- [Narrator] Okay so, now we're gonna talk about…the studentized deleted residual that…we generated in the last video.…Let me briefly mention where the name comes from.…Studentized may seem like a bit of a strange name.…It's actually named after a gentleman…whose pseudonym was student; his real name was Gosset.…It's easy to find information about him on the web,…because he was one of the most famous…20th century statisticians.…Anyway, Gosset worked for Guinness the beer brewery.…
And they didn't allow him to do any kind of outside work…but he enjoyed writing academic papers…and doing statistical work in his spare time.…So again, it's very well known that he wrote…under the name student.…So, this was a contribution that we get from Gosset.…It's a powerful way to look at outliers.…Let me try to explain what's going on with this.…So, the whole idea of this studentized deleted residual,…in the case of this first instance which is a male employee…whose beginning salary is about $32,000 we get a studentized…deleted residual of just over ten.…
Instructor Keith McCormick covers simple linear regression, explaining how to build effective scatter plots and calculate and interpret regression coefficients. He also dives into the challenges and assumptions of multiple regression and steps through three distinct regression strategies. To wrap up, he discusses some alternatives to regression, including regression trees and time series forecasting.
- Building effective scatter plots in Chart Builder
- Challenges and assumptions of multiple regression
- Checking assumptions visually
- Creating dummy codes
- Creating and testing interaction terms
- Understanding partial and part correlations
- Spotting problems and taking corrective action
- Dealing with multicollinearity
Skill Level Intermediate
1. Simple Linear Regression
2. Introduction to Multiple Linear Regression
3. Dummy Code and Interaction Terms
Creating dummy codes8m 4s
4. Three Regression Strategies
5. Spotting Problems and Taking Corrective Action
6. Other Approaches to Regression
What's next1m 35s
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