BUS 308 WEEK 5 ASSIGNMENT..BUS308.Online.2014.5.29.bh.Employee_Salary_Data_Set.xls
Question # 00023998
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Updated on: 08/23/2014 11:42 PM Due on: 12/31/2014
Create a correlation table for the variables in our Employee Salary Data Set. (Use analysis ToolPak or StatPlus:mac LE function Correlation).
Reviewing the data levels from week 1, what variables can be used in a Pearson’s Correlation Table (which is what Excel produces)?
Place the table here.
Using r= approximately .28 as the significant r value (at p = .05) for a correlation between 50 values, what variables are significantly related to salary? To compa?
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?
Does this information help us answer our equal pay for equal work question?
Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint, age, performance rating, service, raise, 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. Please interpret the findings.
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.
Based on all of your results to date, is gender a factor in the pay practices of this company? If so, which gender gets paid more? How do we know? Which is the best variable to use in analyzing pay practices - salary or compa? Why? What is the most interesting or surprising thing about the results we got doing the analyses during the last 5 weeks?
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?
Week 5 Correlation and Regression
1.
Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function a.
Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table b. Place table here (C8 in Output range box):
c.
Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, significantly related to Salary?
To compa?
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?
e.
Does this help us answer our equal pay for equal work question?
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample age, performance rating, service, gender, and degree variables. (Note: since salary and compa are 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
Note: technically we have Ha: The regression coefficient for each variable is significant
Listing it this way to save 1. Create a correlation table for the variables in our Employee Salary Data Set. (Use analysis Sal
a. Reviewing the data levels from week 1, what variables can be used in a Pearson’s Correlation SUMMARY OUTPUT
b. Place the table here.
c. Using r= approximately .28 as the significant r value (at p = .05) for a correlation between Regression Statistics
d. Looking at the above correlations – both significant or not – are there any surprises – by Multiple R
e. Does this information help us answer our equal pay for equal work question?
R Square
2. Below is a regression analysis for salary being predicted/explained by the other variables in Adjusted R Square
3. Perform a regression analysis using compa as the dependent variable and the same independent Standard Error
Note: be sure to include the appropriate hypothesis statements.
Observations
4. Based on all of your results to date, is gender a factor in the pay practices of this company? 5. Why did the single factor tests and analysis (such as t and single factor ANOVA tests on salary ANOVA
df
SS
MS
F
Significance F
Regression
6
17762.3
2960.38
419.1516
1.812E-36
Residual
43
303.7003
7.0628
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-1.7496212
3.618368
-0.48354
0.631166
-9.046755
5.5475126
Midpoint
1.21670105
0.031902
38.1383
8.66E-35
1.1523638
1.2810383
Age
-0.004628
0.065197
-0.07098
0.943739
-0.136111
0.1268547
Performace Rating
-0.0565964
0.034495
-1.64071
0.108153
-0.126162
0.0129695
Service
-0.0425004
0.084337
-0.50394
0.616879
-0.212582
0.1275814
Gender
2.42033721
0.860844
2.81159
0.007397
0.6842792
4.1563952
Degree
0.27553341
0.799802
0.3445
0.732148
-1.337422
1.8884885
Note: since Gender and Degree are expressed as 0 and 1, they are considered dummy variables and 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:
Intercept
Midpoint
Age
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?
Salary =
Is gender a significant factor in salary:
If so, who gets paid more with all other things being equal?
How do we know?
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 Note: be sure to include the appropriate hypothesis statements.
Regression hypotheses
Ho:
Ha:
Coefficient hypotheses (one to stand for all the separate variables)
Ho:
Ha:
Put C94 in output range box
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:
Intercept
Midpoint
Age
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?
Compa =
Is gender a significant factor in compa:
If so, who gets paid more with all other things being equal?
How do we know?
4
Based on all of your results to date, do we have an answer to the question of are males and females If so, which gender gets paid more?
How do we know?
Which is the best variable to use in analyzing pay practices - salary or compa? Why?
