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

1. T F For a simple linear regression model of y on x, when we fail to reject the null hypotheses Ho: \({{\beta }_{1}}=0\) we can be certain that there is no significant linear relationship between x and y.
2. T F Transforming the data (either x, y or sometimes even both using the log function) can help stabilize a non-constant model variance.
3. T F In statistical inference, a confidence interval for a parameter and a hypothesis test for the same parameter yield equivalent conclusions as long as the alternative hypothesis is two—sided.
4. T F In general the smaller the MSR in a fitted regression model, the better the fitted model captures the information in the data.
5. T F In regression, prediction intervals are always narrower than confidence intervals.
6. T F One of the fundamental goals of statistics is to infer information about the population from a sample.

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