It takes into consideration the correlation between the independent variable and the dependent variable. The R squared can be improved by adding more independent variables in the model but not the adjusted R square. This can also be used to analyze the goodness of fit of the regression model. In the above case, the R squared is 0.25 which shows that the two independent variables included in the model are able to explain 25% of the variation in the dependent variable.Īdj R-squared: This is the adjusted value of R squared, which is the adjusted value of R square on the basis of the number of independent variables in the regression model. However, it does not show the association of each independent variable with the dependent variable. On the basis of the R-squared value, the overall strength between the independent variable and dependent variable can be measured. R squared: This value shows how much variance in the dependent variable is explained by the independent variable included in the model. In the above results since it is less than 0.05, at least one of the coefficients out of two variables is non zero. If this value is less than 0.05, then one can reject the null hypothesis with a 95% confidence level. Prob> F: This is the significance value of the F statistic, which test the null hypothesis that all the regression coefficients in the model are zero against the alternative hypothesis that at least one of the coefficient is non zero. The values in the bracket are df of model and residual. Number of obs: Total number of observations used in the regression model.į (2, 66): This is the F statistics which is calculated by dividing the Mean square of the model by the Mean square of residual. Checking the overall fitness of the model This is calculated by dividing the sum of squares (SS) divided by their df. So the df for residual is 68-2, which is 66. In this case, the total df is 68 calculated as ( n-1). Similarly, the df of the residual is the total degree of freedom minus the df of the model.

In the above results since there is a total of 3 coefficients (including constant), the df for the model is 1. The df for the model is the total number of regression coefficients estimated minus 1. The “total” is the sum of the model and residual value.ĭf: It stands for degrees of freedom that are related to the source of variance. Source: It shows the variance in the dependent variable due to variables included in the regression (model) and variables not included (residuals). The results from the above table can be interpreted as follows: On the basis of the above results the regression equation can be written as: price = 9657.754 - 271.6425 mpg + 666.9668 rep78

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Stata regress command