Lecture 21 – Inference for Modeling
by Suraj Rampure (Summer 2020)
The Data 8 textbook chapter on estimation may be very helpful.
| Video | Quick Check | |
|---|---|---|
| 21.1 A big picture overview of inference. Parameters and estimators. Bias and variance of estimators. The sample mean estimator. |
21.1 | |
| 21.2 Using bootstrap resampling in order to estimate the sampling distribution of an estimator. |
21.2 | |
| 21.3 Defining confidence intervals more generally. Describing and demoing how we can use the bootstrap to create confidence intervals for population parameters. |
21.3 | |
| 21.4 The assumptions we make when modeling with linear regression.. |
21.4 | |
| 21.5 Using the bootstrap to estimate the sampling distributions of parameters in a linear regression model. Inference for the true slope of a feature. |
21.5 | |
| 21.6 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. |
21.6 |