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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.

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.

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