Model fit statistics: These are calculations that describe how well the model fits your data

overall.

Residual standard error: In this example, the Residual standard error: (bottom of output)

indicates that the observed-minus-predicted residuals have a standard deviation of 11.23

mmHg.

Multiple r²: This refers to the square of an overall correlation coefficient for the

multivariate fit of the model, and is listed under Multiple R-squared.

F statistic: The F statistic and associated p value (on the last line of the output) indicate

whether the model predicts the outcome statistically significantly better than a null model. A

null model contains only the intercept term and no predictor variables at all. The very low

p value (0.0088) indicates that age and weight together predict SBP statistically

significantly better than the null model.

Checking out optional output to request

Depending on your software, you may also be able to request several other useful calculations from the

regression to be included:

Predicted values for the dependent variable for each participant. This can be output either as a

listing, or as a new variable placed into your data file.

Residuals (observed minus predicted value) for each participant. Again, this can be output either

as a listing, or as a new variable placed into your data file.

Deciding whether your data are suitable for regression analysis

Before drawing conclusions from any statistical analysis, you need to make sure that your data

fulfill assumptions on which that analysis was based. Two assumptions of ordinary linear

regression include the following:

The amount of variability in the residuals is fairly constant, and not dependent on the value of the

dependent variable.

The residuals are approximately normally distributed.

Figure 17-3 shows two graphs you can optionally request that help you determine or diagnose whether

these assumptions are met, so they are called diagnostic graphs (or plots).

Figure 17-3a provides an indication of variability of the residuals. To interpret this plot, visually

evaluate whether the points seem to scatter evenly above and below the line, and whether the

amount of scatter seems to be the same across the left, middle, and right parts of the graph. That

seems to be the case in this figure.

Figure 17-3b provides an indication of the normality of the residuals. To interpret this plot,

visually evaluate whether the points appear to lie along the dotted line or are noticeably following

a curve. In this figure, the points are consistent with a straight line except in the very lower-left