We need the slope and *y*-intercept to write the equation of the least-squares regression line in the form \widehat{y} = a+bx.

The constant coefficient -3.64 is the y-intercept and the goals coefficient 14.02 is the slope. So the number of wins, y, is the response variable and the number of goals, x , is the explanatory variable. The slope of the line is b and a is the y-intercept. Plugging in the slope and the y-intercept in the equation of the least-squares regression line, we get,

So, \widehat{y} = 14.02 x -3.64 is the equation of the least squares regression line for this model.

Plugging in x = 2.5 we get the total wins of team as

\widehat{y} = 14.02 \times 2.5 - 3.64 = 31.41

Rounding off to the nearest whole number of wins, the number of wins are 31.

Skills you may want to recall:

How to interpret computer regression output:

https://www.khanacademy.org/math/ap-statistics/bivariate-data-ap/assessing-fit-least-squares-regression/v/interpreting-computer-regression-data