9.3 Fitting the model

The analysis in example 7.5 concluded that FEV1 in fathers significantly increases by 0.12 (95% CI:0.09, 0.15) liters per additional inch in height (p<.0001). Looking at the multiple \(R^{2}\) (correlation of determination), this simple model explains 25% of the variance seen in the outcome \(y\).

However, FEV tends to decrease with age for adults, so we should be able to predict it better if we use both height and age as independent variables in a multiple regression equation.

What direction do you expect the slope coefficient for age to be? For height?

Fitting a regression model in R with more than 1 predictor is done by adding each variable to the right hand side of the model notation connected with a +.

lm(FFEV1 ~ FAGE + FHEIGHT, data=fev)
## Call:
## lm(formula = FFEV1 ~ FAGE + FHEIGHT, data = fev)
## Coefficients:
## (Intercept)         FAGE      FHEIGHT  
##    -2.76075     -0.02664      0.11440