In linear regression, the p-value for an independent variable is used to judge the significance of the variable.

• A. Yes
• B. No
• C. Sometimes
• D. It measures magnitude

Answer: (A)

The p-value for a predictor in linear regression measures whether its coefficient is significantly different from zero. In this context, each predictor has a coefficient that estimates its average effect on the response. The p-value comes from a t-test of the null hypothesis that that coefficient equals zero. A small p-value indicates there is evidence against no effect, so the variable is considered statistically significant in explaining the outcome. Conversely, a large p-value suggests you cannot conclude a significant association given the data and model assumptions.

Note that the p-value does not tell you how large the effect is—that's the role of the coefficient estimate itself. It simply assesses the strength of evidence against zero. Therefore, the statement that the p-value is used to judge the significance of the variable is correct.