1. The use of correlated explanatory variables in a multiple regression implies collinearity in the model.
2. The presence of collinearity violates an assumption of the Multiple Regression Model (MRM).
3. If a multiple regression has a large F-statistic but a small t-statistic for each predictor (i.e., the t-statistics for the slopes are near zero), then collinearity is present in the model.
4. The F-statistic is statistically significant only if some t-statistic for a slope in multiple regression is statistically significant.
5. If the t-statistic for X2 is larger than 2 in absolute size, then adding X2 to the simple regression containing X1 produces a significant improvement in the ft of the model.