Multiple linear regression in R

Multiple linear regression is important and complicated. Let's get started! Note: I misspeak at about 9:27, stating the null hypothesis incorrectly. It should be that there is no linear relationship between the variables.

#regression

[Nothingimportant]

It was one of the best videos on youtube. That last interpretation is perfect.

[martinalcala]

Thank you very much for your videos. I've watched the ones regarding the multiple linear regressions and they've solved lots of doubts that I had!

[Nothingimportant]

I have my stats exam tomorrow and plan to learn R fundamentally by watching your videos. You do a great job.

[neiltalbert]

Thanks for the info on Cook's D. It's probably useful when sample sizes are relatively small and outliers could be disproportionately influential.

[valaiopwep]

Hey thanks for the vids they're a lifesaver, im taking econometrics this semester and as someone with not much experience in stats or programming this helps a lot

[AlanCheun]

you make good videos, i'd just pad the ends of your video with a few seconds just to let us absorb the final statement and anything on the screen that just changed, youtube will prompt an ad or jump to another video before I can pause or see what just changed

[romanvasiura]

Thank you!

I guess we should correct a little bit this code (rows 18-19)

new_data <- tibble(x1 = new_x1,
x2 = new_x2)

[Victoriataieb]

I wish you can make statistics using R series, I found your explanation straightforward and easy to understand, thank you so much

[haraldurkarlsson]

I know there are multiple ways of writing the multiple regression line but I am still a bit confused. Since you are using the approximation sign then is the y not y hat? Also does not each x_i have its own error that is epsilon_i. Therefore, in your expression should the epsilon subscript not be k?

[mardzj]

at 9:27 shouldn't the null hypothesis be that there is no MLR?

[buffaloperformanceandanaly]

Would there be any difference in the code if there is a positive relationship between one variable and a negative relationship with the second variable? I'm looking at athlete exertion, sleep, and fatigue. Thanks!

[saminrasi]

Hi,
I really like the points you mention in your lectures, very helpful!
a couple of questions
1. looking at ggpairs graph you mentioned y values doesn't seem to be dependent on the value of x1 or x2, how did you come to this conclusion?
2.have you also covered nonlinear regression in this channel?

Thank you!

[yaweli]

Is there a site where you have all your loaded video lectures in R?