1. When the F test is used to test the overall significance of a multiple regression model, if the null hypothesis is rejected, it can be concluded that all of the independent variables X1, X2, Xk are significantly related to the dependent variable Y.
2. An application of the multiple regression model generated the following results involving the F test of the overall regression model: p-value=.0012, R2=.67 and s=.076. Thus, the null hypothesis, which states that none of the independent variables are significantly related to the dependent variable, should be rejected even at the .01 level of significance.
3. High Multicollinearity problem occurs when the Independent variables are highly correlated with the Dependent variable.
4. The assumption of independent error terms in regression analysis is often violated when using time series data and is called the problem of Autocorrelation.
5. Homoscedasticity problem occurs when the assumption of constant error variance is violated.