# Lecture 21 – Inference for Modeling

by Suraj Rampure (Summer 2020)

The Data 8 textbook chapter on estimation may be very helpful.

Video | Quick Check | |
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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 |