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:

link

Kindly check explanation

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

I do not know what they are called but I do believe you use them to analyze your data.