What is most interesting or surprising about the results we got doing the analysis during the last 5 5
Why did the single factor tests and analysis (such as t and single factor ANOVA tests on salary equality) What outcomes in your life or work might benefit from a multiple regression examination rather
ToolPak or StatPlus:mac LE function Correlation.)Pearson's Correlation table (which is what Excel produces)?correlation between 50 values, what variables areLooking surprises -by that I the other variables in our sample (Midpoint, since salary and compa are different ways of the same regression.) Note: technically we have one for each input variable. Listing it this way to save space.Salary Data Set. (Use analysis ToolPak or StatPlus:mac LE function Correlation). used in a Pearson’s Correlation Table (which is what Excel produces)? .05) for a correlation between 50 values, what variables are significantly related to salary? To compa?are there any surprises – by that I mean any relationships you expected to be meaningful and are not, and vice-versa?work question? explained by the other variables in our sample (Midpoint, age, performance rating, service, raise, and degree variables). Note: since salary and compa variable and the same independent variables as used in question 2. Show the result, and interpret your findings by answering the same questions.
pay practices of this company? If so, which gender gets paid more? How do we know? Which is the best variable to use in analyzing pay practices single factor ANOVA tests on salary equality) not provide a complete answer to our salary equality question? What outcomes in your life or work Lower 95.0%
Upper 95.0%
-9.046755043
5.547512618
1.152363828
1.281038273
-0.136110719
0.126854699
-0.126162375
0.012969494
-0.212582091
0.127581377
0.684279192
4.156395232
-1.337421655
1.888488483
considered dummy variables and can be used in a multiple regression equation.Perf. Rat.
Service
Gender
Degree
the same independentvariables findings by answering the same questions.
Perf. Rat.
Service
Gender
Degree
question of are males and females paid equally for equal work?compa? Why?analysis during the last 5 weeks?ANOVA tests on salary equality) not provide a complete answer to our salary equality question?regression examination rather than a simpler one variable test?
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. answering the same questions.
use in analyzing pay practices - salary or compa? Why? What is the most interesting or surprising thing about the results we got doing the analyses outcomes in your life or work might benefit from a multiple regression examination rather than a simpler one variable test?
Reviewing the data levels from week 1, what variables can be used in a Pearson’s Correlation Table (which is what Excel produces)?
Place the table here.
Using r= approximately .28 as the significant r value (at p = .05) for a correlation between 50 values, what variables are significantly related to salary? To compa?
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?
Does this information help us answer our equal pay for equal work question?
Below is a regression analysis for salary being predicted/explained by the other variables in our sample (Midpoint, age, performance rating, service, raise, 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. Please interpret the findings.
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.
Based on all of your results to date, is gender a factor in the pay practices of this company? If so, which gender gets paid more? How do we know? Which is the best variable to use in analyzing pay practices - salary or compa? Why? What is the most interesting or surprising thing about the results we got doing the analyses during the last 5 weeks?
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?
Week 5 Correlation and Regression
1.
Create a correlation table for the variables in our data set. (Use analysis ToolPak or StatPlus:mac LE function a.
Reviewing the data levels from week 1, what variables can be used in a Pearson's Correlation table b. Place table here (C8 in Output range box):
c.
Using r = approximately .28 as the signicant r value (at p = 0.05) for a correlation between 50 values, significantly related to Salary?
To compa?
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?
e.
Does this help us answer our equal pay for equal work question?
2
Below is a regression analysis for salary being predicted/explained by the other variables in our sample age, performance rating, service, gender, and degree variables. (Note: since salary and compa are 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
Note: technically we have Ha: The regression coefficient for each variable is significant
Listing it this way to save 1. Create a correlation table for the variables in our Employee Salary Data Set. (Use analysis Sal
a. Reviewing the data levels from week 1, what variables can be used in a Pearson’s Correlation SUMMARY OUTPUT
b. Place the table here.
c. Using r= approximately .28 as the significant r value (at p = .05) for a correlation between Regression Statistics
d. Looking at the above correlations – both significant or not – are there any surprises – by Multiple R
e. Does this information help us answer our equal pay for equal work question?
R Square
2. Below is a regression analysis for salary being predicted/explained by the other variables in Adjusted R Square
3. Perform a regression analysis using compa as the dependent variable and the same independent Standard Error
Note: be sure to include the appropriate hypothesis statements.
