Beer and BAC How well does the number of beers a person drinks predict his or her blood alcohol content (BAC)? Sixteen volunteers with an initial BAC of $0$drank a randomly assigned number of cans of beer. Thirty minutes later, a police officer measured their BAC. Least-squares regression was performed on the data. A residual plot and a histogram of the residuals are shown below. Check whether the conditions for performing inference about the regression model are met.

Sixteen volunteers with an initial BAC of $0$drank a randomly assigned number of cans of beer. Thirty minutes later, a police officer measured their BAC. Least-squares regression was performed on the data. A residual plot and a histogram of the residuals are shown below.

The five requirements for regression inference are linear, independent, normal, equal variance, and random.

Linear: Satisfied since the residual plot's points are all centered around $0$.

Independent: Because the respondents were distributed at random, the subjects were satisfied.

Normal: The histogram is basically bell-shaped, so I'm satisfied.

Equal variance: Satisfied since the vertical spread of the residual plot points is almost the same everywhere.

Random: Because the subjects were allocated at random, I'm satisfied.