Lecture 21 – Inference for Modeling
by Suraj Rampure (Summer 2020)
The Data 8 textbook chapter on estimation may be very helpful.
A big picture overview of inference. Parameters and estimators. Bias and variance of estimators. The sample mean estimator.
Using bootstrap resampling in order to estimate the sampling distribution of an estimator.
Defining confidence intervals more generally. Describing and demoing how we can use the bootstrap to create confidence intervals for population parameters.
The assumptions we make when modeling with linear regression..
Using the bootstrap to estimate the sampling distributions of parameters in a linear regression model. Inference for the true slope of a feature.
Multicollinearity, and its impacts on the interpretability of the parameters of our model. A summary of the lecture, and a brief overview of the ML taxonomy.