Hello, thank you for your help!

Below are True and False questions that I was hoping to get double checked in case they are incorrect and I need to do some more reading to understand them better. I decided to cluster a few questions together because I am fairly confident in some of them please let me know if this is not allowed.

In simple linear regression, the prediction interval of one member of the population will always be wider than the confidence interval of the mean response for all members of the population when using the same predicting value. TRUE

If the assumptions of a simple linear regression model hold, then the estimator for the variance of the error terms,^2, is a random variable. TRUE

In ANOVA, the linearity assumption is assessed using a plot of the response variable against the predicting variable. FALSE because we assess based on the errors.

The estimated simple linear regression coefficient,^0, measuresthe strength of a linear relationship between the predicting and response variables.

FALSE. [My thoughts]: In simple linear regression, a negative value of B1 is consistent with an inverse relationship between the predicting variable and the response variable. While I know that B1 hat is consistent with a relationship between x and y. so if B1 hat is negative then this the above statement is true, there is an inverse relationship. But for B1 (no hat), given this is the population and not the sample, we would also need to have statistical significance, and thus while B1 hat is true, B1 may not be. So ultimately this is a false statement.

In simple linear regression,^1 is an unbiased estimator for0 FALSE

In simple linear regression, the normality assumption states that the response variable is normally distributed. FALSE, wouldn't it be the predictor variables or the errors?

In simple linear regression, the sampling distribution for the variance estimator is2 (chi-squared) regardless of whether the assumptions of the model hold or not. FALSE