A correlation analysis (described in Chapter 15) will tell you how strong this type of

association is, as well as its direction (which is positive in this case). The results of a correlation

analysis help you decide whether or not the association is likely due to random fluctuations.

Assuming it is not, proceeding to a regression analysis provides you with a mathematical formula

that numerically expresses the relationship between the two variables (which are weight and SBP

in this example).

Interpreting the Output of Straight-Line

Regression

In the following sections, we walk you through the printed and graphical output of a typical straight-

line regression run. Its looks will vary depending on your software. The output in this chapter was

generated using R (see Chapter 4 to get started with R). But regardless of the software you use, you

should be able to program the regression so the following elements appear on your output:

A statement of what you asked the program to do (the code you ran for the regression)

A summary of the residuals, including graphs that display the residuals and help you assess

whether they’re normally distributed

The regression table (providing the results of the regression model)

Measures of goodness-of-fit of the line to the data

Seeing what you told the program to do

In the example data set, the SBP variable is named BPs, and the weight variable is named wgt. In

Figure 16-4, the first two lines produced by the statistical software reprint the code you ran. The code

says that you wanted to fit a linear formula where the software estimates the parameters to the equation

SBP = weight based on your observed SBP and weight values. The code used was lm(formula = BPs

~ Wgt).

FIGURE 16-4: Sample straight-line regression output from R.

The actual equation of the straight line is not SBP = weight, but more accurately SBP = a + b ×