Currently, I am studying the third chapter of An Introduction to Statistical Learning with Application in R which discusses linear regression. In section 3.6.5: Non-linear Transformations of the Predictors the
poly() function was used to create a polynomial regression model. After that the writers wrote:
By default, the poly() function orthogonalizes the predictors: this means that the features output by this function are not simply a sequence of powers of the argument. However, a linear model applied to the output of the poly() function will have the same fitted values as a linear model applied to the raw polynomials (although the coefficient estimates, standard errors, and p-values will differ). In order to obtain the raw polynomials from the poly() function, the argument raw = TRUE must be used.
Here I can't understand what the writers meant by " a linear model applied to the output of the poly() function.... ".
Can anyone please help me?