Why is it we can model non-linear effects in linear regression?
What the heck does it mean for a model to be “linear in the parameters?”
In this webinar we will explore a number of ways of using a linear regression to model a non-linear effect between X and Y.
Usually these are intentional: we can model a whole slew of curves in linear regression. We’ll explore a number of these: log curves, exponential curves, sine and cosine curves, quadratic and cubic curves.
We’ll also compare what happens when we apply these functions to just X, just Y, or both.
But sometimes we’re left with a transformation on Y, simply to meet distributional assumptions.
We’ll talk about the different curves, how to know which to apply, and how to interpret the coefficients from these kinds of regression models.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
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Why do we need to transform (s transform ) independent variables before logistic regression .