Running head: BUSN311 - Quantitative Methods and Analysis 1
Unit 5 –Regression Analysis
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American InterContinental University
BUSN311 - Quantitative Methods and Analysis 1
Abstract
This paper analyzed employee benefits satisfaction as compared to three types of job satisfaction: intrinsic, extrinsic and overall. Microsoft Excel was used to perform regression analysis and to make charts of the possible correlations of the data.
Introduction
This paper analyzed employee benefits satisfaction as compared to three types of job satisfaction: intrinsic, extrinsic and overall. Microsoft Excel was used to perform the regression analyses and create the charts that are presented in groups on the next three pages for ease of reading. As will be seen, not all of the correlations of the data were positive and there were varying degrees of overall correlation, which should be considered when attempting to draw any conclusions from the data.
Benefits and Intrinsic Job Satisfaction
Regression output from Excel
Graph
Benefits and Extrinsic Job Satisfaction
Regression output from Excel
Graph
Benefits and Overall Job Satisfaction
Regression output from Excel
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 /Intrinsic / 0.3169 / 3.5035 / y=0.3169x+3.5035 / 0.2776
Extrinsic / -0.1155 / 5.9383 / y=-0.1155x+5.9383 / 0.1723
Overall / 0.1247 / 4.5954 / y=0.1247x+4.5954 / 0.1323
Similarities and Differences
Both the Intrinsic and Overall Job satisfaction graphs showed positive correlations whereas the Extrinsic Job satisfaction had a negative correlation (Schmidt, 2000).
Correlation coefficients
The Benefits vs. Intrinsic Job Satisfaction showed the strongest correlation with a correlation coefficient at r2=0.2776. It is the largest of the three because that data is most linear of the three (Schmidt, 2000). Assuming that it’s causal, a manager could infer that an increase in benefits would have a more direct increase in Intrinsic Job Satisfaction and could that to craft employee benefits packages (Inc., 2010).
Conclusion
It is clear that using correlation statistics and regression can reveal if there are connections between sets of data. Some times the relationships can be causal and sometimes just a coincidence. Using regression analysis helps determine how strong and in what way the relationship exists. In the case of the data examined here, it appears that the strongest relationship may be that as employees indicate they are satisfied with their benefits, they are also intrinsically satisfied with their job. This could be an effective tool for a manager to use in helping motivate employees as well as figuring out how many resources should be used on benefits packages to help improve employee morale.
References
Inc. (2010, Apr 26). How to build a competitive employee benefits package. [general format]. Retrieved from
Schmidt, S. R. (2000, Jan 6). Research methods for the digitally inclined. [general format]. Retrieved from