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Multiple Regression Analysis

Multiple Regression Analysis

Linear Regression

Linear regression is a method for predicting the association between a dependent variable and one or more independent variables. If a single independent variable is being investigated then the analysis is known as a simple linear regression. When there are multiple independent variables, the analysis is known as a multiple linear regression (Freedman, 2012).

A Hypothetical Study

            For the purpose of this assignment, I will describe a hypothetical study that analyzes the association between job performance in a business or corporate environment related to sales based on mathematical aptitude test scores and personality tests. The dependent variable for this analysis is job performance, which would be measured with overall sales figures, client feedback and performance reviews. These three variables would be measured using a standardized scale. For example, client feedback would be rated on a scale from one through ten for each sales employee. Similarly, sales figures would be rated within the same range. Therefore, the three figures could be averaged to create an overall job performance figure.

            As previously stated, the independent variables for this analysis would be the results of a mathematical aptitude examination and a personality test. Assuming that the working environment associated with the study involves the sale of a complex business tool or instrument, the results of the mathematical aptitude test should be relevant to an employee’s overall job performance. The employee would need to be capable of understanding mathematical calculations, methods, and concepts. Additionally, a personality test is more obviously relevant to a sales position within a company. To be a successful salesperson, an individual must possess particular personality traits. At the very least, certain personality traits are likely common among successful employees involves in sales across numerous industries.

References

Freedman, D. A. (2012). Statistical models: Theory and practice (2nd ed.). New York:

Cambridge University Press.

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