In regression analysis, a p-value provides the probability (judged by the t-value associated with an estimated regression coefficient) of being true, given the claim The true regression coefficient equals 0.

Question

In regression analysis, a p-value provides the probability (judged by the t-value associated with an estimated regression coefficient) of  being true, given the claim  The true regression coefficient equals 0.

Quizplus · Accepted Answer

The p-value in regression analysis provides the probability of obtaining a t-value as extreme as the one observed in the data, assuming that the true regression coefficient is 0. The smaller the p-value, the stronger the evidence against the null hypothesis of no effect. Therefore, a low p-value (below the chosen significance level) indicates that it is unlikely that the regression coefficient is 0, and we reject the null hypothesis in favor of the alternative hypothesis that there is a significant relationship between the predictor and the outcome variable.

In Regression Analysis, a P-Value Provides the Probability (Judged by the T-Value