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Can anyone revise  the attached document below to avoid plagiarism and update up current information and references. I willing to pay more for a good document


Running head: BUSN311 ? Regression Analysis 1 Unit 5 Individual Project

 

BUSN311-1301B-04

 

Natasha Rumph

 

American Intercontinental University Online BUSN311 ? Regression Analysis 2

 

Abstract This paper will analyze employee benefits compared to intrinsic, extrinsic, and overall job

 

satisfaction. It will use calculations from Microsoft Excel to determine regression analysis and

 

charts to graph the correlations so that it could be read easily. BUSN311 ? Regression Analysis 3 Linear Regression

 

Regression Statistics

 

R

 

R Square

 

Adjusted R Square

 

Standard Error

 

Total Number Of Cases 0.01831

 

0.00034

 

-0.00987

 

0.39152

 

100

 

Intrinsic = 5.0749 + 0.0060 * Benefits ANOVA

 

d.f.

 

Regression

 

Residual

 

Total Intercept 1.

 

98.

 

99. SS

 

0.00504

 

15.02246

 

15.0275 MS

 

0.00504

 

0.15329 F

 

0.03288 p-level

 

0.85648 Coefficients

 

5.07495 Standard Error

 

0.17029 LCL

 

4.73702

 

0.05956 UCL

 

5.41288 t Stat

 

29.80201 p-level

 

0.E+0 H0 (5%) rejecte

 

Yes 0.07154 0.18134 0.85648 No Benefits

 

0.00599

 

T (5%)

 

1.98447

 

LCL - Lower value of a reliable interval (LCL)

 

UCL - Upper value of a reliable interval (UCL) Introduction 0.03303 BUSN311 ? Regression Analysis 4 Correlation statistics and regression analysis is used to determine if there is a relationship

 

between two variables. Correlations can be either positive or negative. The regressions analysis

 

calculates how strong the relationships are between the variables. Charts and analysis have been

 

used to determine the results.

 

Benefits and Intrinsic Job Satisfaction

 

Regression output from Excel Graph Linear Regression

 

Regression Statistics

 

R

 

R Square

 

Adjusted R Square

 

Standard Error

 

Total Number Of Cases 0.62825

 

0.3947

 

0.38853

 

0.34533

 

100

 

Extrinsic = 6.5035 - 0.2329 * Benefits BUSN311 ? Regression Analysis 5 ANOVA

 

d.f.

 

Regression

 

Residual

 

Total Intercept SS

 

1.

 

98.

 

99. 7.62071

 

11.68679

 

19.3075 Coefficients

 

6.50351 Standard

 

Error

 

0.1502 Benefits -0.23291

 

T (5%)

 

1.98447

 

LCL - Lower value of a reliable interval (LCL)

 

UCL - Upper value of a reliable interval (UCL) Benefits and Extrinsic Job Satisfaction

 

Regression output from Excel Graph 0.02914 MS F 7.62071

 

0.11925 63.90373 p-level

 

2.61302E12 LCL

 

6.20545

 

0.29073 UCL

 

6.80157 t Stat

 

43.29969 -0.17509 -7.99398 p-level

 

0.E+0

 

2.61302E12 H0 (5%) re

 

Yes

 

Yes BUSN311 ? Regression Analysis 6

 

Linear Regression Regression Statistics

 

R

 

R Square

 

Adjusted R Square

 

Standard Error

 

Total Number Of Cases 0.15949

 

0.02544

 

0.01549

 

0.66619

 

100

 

Job Satisfaction = 5.4970 - 0.0899 * Benefits ANOVA

 

d.f.

 

1.

 

98.

 

99. SS

 

1.13518

 

43.49322

 

44.6284 MS

 

1.13518

 

0.44381 F

 

2.55782 p-level

 

0.11297 Coefficients

 

5.49699 Standard Error

 

0.28975 LCL

 

4.92199

 

0.20143 UCL

 

6.07199 t Stat

 

18.97137 p-level

 

0.E+0 H0 (5%) rejected

 

Yes 0.02165 -1.59932 0.11297 No Regression

 

Residual

 

Total Intercept Benefits

 

-0.08989

 

T (5%)

 

1.98447

 

LCL - Lower value of a reliable interval (LCL)

 

UCL - Upper value of a reliable interval (UCL) Benefits and Overall Job Satisfaction

 

Regression output from Excel Graph 0.05621 BUSN311 ? Regression Analysis 7 Key components of the regression analysis

 

Complete the following chart to identify key components of each regression output.

 

Dependent Variable

 

Intrinsic Slope

 

0.006 Y-intercept

 

5.0749 Equation

 

y=0.006x+5.0749 0.00034 Extrinsic -0.2329 6.5035 y=-0.2329x+6.5035 0.3947 Overall -0.0899 5.497 y=-0.0899x+5.497 0.02544 Similarities and Differences

 

The intrinsic job satisfaction variable was the only variable to present a positive correlation

 

and slope. The extrinsic job satisfaction and overall job satisfaction variables presented negative

 

correlations and slopes (Editorial Board, 2012).

 

Correlation coefficients

 

The extrinsic job satisfaction variable presented the strongest correlation at r2 =0.3947.

 

Correlation coefficients can be between -1 and 1 and whichever variable is closer to -1 or 1 is the

 

stronger correlation. It does not matter whether the correlation coefficient is negative or positive.

 

The reason for this is because with positive correlation as one variable increases, so will the BUSN311 ? Regression Analysis 8 other. As far as negative correlation, as one increases the other decreases (Editorial Board, 2012).

 

What this means to the manager is that as the benefits increases the extrinsic job satisfaction

 

would decrease. This could help the manager to identify where he or she needs to focus to put

 

together an employee benefits package.

 

Conclusion

 

Connections two variables can be determined by using correlations and regression. The use

 

of regression can determine how strong the relationship is between the two sets of data. After

 

analyzing the three sets of data it can be determined that employees are more extrinsically

 

satisfied with their job than any other factor. BUSN311 ? Regression Analysis 9 References

 

Editorial Board. (2012). Elementary Statistics (2). Schaumburg, IL: Words of Wisdom. Retrieved

 

from http://wow.coursesmart.com/9781934920657/firstsection#

 


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