Statistical Learning: 3.3 Multiple Linear Regression

[@zarinskie]

NOTES: In the equation, betas are unknown and X is observed to predict Y (known as predictors) Hyperplane is a plane that tries to minimize the squared distance between the points when you have multiple predictors p-values close to 1 are not significant completed 08.08.2024

[@EBEALEX]

Thank you sir

[@michaelcheung6290]

2:14 should it be not the closest point to the plane? As the closest point to the plane is the perpendicular projection onto the place but not f(x1, x2) in R^3

[@sparse-manatee]

The distance between a data point and its projection vertically to the plane along the vertical axis might be more easily interpreted. However, regression can also be solved using least-squares I think (which is in the sense of orthogonal projection you just mentioned).

[@OG-jz9fh]

Hi, thanks for the info, but where are the codes? Without codes or the real examples of the statics in Python, it is just a dry class or session. Please, can we be like Karl Pearson? I am sorry to say it in a not very positive way. Thanks again.