Code: The first line starting with Call: reflects back the code run to execute the regression, which

contains the linear model using variable names: SBP ~ Age + Weight.

FIGURE 17-2: Output from multiple regression using the data from Table 17-2.

Residual information: As a reminder, the residuals are the observed outcome values minus

predicted values coming from the model. Under Residuals, the minimum, first quartile, median,

third quartile and maximum are listed (under the headings Min, IQ, Median, 3Q, and Max,

respectively). The maximum and minimum indicate that one observed SBP value was 17.8 mmHg

greater than predicted by the model, and one was 15.4 mmHg smaller than predicted.

Regression or coefficients table: This is presented under Coefficients:, and includes a row for

each parameter in the model. It also includes columns for the following:

Estimate: The estimated value of the parameter, which tells you how much the outcome

variable changes when the corresponding variable increases by exactly 1.0 unit, holding all

the other variables constant. For example, the model predicts that if all participants have

the same weight, every additional year of age is associated with an increase in SBP of 0.84

mmHg.

Standard error: The standard error (SE) is the precision of the estimate, and is in the

column labeled Std. Error. The SE for the Age coefficient is

mmHg per year,

indicating the level of uncertainty around the 0.84 mmHg estimate.

t value: The t value (which is labeled t value) is the value of the parameter divided by its

SE. For Age, the t value is

, or 1.636.

p value: The p value is designated Pr(>|t|) in this output. The p value indicates whether the

parameter is statistically significantly different from zero at your chosen α level (let’s

assume 0.05). If

, then the predictor variable is statistically significantly associated

with the outcome after controlling for the effects of all the other predictors in the model. In

this example, neither the Age coefficient (p = 0.126) nor the Weight coefficient (p = 0.167)

is statistically significantly different from zero.