Lecture 12 – Simple Linear Regression
by Suraj Rampure (Summer 2020)
Make sure to complete the Quick Check questions in between each video. These are ungraded, but it’s in your best interest to do them.
The correlation coefficient and its properties.
Defining the simple linear regression model, our first model with two parameters and an input variable. Motivating linear regression with the graph of averages.
Using calculus to derive the optimal model parameters for the simple linear regression model, when we choose squared loss as our loss function.
Visualizing and interpreting loss surface of the SLR model.
Interpreting the slope of the simple linear model.
Defining key terminology in the regression context. Expanding the simple linear model to include any number of features.
RMSE as a metric of accuracy. Multiple R-squared as a metric of explained variation. Summary.