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#### Ona took a random sample of 25 soccer teams across Germany. She tracked the average number of goals each team scored per match and how many total matches each team won in the 2015-2016 season. Ona noticed a moderately strong linear relationship in the sample. Here is the computer output from a least squares analysis for predicting total wins from a team's average goals per match

1260 viewed last edited 1 year ago Anonymous
0 Use this model to predict the total wins of team that average 2.5 goals per match. Sangeetha Pulapaka
0

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: