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Regression Analysis

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Unit 5 – Regression Analysis
Jessica Laux/Bakos
American InterContinental University

Abstract
Data regression and charting are important parts of interpreting data. If one uses scatter plots, and data analysis, one can determine if a correlation exists between two data sets, or if there is actually very little. This can help when it comes to seeing for example, if job satisfaction overall is related to benefits, and if so how to change that in the favor of the business.

Introduction In the following information, we will show regression outputs for data sets from the AIU data set. We will determine correlation and what it means, as well as show scatter graphs that can help determine if there is any correlation to be shown. One has to be careful to input the proper data if they want the analysis to come out correctly.
Benefits and Intrinsic Job Satisfaction
Regression output from Excel
|SUMMARY OUTPUT | | | | |
|Intrinsic |0.326704508 |3.438142011 |Y=0.0034x+4.5491 |0.0012 |
|Extrinsic |-0.134516538 |6.034361553 |Y=1.6912x+13.859 |0.2275 |
|Overall |0.101037811 |4.712869316 |Y=1.0105x+0.5195 |0.1021 |

Similarities and Differences In all three graphs the r2 output is nearly the same. The difference is that all three graphs have extremely different scatter plot patterns.
Correlation coefficients The first output has the strongest correlation coefficient because, the lower the number the more chance it is the one item causes the other. The higher the correlation number goes the less likely it is to affect one another.
Conclusion
When using a scatterplot to show information, it is important to be sure to have the correct information entered, so that it will turn out correctly. It is also important to make sure that your calculations are correct in order to get the desired answer. As the above information shows the correlation coefficient can determine if two items are related and to what degree.

References

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