Machine Learning Algorithms with R : Linear Regression

For a given predictor, the t-statistic (and its associated p-value) tests whether or not there is a statistically significant relationship between a given predictor and the outcome variable, that is whether or not the beta coefficient of the predictor is significantly different from zero. Null hypothesis (H0): the coefficients are equal to zero (i.e., no relationship between x and y) Alternative Hypothesis (Ha): the coefficients are not equal to zero (i.e., there is some relationship between x and y) Another aspect to pay attention to your linear models is the p-value of the coefficients. A p-value indicates whether or not you can reject or accept a hypothesis. A very small p value means that the predictor is probably an excellent addition to your model. A standard way to test if the predictors are not meaningful is looking if the p-values smaller than 0.05.