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There is a lot more to the Excel Regression output than just the regression equation. Some parts of the Excel Regression output are much more important than others. 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. This is the most important number of the output.

R Square tells how well the regression line approximates the real data. This number tells you how much of the output variable’s variance is explained by the input variables’ variance. Ideally we would like to see this at least 0. This is quoted most often when explaining the accuracy of the regression equation. Adjusted R Square is more conservative the R Square because it is always less than R Square.

This indicates the probability that the Regression output could have been obtained by chance. A small Significance of F confirms the validity of the Regression output. Regression output was merely a chance occurrence. The P-Values of each of these provide the likelihood that they are real results and did not occur by chance.

The lower the P-Value, the higher the likelihood that that coefficient or Y-Intercept is valid. For example, a P-Value of 0. 016 for a regression coefficient indicates that there is only a 1. The residuals are the difference between the Regression’s predicted value and the actual value of the output variable.