Linear Regression MC Notes

Associative forecasting

Used when changes in one or more independent variables can be used to predict the changes in the dependent variable

F statistic

Compares the model’s prediction to a model that contains no independent variables

Null hypothesis

All regression coefficients (except the intercept) are equal to zero
This means none of the independent variables help explain the dependent variable

Alternative hypothesis

At least one regression coefficient is not zero
This means at least one independent variable contributes to explaining the dependent variable

When to reject the null hypothesis

When the significance F/p-value is < a — this means at least one independent variable contributes to explaining the dependent variable

T-test

Checks if an individuals predictor (independent variable) in your regression model is actually useful in explaining the outcome