You can find the spreadsheets for this video and some additional materials here: drive.google.com/drive/folders/1sP40IW0p0w5IETCgo464uhDFfdyR6rh7 Please consider supporting NEDL on Patreon: www.patreon.com/NEDLeducation
Yes but you’ll likely mace lots of multicollinearity between your regressors which will essentially break the model. Instead, us dimensionality reduction or variable selection techniques. If u really really want to use all your variables then linear regression isn’t for you - use a regression tree.
You can find the spreadsheets for this video and some additional materials here: drive.google.com/drive/folders/1sP40IW0p0w5IETCgo464uhDFfdyR6rh7
Please consider supporting NEDL on Patreon: www.patreon.com/NEDLeducation
Gosh, your tutorials are SO good. Thank you!
I love your videos!!!! They are really pedagogic
Thank you very much for showing us this technique.
Thank you so much NEDL. Can you please make an example with the intercept set to 0 (linest(known_ys,known_xs,0,1) with matrix formulas?
Is the process you've shown still applicable if I have more than 16 independent variables?
Yes but you’ll likely mace lots of multicollinearity between your regressors which will essentially break the model. Instead, us dimensionality reduction or variable selection techniques. If u really really want to use all your variables then linear regression isn’t for you - use a regression tree.