Related: Understanding the Standard Error of the Regression Using the Model to Make Predictionsįrom the output of the model we know that the fitted multiple linear regression equation is as follows: In this example, the observed values fall an average of 3.008 units from the regression line. This measures the average distance that the observed values fall from the regression line. This indicates that 60.1% of the variance in mpg can be explained by the predictors in the model. In this example, the multiple R-squared is 0.775. Multiple R is also the square root of R-squared, which is the proportion of the variance in the response variable that can be explained by the predictor variables. A multiple R-squared of 1 indicates a perfect linear relationship while a multiple R-squared of 0 indicates no linear relationship whatsoever. This measures the strength of the linear relationship between the predictor variables and the response variable. To assess how “good” the regression model fits the data, we can look at a couple different metrics: In particular, the coefficient from the model output tells is that a one unit increase in drat is associated with a 2.715 unit increase, on average, in mpg, assuming disp and hp are held constant.Īssessing the Goodness of Fit of the Model
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