Observations
4. Based on all of your results to date, is gender a factor in the pay practices of this company? 5. Why did the single factor tests and analysis (such as t and single factor ANOVA tests on salary ANOVA
df
SS
MS
F
Significance F
Regression
6
17762.3
2960.38
419.1516
1.812E-36
Residual
43
303.7003
7.0628
Total
49
18066
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Intercept
-1.7496212
3.618368
-0.48354
0.631166
-9.046755
5.5475126
Midpoint
1.21670105
0.031902
38.1383
8.66E-35
1.1523638
1.2810383
Age
-0.004628
0.065197
-0.07098
0.943739
-0.136111
0.1268547
Performace Rating
-0.0565964
0.034495
-1.64071
0.108153
-0.126162
0.0129695
Service
-0.0425004
0.084337
-0.50394
0.616879
-0.212582
0.1275814
Gender
2.42033721
0.860844
2.81159
0.007397
0.6842792
4.1563952
Degree
0.27553341
0.799802
0.3445
0.732148
-1.337422
1.8884885
Note: since Gender and Degree are expressed as 0 and 1, they are considered dummy variables and 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:
Intercept
Midpoint
Age
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?
Salary =
Is gender a significant factor in salary:
If so, who gets paid more with all other things being equal?
How do we know?
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 Note: be sure to include the appropriate hypothesis statements.
Regression hypotheses
Ho:
Ha:
Coefficient hypotheses (one to stand for all the separate variables)
Ho:
Ha:
Put C94 in output range box
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:
Intercept
Midpoint
Age
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?
Compa =
Is gender a significant factor in compa:
If so, who gets paid more with all other things being equal?
How do we know?
4
Based on all of your results to date, do we have an answer to the question of are males and females If so, which gender gets paid more?
How do we know?
Which is the best variable to use in analyzing pay practices - salary or compa? Why?
What is most interesting or surprising about the results we got doing the analysis during the last 5 5
Why did the single factor tests and analysis (such as t and single factor ANOVA tests on salary equality) What outcomes in your life or work might benefit from a multiple regression examination rather
ToolPak or StatPlus:mac LE function Correlation.)Pearson's Correlation table (which is what Excel produces)?correlation between 50 values, what variables areLooking surprises -by that I the other variables in our sample (Midpoint, since salary and compa are different ways of the same regression.) Note: technically we have one for each input variable. Listing it this way to save space.Salary Data Set. (Use analysis ToolPak or StatPlus:mac LE function Correlation). used in a Pearson’s Correlation Table (which is what Excel produces)? .05) for a correlation between 50 values, what variables are significantly related to salary? To compa?are there any surprises – by that I mean any relationships you expected to be meaningful and are not, and vice-versa?work question? explained by the other variables in our sample (Midpoint, age, performance rating, service, raise, and degree variables). Note: since salary and compa variable and the same independent variables as used in question 2. Show the result, and interpret your findings by answering the same questions.
pay practices of this company? If so, which gender gets paid more? How do we know? Which is the best variable to use in analyzing pay practices single factor ANOVA tests on salary equality) not provide a complete answer to our salary equality question? What outcomes in your life or work Lower 95.0%
Upper 95.0%
-9.046755043
5.547512618
1.152363828
1.281038273
-0.136110719
0.126854699
-0.126162375
0.012969494
-0.212582091
0.127581377
0.684279192
4.156395232
-1.337421655
1.888488483
considered dummy variables and can be used in a multiple regression equation.Perf. Rat.
Service
Gender
Degree
the same independentvariables findings by answering the same questions.
Perf. Rat.
Service
Gender
Degree
question of are males and females paid equally for equal work?compa? Why?analysis during the last 5 weeks?ANOVA tests on salary equality) not provide a complete answer to our salary equality question?regression examination rather than a simpler one variable test?
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. answering the same questions.
use in analyzing pay practices - salary or compa? Why? What is the most interesting or surprising thing about the results we got doing the analyses outcomes in your life or work might benefit from a multiple regression examination rather than a simpler one variable test?
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Rating:
/5
Solution: BUS 308 WEEK 5 ASSIGNMENT..BUS308.Online.2014.5.29.bh.Employee_Salary_Data_Set.xls