Anyone have any topics or questions they would like to chat about during the livestream?
I’ll be doing a comparison with the regression estimation used in the chapter and Bayesian regression as that has been requested.
Anything else that folks have on their mind?
June 24, 2022, 8:40am
I thought the discussion of the regression anatomy theorem was good and wouldn’t mind working through some of the implications for the theorem in applied work, thinking here of the inevitable conversation which goes: “That’s nice, why should i care?”
It may be of very little importance, but I was just wondering about the choice of the word
explained as in ‘explained sum of square’ or ‘explained variance’, when it actually does not explain anything in the sense of give the reason or the cause.
@lacopoff, any bit of learning is important. You’re right the word explained is a bit overloaded. He’s using this term.
In statistics, the explained sum of squares (ESS), alternatively known as the model sum of squares or sum of squares due to regression (SSR – not to be confused with the residual sum of squares (RSS) or sum of squares of errors), is a quantity used in describing how well a model, often a regression model, represents the data being modelled. In particular, the explained sum of squares measures how much variation there is in the modelled values and this is compared to the total sum of squares (TSS)...
But really I think a visual does more justice. If I use my own words the explained variance is the amount of variance “reduced” once you use a linear regression to estimate the data.
If you’re like me I learn more hands on, what you could do is
Simulate some data with a linear trend,
Calculate the variance without a regression
Run a regression
Calculate the residual
Measure the variance of those
If that sounds interesting to you we can start another topic and walk through it! Great question
Here’s a quick crash course on the R-squared statistic, aka the Coefficient of Determination, and how it can help us in our stats work.
Est. reading time: 2 minutes