This is quoted most often when explaining the accuracy of the regression equation. Ideally we would like to see this at least 0.6 (60%) or 0.7 (70%). This number tells you how much of the output variable’s variance is explained by the input variables’ variance. R Square tells how well the regression line approximates the real data. This is the most important number of the output. The goal here is for you to be able to glance at the Excel Regression output and immediately understand it, so we will focus our attention only on the four most important parts of the Excel regression output. Some parts of the Excel Regression output are much more important than others. The 4 Most Important Parts of Regression Outputġ) Overall Regression Equation’s AccuracyĢ) Probability That This Output Was Not By Chanceģ) Individual Regression Coefficient and Y-Intercept Accuracy (Is Your Sound and Internet Connection Turned On?)Īmazon Kindle Users Click here to View Video
#EXCEL LINEAR REGRESSION MEANING HOW TO#
Step-By-Step Video About How To Quickly Read and Understand the Output of Excel Regression
This video will illustrate exactly how to quickly and easily understand the output of Regression performed in Excel: If you know how to quickly read the output of a Regression done in, you’ll know right away the most important points of a regression: if the overall regression was a good, whether this output could have occurred by chance, whether or not all of the independent input variables were good predictors, and whether residuals show a pattern (which means there’s a problem).Įxcel Regression Output With Color-Coding Added There is a lot more to the Excel Regression output than just the regression equation. How To Quickly Read the Output of Excel Regression