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Quantitative Methods and Analysis Unit 5 Ip

In: Business and Management

Submitted By Abbott
Words 910
Pages 4
Unit 5 – Regression Analysis
American InterContinental University

Abstract
In this scenario, Microsoft Excel has been utilized in order to perform a regression analysis therefore; each one has a chart in order to show the correlations in the data. However, satisfaction: overall, intrinsic, and extrinsic had been used.

Introduction An analysis has been given to employees for the benefits satisfaction and compared to three different job types such intrinsic, extrinsic, as well as the over all. However, the regression analysis that was performed had been done in excel as well as there were charts made up.
Benefits and Intrinsic Job Satisfaction
Regression output from Excel Regression Statistics
Multiple R 0.022301
R Square 0.000497
Adjusted R Square -0.0093
Standard Error 0.656922
Observations 104 ANOVA df SS MS F Significance F
Regression 1 0.021902 0.021902 0.050753 0.822209
Residual 102 44.01771 0.431546
Total 103 44.03962 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 5.270871 0.348709 15.11541 8.66E-28 4.579209 5.962532 4.579209 5.962532
X Variable 1 0.017947 0.079664 0.225284 0.822209 -0.14007 0.175959 -0.14007 0.175959 It did not want to add my 2 to the answer of 5.962532 or did it add the 9 to the answer of 0.175959
Graph

Benefits and Extrinsic Job Satisfaction
Regression output from Excel
Regression Statistics
Multiple R 0.026829
R Square 0.00072
Adjusted R Square -0.00908
Standard Error 0.467616
Observations 104 ANOVA df SS MS F Significance F
Regression 1 0.016065 0.016065 0.073469 0.786899
Residual 102 22.30384 0.218665
Total 103 22.3199 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 5.207915 0.248221 20.98096 8.66E-39 4.71557 5.700261 4.71557 5.700261
X Variable 1 0.015371 0.056707 0.271052 0.786899 -0.09711 0.127849 -0.09711 0.127849
Again the last numbers did not want to show up the first one is 1 for the answer of 5.700261 and the other number that did not show was the 9 for answer of 0.127849.
Graph

Benefits and Overall Job Satisfaction
Regression output from Excel Regression Statistics
Multiple R 0.099659
R Square 0.009932
Adjusted R Square 0.000225
Standard Error 0.627644
Observations 104 ANOVA df SS MS F Significance F
Regression 1 0.40308 0.40308 1.023209 0.314153
Residual 102 40.18154 0.393937
Total 103 40.58462 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 4.491866 0.333167 13.48232 2.13E-24 3.831031 5.152701 3.831031 5.152701
X Variable 1 0.076991 0.076113 1.011538 0.314153 -0.07398 0.227962 -0.07398 0.227962
The number of 1did not show for the upper last numbers and it should be 5.157701 and then for the bottom upper 95.0% number of the answer is 2 which should read 0.227962
Graph

Key components of the regression analysis Complete the following chart to identify key components of each regression output.
Dependent Variable Slope Y-intercept Equation r^2
Intrinsic
0.0179 5.2709 Y=0.0179x+5.2709 0.0005
Extrinsic
0.0154 5.2709 Y=0.0154x+5.2079 0.0007
Overall
0.077 4.4919 Y=0.077x+4.4919 0.0099

Similarities and Differences
One of the similarity’s is that all three intrinsic, extrinsic, and overall job satisfaction each had a positive correlation. All three of the x variables are all different.
Correlation coefficients The overall job satisfaction would be the strongest correlation since it has a great r² at 0.009932 while the benefits of extrinsic was only 0.0072 it was the lowest of them all and then you have intrinsic with an r² of 0.000497. Therefore since the job satisfaction is the biggest of all three of them and since the data has the most linear of the three (Schmidt, 2000). Perhaps the manager is able to infer an increase into the benefits that the benefits are going to have a more direct increase in the extrinsic job satisfaction as well as it can craft employees benefits packages (Inc., 2010).
Conclusion
When researchers use the correlation statistics as well as the regression they are able to disclose whether there is a connection between each set of data (Malek., S., 2013). Every now and then, the relationships might be causal as well as occasionally or even just coincidence. Utilizing a regression analysis would be helpful in determining how strong as well as what way the relationship exists. In the scenario of the data that was performed here, it seems as the strongest relationship might be that as associates have indicated that they are pretty much content with their benefits, The employees are basically content with their job. Therefore, this might be an useful tool for a manager to utilizing motivated associates and for then figuring out how many on the benefit packages in order to help improve associates morale.

References
Malek., S., (2013) Retrieved August 24, 2013 from AIU Online learning lab
Schmidt, S. R. (2000). Research methods for the digitally inclined. [general format]. Retrieved August 24, 2013 from http://www.webster.edu/~woolflm/statmethods.html

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