![]() This value is smaller than any reasonable significance level. The E-12 indicates that we need to move the decimal point 12 places to the left. It’s written in scientific notation because it is a tiny value. Our p-value for the overall F-test is 8.93783E-12. If this test result is statistically significant, it suggests you have a good model. This test determines whether your model with all of its independent variables does a better job explaining the dependent variable’s variability than a model with no independent variables. This is the p-value for the F-test of overall significance. In Excel’s ANOVA table, the most important statistic is Significance F. Conveniently, this value uses the measurement units of the dependent variable.įrom the output, we know that the standard distance between the predicted and observed values is 8.93 degrees Celsius.įor more information, read my posts about: You want lower values because it signifies that the distances between the data points and the fitted values are smaller. This statistic shows how wrong the regression model is on average. The standard error of the regression indicates the typical size of the residuals. For example, if you compare a model with one independent variable to a model with two, you often favor the model with the higher adjusted R-squared. The adjusted R-squared value helps us compare regression models with differing numbers of independent variables. However, there are important caveats about that! Usually, higher R-squared values are better. The R-squared value of ~0.858 indicates that our model accounts for about 85.8% of the dependent variable’s variance. So, we’ll skip it and go to the two R-squared values. ![]() Multiple R is not a standard measure for regression and it is difficult to interpret. The Regression Statistics table provides statistical measures of how well the model fits the data. If you want to learn more about the statistics, be sure to click the links for more detailed information! Regression Statistics Table ![]() We’ll work our way down from the top of Excel’s regression analysis output. Interpreting Excel’s Regression Analysis ResultsĪfter Excel creates the statistical output, I autofit some of the columns for clarity.
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