Question: TRUE or FALSE: Identify which of the following are valid statements.

1. T F The correlation coefficient \(r\) is a useful measure to assess the strength of a linear relationship for both qualitative and quantitative variables.
2. T F We do not need to worry about multicollinearity when we can ensure a sufficiently large sample size \(n\).
3. T F A residual plot can be used to assess the assumption of independence of the observations in a data set.
4. T F A high \(R_{a d j}^{2}\) in a multiple linear regression model implies causation between the response and at least one of the explanatory variables.
5. T F In regression the sum of the residuals will add to a value less than zero if the relationship between \(x\) and \(y\) is negative is always zero and to a value greater than zero if the relationship is positive.
6. T F The larger the Mean Squared Error (MSE), the better the fit of the model.
7. b When there are only two groups, an ANOVA model is equivalent to a two-sample independent \(\mathrm{t}\) -test.
8. T F If a data set does not meet the assumption of independence, a data transformation might improve the situation.
9. T F In a quadratic model, the regression coefficient associated with the quadratic term corresponds to the shift parameter.
10. T F Forward Selection and the Mixed Selection procedure always yield the same set of explanatory variables when all explanatory variables are quantitative.

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