Lecture 22 – Inference for Modeling

Presented by Suraj Rampure

Content by Suraj Rampure, John DeNero, Sam Lau, Ani Adhikari

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

A reminder – the right column of the table below contains Quick Checks. These are not required but suggested to help you check your understanding.

	Video	Quick Check
22.0 Introduction.
22.1 A big picture overview of inference. Parameters and estimators. Bias and variance of estimators. The sample mean estimator.		22.1
22.2 Using bootstrap resampling in order to estimate the sampling distribution of an estimator.		22.2
22.3 Defining confidence intervals more generally. Describing and demoing how we can use the bootstrap to create confidence intervals for population parameters.		22.3
22.4 The assumptions we make when modeling with linear regression..		22.4
22.5 Using the bootstrap to estimate the sampling distributions of parameters in a linear regression model. Inference for the true slope of a feature.		22.5
22.6 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.		22.6