It also has some novel tools for navigating the model space, keeping an audit trail, and providing instruction as the user goes along. It performs both linear and logistic regression in Excel, producing highly interactive model worksheets with well-designed outputs. I urge you to take a look and give it a test drive. It was first released to the public in 2014 and has undergone major enhancements recently. I've used it for teaching an advanced course on regression and time series analysis to grad students in business and engineering, but it's intended for use in teaching at all levels and in applications.

Its home page is, and a set of slides that gives a helicopter tour of its features is here. Over the last 10 years I've developed an alternative, a free add-in called RegressIt, which is designed to take maximal advantage of the Excel environment and support good practices of data analysis. (See this link for a discussion.) That’s unfortunate, because an Excel file can be a very good place in which to build regression models, compare and refine them, create high-quality editable tables and charts, share and present the results, and teach regression to those constituencies of students and practitioners for whom Excel is the only analytic tool they may ever use on a regular basis. The regression add-in in its Analysis Toolpak has not changed since it was introduced in 1995, and it was a flawed design even back then. R Square always goes up when a new variable is added, whether or not the new input variable improves the Regression equation’s accuracy.Excel is often poorly regarded as a platform for regression analysis. Another reason that Adjusted R Square is quoted more often is that when new input variables are added to the Regression analysis, Adjusted R Square increases only when the new input variable makes the Regression equation more accurate (improves the Regression equations’s ability to predict the output).

Adjusted R Square is more conservative the R Square because it is always less than R Square.

Ideally we would like to see this at least 0.6 (60%) or 0.7 (70%).Īdjusted R Square – This is quoted most often when explaining the accuracy of the regression equation. 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. R Square– This is the most important number of the output.

Regression excel explained