With correlation, all we can measure is the relative strength of an association and whether it is statistically significant. With regression, we can model that association in a linear form and predict values of Y given the values of X.
After completing this chapter, you will be able to
The simple form of a linear regression model is as follows:
y = ax + b
We read this as “y equals a times x, plus a constant b.” You will note that this is the equation for a line with slope a and intercept b. The value a is also termed the coefficient for x (Figure 11.1). The constant b is where the regression line intersects the y-axis and is also called the y-intercept.
Using the baseball payroll example and assuming that a correlation exists between the payroll amount in dollars and the number of wins over three seasons, can we predict wins based on a given payroll amount?
On the basis of Figure 11.2, it appears that an ...