Term: Spring 2023

Date: March 22nd, 2023

Time: 4:30pm - 6:30pm

Location: Torgersen Hall 3310

Instructors: Simin Zheng & Parul Patil

Presented By: Statistical Applications and Innovations Group (SAIG)

Description:

Bootstrapping is a statistical procedure that resamples a single dataset with replacement to create many simulated samples. This results in mimicking the sampling process from the population.  Practitioners can use the resamples to develop an estimate along with its bias, variance, confidence intervals, prediction intervals, etc.  Bootstrapping is often used when the assumptions of parametric techniques, such as the assumption of normality, are not met.  It can be used to develop hypothesis tests and confidence intervals in these cases (and much more!).   This course provides an introduction to the topic of bootstrapping.  You will learn the idea of bootstrapping and get a deeper understanding why it is helpful and important through an example implemented in R.