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 y^ = 60.7 + 0.139x . Interpret the slope of the regression line.

The slope of a regression line is the average rate of change of the line.

The interpretation of the slope is: the rate of wins per payroll is 13.9%

From the question, we have:

The equation of a linear regression is:

Where m represents the slope

So, by comparison:

This means that, the rate of wins per payroll is 13.9%

Read more about slopes at:

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Step-by-step explanation:

Given that:

Equation of regression line : y^ = 60.7 + 0.139x

x = payroll (in millions of dollars) and y = number of wins for Major League Baseball teams in 2016.

From the general regression equation:

y = mx + c

Where m = slope of regression line.

The slope (m) of the equation given is 0.139

The slope could be interpreted as ; for every per unit change in y (every win a major league baseball team has in 2016), the payroll in million dollar increases by a product of 0.139

the slope of the line is 60

one point on the line is (40,10)

step-by-step explanation:

in the equation outside the parenthesis the equation always tells you the slope. in this case it is 60.

in the parenthesis is also says 40 which is x because it is near the x and 10 is y because it is near y.

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step-by-step explanation: