# Lecture 21 – Inference for Modeling

Presented by Fernando Perez and Suraj Rampure

Content by Suraj Rampure, Fernando Perez, John DeNero, Sam Lau, Ani Adhikari, Deb Nolan

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

The Quick Check for this lecture is due **Monday, November 23rd at 11:59PM.** A random one of the following Google Forms will give you an alphanumeric code once you submit; you should take this code and enter it into the “Lecture 21” question in the “Quick Check Codes” assignment on Gradescope to get credit for submitting this Quick Check.

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 |