In statistics, linear regression is a linear approach to modeling the relationship between a dependent variable and one or more independent variables. It is one of the fundamental concepts in statistical modeling and is used to understand the relationship between variables and to make predictions. The p-value is a critical component of linear regression as it helps determine the statistical significance of the relationship between variables.
The p-value represents the probability of obtaining a test statistic as extreme as or more extreme than the observed test statistic, assuming that the null hypothesis is true. In other words, it tells us the likelihood that the observed relationship between variables is due to chance or random variation, as opposed to a genuine statistical relationship. A lower p-value indicates a lower probability of the relationship being due to chance and, therefore, stronger evidence for the statistical significance of the relationship.
