Question

earlier we investigated the relationship between x = payroll (in millions of dollars) and y = number of wins for major league baseball teams in 2016. given is a scatterplot of the data, along with the regression line ŷ = 60.7 + 0.139 ⋅ x. interpret the slope of the regression line. the slope is 0.139. the predicted number of wins goes up by 0.139 for each increase of $1 million in payroll. the slope is 60.7. the predicted number of wins goes up by 60.7 for each increase of $1 million in payroll. the slope is 60.7. the predicted number of wins goes down by 60.7 for each increase of $1 million in payroll. the slope is 60.7. the predicted amount of payroll goes down by $60.7 for each increase of 1 game won. the slope is 0.139. the predicted number of wins goes down by 0.139 for each increase of $1

Explanation:

The regression line is in the form \(\hat{y} = b_0 + b_1x\), where \(b_1\) is the slope. Here, \(\hat{y}=60.7 + 0.139x\), so the slope \(b_1 = 0.139\). The slope represents the change in the predicted \(y\) (number of wins) for a one - unit change in \(x\) (payroll in millions of dollars). So for each increase of \$1 million in payroll, the predicted number of wins increases by 0.139.

Answer:

The slope is 0.139. The predicted number of wins goes up by 0.139 for each increase of \$1 million in payroll.