These charts can give you insight into which variables are associated with each other, how strongly

they’re associated, and their direction of association. They also show whether your data have outliers.

The scatter charts in Figure 17-1 indicate that there are no extreme outliers in the data. Each scatter

chart also shows some degree of positive correlation (as described in Chapter 15). In fact, if you refer

to Figure 17-1, you may guess that the charts in Figure 17-1 correspond to correlation coefficients

between 0.5 and 0.8. In addition to the scatter charts, you can also have your software calculate

correlation coefficients (r values) between each pair of variables. For this example, here are the

results:

for Age versus Weight,

for Age versus SBP, and

for Weight

versus SBP.

Taking a few steps with your software

The exact steps you take to run a multiple regression depend on your software, but here’s the

general approach:

1. Assemble your data into a file with one row per participant and one column for each variable

you want in the model.

2. Tell the software which variable is the outcome and which are the predictors.

3. Specify whatever optional output you want from the software, which could include graphs,

summaries of the residuals (observed minus predicted outcome values), and other useful

results.

4. Execute the regression (run or submit the code).

Now, you should retrieve the output, and look for the optional output you requested.

Interpreting the Output of a Multiple Regression

Analysis

The output from a multiple regression run is formatted like the output from the straight-line

regression described in Chapter 16.

Examining typical multiple regression output

Figure 17-2 shows the output from a multiple regression analysis on the data in Table 17-2, using R

statistical software as described in Chapter 4. Other statistical software produces similar output, but

the results are arranged and formatted differently.

Here we describe the components of the output in Figure 17-2: