trealization of student' test statistics associated with the hypotheses bi and bi in particularthe outcome of in ( >| | -value of student' test (two-sided test[ ] confidence intervals for the parameters -squaredcoefficient of determination (percentage of variation explained by the regression)as defined in ( adj -squaredadjusted (explained in section -statisticrealization of the test statistic ( associated with testing the full model against the default model the associated degrees of freedom (df model and df residuals - are givenas is the -valueprob ( -statistic aicthe aic number in ( )that isminus two times the log-likelihood plus two times the number of model parameters (which is here you can access all the numerical values as they are attributes of the fit object first check which names are availableas indir(fitthen access the values via the dot construction for examplethe following extracts the -value for the slope fit pvalues [ - the results show strong evidence for linear relationship between shoe size and height (ormore accuratelystrong evidence that the slope of the regression line is not zero)as the -value for the corresponding test is very small ( - the estimate of the slope indicates that the difference between the average height of students whose shoe size is different by one cm is cm only of the variability of student height is explained by the shoe size we therefore need to add other explanatory variables to the model (multiple linear regressionto increase the model' predictive power analysis of variance (anovawe continue the student survey example of the previous sectionbut now add an extra variableand also consider an analysis of variance of the model instead of "explainingthe student height via their shoe sizewe include weight as an explanatory variable the corresponding ols formula for this model is height~shoe weight