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What is Regression Analysis?

Multiple regression is a technique used to help evaluate the relationship between measurable data factors. Specifically, it assists in determining the extent that one or more factors (tenure, performance appraisals, education, etc.) impact a quantity of interest, like compensation. A factor in a regression is said to be significant if it helps explain variations in the quantity of interest to a large degree. If demographic characteristics of employees appear to be significant factors in a regression analysis of pay changes, this may be evidence that discrimination is present in employer compensation decisions.

PayEval makes conducting a regression analysis of employer decisions simple.

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The detailed report will include a statistical test of the factors used in the regression model.  For this analysis, a table is included with the number of observations used in the analysis, the degrees of freedom for error and the percent of the variation explained by the model.  For the overall analysis of gender impacts on pay change, the selected model with the included factors explains 60% of the variation in pay changes.  In the subsequent table, note that only length of service has a significant impact on pay change at a 95% confidence level, as indicated by the highlighted standard deviation of 8.41.