shoe weight the -statistic is used to test whether the full model (here with two explanatory variablesis better at "explainingthe height than the default model the corresponding null hypothesis is the assertion of interest is at least one of the coefficients is significantly different from zero given the result of this test ( -value - )we can conclude that at least one of the explanatory variables is associated with height the individual student tests indicate thatshoe size is linearly associated with student heightafter adjusting for weightwith -value at the same weightan increase of one cm in shoe size corresponds to an increase of cm in average student heightweight is linearly associated with student heightafter adjusting for shoe size (the -value is actually - the reported value of should be read as "less than "at the same shoe sizean increase of one kg in weight corresponds to an increase of cm in average student height further understanding is extracted from the model by conducting an analysis of variance the standard statsmodels function is anova_lm the input to this function is the fit object (obtained from model fit()and the output is dataframe object table sm stats anova_lm (fitprint table shoe weight residual df sum_sq mean_sq nan pr(> - - nan the meaning of the columns is as follows df the degrees of freedom of the variablesaccording to the sum of squares decomposition ( as both shoe and weight are quantitative variablestheir degrees of freedom are both (each corresponding to single column in the overall model matrixthe degrees of freedom for the residuals is sum sqthe sum of squares according to ( the total sum of squares is the sum of all the entries in this column the residual error in the model that cannot be explained by the variables is rss mean sqthe sum of squares divided by their degrees of freedom note that the residual square error rse rss/( is an unbiased estimate of the model variance see section fthese are the outcomes of the test statistic ( pr(> )these are the -values corresponding to the test statistic in the preceding column and are computed using an distribution whose degrees of freedom are given in the df column