91Ó°ÊÓ

The value of $r^2$and the data fit of the least square line. For these data, the dot plot and Minitab output for least squares regression analysis.

$R-sq$has a value of $77\%$ according to Minitab's report. This means that the linear relationship between automobile age (in years) and mileage explains $77\%$ of the variation in mileage.

The mean age of the students’ cars in the sample was $\stackrel{¯}{x}=8$. We have to find the mean mileage of the cars in the sample

Let's use the regression line equation:$\hat{y}=a+bx$

The constant $a$is stated in the column "Coef" and in the row "Constant": $a=-13832$

In the row with "Age" and the column with "Coef," the slope $b$is given: $b=14954$

The least-squares equation is then:

localid="1652895688419" $$\begin{gathered} \hat{y}=-13832+14954x \\ \stackrel{¯}{y}=-13,832+14954\stackrel{¯}{x} \\ \stackrel{¯}{y}=-13832+14954×8=105800 \end{gathered}$$

We have to interpret the value of $s$in the context of this setting.

Given in the question that, $22723$is the standard deviation from the minitab

To predict the mileage of cars based on their age, the least square regression line is utilised. The approximation of prediction error is given by the standard deviation of the residuals (s).

Is it appropriate for a Council high school teacher to use the least squares line to predict a car's mileage based on its age?

The data and Minitab output are used to anticipate the mileage of student-owned cars based on their age. It is not fair to use the least square line to forecast a car's mileage from its age for a teacher because the least square line is based on a sample of automobiles owned by students, not teachers.