Tutorial 3: Confidence intervals and bootstrapping
This is Tutorial 3 of a series on fitting models to data. We start with simple linear regression, using least squares optimization (Tutorial 1) and Maximum Likelihood Estimation (Tutorial 2). We will use bootstrapping to build confidence intervals around the inferred linear model parameters (Tutorial 3). We'll finish our exploration of regression models by generalizing to multiple linear regression and polynomial regression (Tutorial 4). We end by learning how to choose between these various models. We discuss the bias-variance trade-off (Tutorial 5) and Cross Validation for model selection (Tutorial 6).
In this tutorial, we will discuss how to gauge how good our estimated model parameters are.
- Learn how to use bootstrapping to generate new sample datasets
- Estimate our model parameter on these new sample datasets
- Quantify the variance of our estimate using confidence intervals
Experience with Python Programming Language