## With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, tutorials, and more.

No credit card required

# Time for action – understanding overfitting

Polynomial regression models are built using the `lm` function, as we saw earlier, with the option `poly`.

1. Read the hypothetical dataset into R by using `data(OF)`.
2. Plot `Y` against `X` by using `plot(OF\$X, OF\$Y,"b",col="red",xlab="X", ylab="Y")`.
3. Fit the polynomial regression models of orders 1, 2, 3, 6, and 9, and add their fitted lines against the covariates `X` with the following code:
`lines(OF\$X,lm(Y~poly(X,1,raw=TRUE),data=OF)\$fitted.values,"b",col="green") lines(OF\$X,lm(Y~poly(X,2,raw=TRUE),data=OF)\$fitted.values,"b",col="wheat") lines(OF\$X,lm(Y~poly(X,3,raw=TRUE),data=OF)\$fitted.values,"b",col="yellow") lines(OF\$X,lm(Y~poly(X,6,raw=TRUE),data=OF)\$fitted.values,"b",col="orange") lines(OF\$X,lm(Y~poly(X,9,raw=TRUE),data=OF)\$fitted.values,"b",col="black") ...`

## With Safari, you learn the way you learn best. Get unlimited access to videos, live online training, learning paths, books, interactive tutorials, and more.

No credit card required