The glm function fits a model to the data in a similar fashion to the lm function. The only difference is that you can specify a different link function in the parameter, family (you may use ?family in the console to find different types of link functions). In this recipe, we first input the independent variables, age, sex, and education, and the dependent wages variable to the glm function, and assign the built model to lmfit1. You can use the built model for further prediction.

Next, to determine whether glm with a Gaussian model is exactly the same as lm, we fit the independent variables, age, sex, and education, and the dependent variable, wages, to the lm model. By applying the summary function to the two different models, it reveals that the residuals and coefficients of the two output summaries are exactly the same.

Finally, we further compare the two fitted models with the anova function. The result of the anova function shows that the two models are similar, with the same residual degrees of freedom (Res.DF) and residual sum of squares (RSS Df).