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

  • specify the equation format for a simple linear regression model,
  • define residuals (errors),
  • fit a linear regression line by eye,
  • describe how fitting the regression line by minimizing residuals works,
  • use the fitted regression model to make predictions of y, based on the values of x,
  • interpret residual plots,
  • determine the confidence interval for the slope of a regression line.

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.


Figure 11.1 Slope and intercept of a line.


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 ...

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