis tool pak has been removed from Excel for Windows, but a free third-party tool that can be used (found on an answers Microsoft site) is: http://www.analystsoft.com/en/products/statplusmacle Like the Microsoft site, I make cannot guarantee the program, but do know that Statplus is a respected statistical package. You may use other approaches or tools as desired to complete the assignments. nd females paid the same for equal work (under the Equal Pay Act)? Week 1. Describing the data. e .05 for your significance level in making your decisions. the calculations you performed. o the overall sample mean. ts suggest about the population means for male and female salaries? Ho) is listed as the same value for every corresponding value in the data set. le Assuming Unequal Variances 1 sample t in this situation Ho 45 0 25 n male and female salaries could be equal to each other. on be equal to each other? (Another 2-sample t-test.) salary equity between the genders? Why? lts about male and female salary equality, ry equity? Why? Week 3 Testing multiple means with ANOVA Use .05 for your significance level in making your decisions. or the calculations you performed. be the same for each of the grade levels? (Assume equal variance, and use the analysis toolpak function ANOVA.) or each grade under the appropriate grade label. cal test and result. ume equal variances for all grades. on. Please interpret the results. The salary values were randomly picked for each cell. Note: a number with an E after it (E9 or E-6, for example) means we move the decimal point that number of places. For example, 1.2E4 becomes 12000; while 4.56E-5 becomes 0.0000456 lusions mean about the population values being tested? on are equal by grade and/or gender, and are independent of each factor? the statistical test and result. he completed table above. The results should look something like those in question 2. mple) equal? For each grade? Do grade and gender interaction impact compa values? VA without replication. Why did you pick this variable and what do the results show? along with the statistical test and result. Hint: use mean values in the boxes. qual pay for equal work at this point? Week 4 Confidence Intervals and Chi Square (Chs 11 - 12) Let's look at some other factors that migh For question 3 below, be sure to list the null and alternate hypothesis statements. Use .05 for your significance level For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you perf One question we might have is if the distribution of graduate and undergraduate degrees indep (Note: this is the same as asking if the degrees are distributed the same way.) Based on the analysis of our sample data (shown below), what is your answer? Ho: The populaton correlation between grade and degree is 0. Ha: The population correlation between grade and degree is > 0 Perform analysis: A B C D E F Total OBSERVED 7 5 3 2 5 3 25 COUNT - M or 0 8 2 2 3 7 3 25 COUNT - F or 1 15 7 5 5 12 6 50 total EXPECTED 7.5 3.5 2.5 2.5 6 3 25 7.5 3.5 2.5 2.5 6 3 25 15 7 5 5 12 6 50 1 By using either the Excel Chi Square functions or calculating the results directly as the text sho reject or not reject the null hypothesis? What does your conclusion mean? Interpretation: Using our sample data, we can construct a 95% confidence interval for the population's mean s Interpret the results. How do they compare with the findings in the week 2 one sample t-test ou Males Mean St error Low to High 52 3.65878 44.4483 59.5517 Females 38 3.62275 30.5226 45.4774 directly as the text shows, do we e population's mean salary for each gender. 2 one sample t-test outcomes (Question 1)? Results are mean +/-2.064*standard error 2.064 is t value for 95% interval e square root of the sample size.> stributed across grades in a similar pattern within the population? ation's mean service difference for each gender. gs in week 2, question 2? y for equal work? m analysis below any question> Week 5 Correlation and Regression For each question involving a statistical test below, list the null and alternate hypothesis statements. Use .05 for your For full credit, you need to also show the statistical outcomes - either the Excel test result or the calculations you perf 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 e 2 Below is a regression analysis for salary being predicted/explained by the other variables in our samp 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 SUMMARY OUTPUT The analysis used Sal as the y (dependent variable) and 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 ANOVA df Regression Residual Total SS MS F Significance F 7 17783.66 2540.522 377.9139 8.44043E-36 42 282.3445 6.722489 49 18066 Coefficients Intercept -4.009 Mid 1.220 Age 0.029 EES -0.096 SR -0.074 G 2.552 Raise 0.834 Deg 1.002 Standard Error 3.775 0.030 0.067 0.047 0.084 0.847 0.643 0.744 t Stat -1.062 40.674 0.439 -2.020 -0.876 3.012 1.299 1.347 P-value 0.294 0.000 0.663 0.050 0.386 0.004 0.201 0.185 Interpretation: Do you reject or not reject the regression null hypothesis? Lower 95% -11.627 1.159 -0.105 -0.191 -0.244 0.842 -0.462 -0.500 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 q 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 w 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 equal What outcomes in your life or work might benefit from a multiple regression examination rather than ypothesis statements. Use .05 for your significance level in making your decisions. l test result or the calculations you performed. e analysis ToolPak function Correlation.) n seeing if we pay males and females equally for equal work? ined by the other variables in our sample (Mid, compa are different ways of used in the same regression.) e y (dependent variable) and and deg as the dependent Upper 95% Lower 95.0% Upper 95.0% 3.609 -11.627 3.609 1.280 1.159 1.280 0.164 -0.105 0.164 0.000 -0.191 0.000 0.096 -0.244 0.096 4.261 0.842 4.261 2.131 -0.462 2.131 2.504 -0.500 2.504 ...
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