Introduction to Linear Regression | Laura Lyman
About This Video
Linear regression is a fundamental tool in statistics and data science for modeling the relationship between different parameters. It can be used for prediction, forecasting and error reduction by fitting a predictive model between a response variable and a collection of explanatory variables based on an observed data set. Through linear regression analysis, we can quantify the strength of the linear relationship between the response and different explanatory variables, and we can identify parameters that may contain redundant information.
This workshop introduces the basics of simple and multiple linear regression. We will present both mathematical theory and applications in the context of real data sets — ranging from survey results collected by the US National Center for Health Statistics (NHANES), to real estate listings in Sacramento, CA. After the talk, the R code used will be provided, so attendees can revisit examples of how to apply this foundational modeling method.
This workshop was conducted by Laura Lyman, Instructor of Mathematics, Statistics, and Computer Science (MSCS) at Macalester College
In This Video
Instructor of Mathematics, Statistics, and Computer Science (MSCS), Macalester College
Laura Lyman is an instructor of mathematics, statistics, and computer science at Macalester College, a top-tier liberal arts school located in Saint Paul, Minnesota. She is an applied mathematician who researches how uncertainty propagates through models in science and engineering using a class of tools called spectral methods.
Prior to her current role, Laura did her PhD work at Stanford University in the Institute for Computational and Mathematical Engineering (ICME). She is a recipient of the Stanford Centennial Teaching Award and the ICME Instructor Award for 2021-2022.