91Ó°ÊÓ

It is stated in the regression line question that,

$\stackrel{Áåœ}{y}=60.7+0.139x$

The most common method for fitting a line to a scatterplot is least squares.

As a result, the number of victories for a team with a salary of $\$200$million is computed as follows:

$$\begin{gathered} \stackrel{Áåœ}{y}=60.7+0.139x \\ =60.7+0.139\left(200\right) \\ =88.5 \end{gathered}$$

As a result, for a team with a salary of $\$200$ million, the expected number of victories is $88.5$

The regression line is provided in the question.

$\stackrel{Áåœ}{y}=60.7+0.139x$

Thus, the number of wins for a team that spends $\$400$million on payroll is calculated as:

$$\begin{gathered} \stackrel{Áåœ}{y}=60.7+0.139x \\ =60.7+0.139\left(400\right) \\ =116.3 \end{gathered}$$

As a result, for a team with a salary of $\$400$million, the expected number of victories is $116.3$

The regression line is provided in the question,

$\stackrel{Áåœ}{y}=60.7+0.139x$

As a result, we must assess our confidence in the results of parts (a) and (b) (b). We can see from the scatterplot that the payroll in the data set appears to range from $\$75$million to $275$million dollars.

We are confident in the forecast of the portion since $200$million dollars is within the I range of payrolls in the data set (a). We are not confident in the prediction of part (b) since we are using extrapolation and $\$400$million is not within the range of payrolls in the data set.