For a simple linear regression model, significance of regression is:
A) a measure of how well the regression line fits the data.
B) a hypothesis test of whether the true regression coefficient ß1 is zero.
C) a statistic that modifies the value of R2 by incorporating the sample size and the number of explanatory variables in the model.
D) the variability of the observed Y-values from the predicted values.

Question

Quizplus · Accepted Answer

Significance of regression in a simple linear regression model is determined through a hypothesis test (usually a t-test) to check if the slope coefficient (ß1) is significantly different from zero. This tests the null hypothesis that ß1 = 0, meaning it checks if there is a statistically significant linear relationship between the independent variable and the dependent variable.

For a Simple Linear Regression Model, Significance of Regression Is