Multiple Regression Syllabus

Multiple Regression

This course covers Multiple Regression and consists of 14 topics.

Multiple Regression Part One – In this video we show how multiple regression is used to predict computer sales per-capita based on education and income.
Multiple Regression Part Two – In this video we continue from the previous example.
Dummy Variables – In this video we show how to incorporate qualitative variables such as seasonality to predict auto sales.
Introduction to Conjoint Analysis – In this video we show how conjoint analysis can determine what product attributes are valued by customers.
Conjoint Analysis and Regression – In this video we show how an actual regression is used to illustrate conjoint analysis.
Value Based Pricing –In this video we show how conjoint analysis is used to estimate the value of product attributes.
Nonlinearities and Interactions – In this video we show how to include nonlinearities and interactions in a multiple regression analysis.
What Makes NBA Teams Win – In this video we use multiple linear regression to show what makes NBA teams win.
QB Rating – In this video we show how to use multiple regression to closely approximate the NFL’s QB Rating formula.
Maximum Likelihood Estimation – In this video we show how Maximum Likelihood Estimation is used to estimate statistical parameters.
Logistic Regression – In this video we show logistic regression can be used to predict dependent variables that can assume two outcomes (such as Live or Die after an operation.)
​Logistic Regression for Grouped Data – In this video we explain how to do logistic regression with grouped data
The Windchill Index – In this video we use multiple regression with nonlinearities and interactions to reverse engineer the complex Windchill Index formula.
Predicting Wine Prices – In this video we show how to use multiple regression to predict prices of Bordeaux wine based on temperature and rainfall.
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