![]() ![]() SS (Sum of Squares): The Sum of Squares is the square of the difference between a value and the mean value.It can be calculated using the df=N-k-1 formula where N is the sample size, and k is the number of regression coefficients. df (degrees of freedom): df refers to degrees of freedom.The terms used in the table are as follows. ![]() Moreover, in the middle of the output, you’ll see the ANOVA (Analysis of Variance) Table. Interpreting Regression Results of ANOVA Table in Excel Observations: It shows the number of products which is 11.Ģ.Standard Error: Simply, the Standard error tells about the precision of your multiple regression analysis.That means 92% of the points fit the regression line. In this dataset, the value of the Adjusted R Square is 0.92. The value will be higher than the R Square if a new independent variable improves the model or vice versa. As it provides the comparison among the variables which one is more important than the other. Adjusted R Square: Adjusted R Square is fruitful when you have two or more independent variables.In the case of multiple regression relationships, you have to keep attention to the Adjusted R square. It means that 94% variation in the dependent variable can be explained by the independent variable. Here, the value of R Square represents an excellent fit as it is 0.94. The higher the value of R Square, the better-fitted the regression line you’ll get. That means how many points fit with the regression line. R Square (Coefficient of Determination): R Square reveals the goodness of fit.The following table may help you to understand the term better. Multiple R (Correlation Coefficient): Multiple R refers to the degree of linear relationship among the variables.If you closely look at the upper portion of the regression output, you’ll get a table titled Regression Statisticsas shown in the below screenshot. Interpreting Results of Multiple Regression Statistics Table in Excel Also, check the box before Labels and press OK.Įventually, you’ll get the following output.ġ. ![]()
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