What are the tests called that are conducted for each independent variable in a multiple regression model?

In a multiple regression model, the tests conducted for each independent variable are known as individual significance tests. These tests assess whether each variable contributes significantly to the regression model and helps to explain the variability in the dependent variable. Specifically, individual significance tests evaluate the null hypothesis that the coefficient of each independent variable is equal to zero; if the null hypothesis is rejected, it suggests that the independent variable has a statistically significant relationship with the dependent variable.

Using t-tests, these individual tests provide a p-value for each independent variable, which helps determine if the variable is a meaningful predictor in the context of the model. This analysis allows researchers and analysts to understand the impact of specific independent variables on the dependent variable, leading to better decision-making and interpretive insights in various business and research applications.