Multiple Regression in R, Step by Step!!! 

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I love your channel and your way of explaining things! Thank you

I'm giving this a thumbs up...just on the intro !

Excelent work. Double sworded, swiftly slaying the stats serpent from planet r

always amazing!

This is super as always thanks

great video! As usual, I should say :) what about diff-in-diff (with R example, possibly)?

Thanks a lot Sir. 🤩💐

wow amazing video , thank you so much

thank you very very much!!

Hey, thank you for your videos, it is really helpful, how do we conduct full inference for multiple linear regression model?

Please can you do a video on multi nominal logistic regression in R?

So when do you use MLR versus multidimensional scaling?

what is data mouse data i kinda confuse but your video really help me

How do you know that the relationship between tail and weight? Is there any decision rules? I don't get how to conclude using r square and p values

i like that you sing; me writer director producer who is also very statistically literate for my work in psychology with daughter who is statistics major

The r^2, adjusted r^2, and p-value look good; HOOOOOORAY!

Sorry if this is a stupid question, but is there a good way to format the results table? For instance, if I wanted the beta to be rounded to 3 digits, and the t and p values to be rounded to 4 digits?

I'm sorry if I'm asking a stupid question, why p-value of weight can tell us using weight and tail isn't significantly better than only tail. Thank you so much

hello, very helpful video. Anyway I have question though - why inference (06:35) 'using weight and tail isnt significanlty better than using tail alone' is derived from line "weight" and not from "tail" line below? Does the line "weight" of regression output compares exactly 'if using weight and tail is better than using tail alone to predict size'? shouldnt it be 'if predictor WEIGHT alone is better at prediction of size than MODEL WITH BOTH WEIGHT AND TAIL' instead or im wrong? I had course about multiple regression at academics couple years ago and trying to remind everything, I have found your video , but still I actually wanted to know that. Best wishes edit: i remember that in stepwise regression we somehow exclude predictors which do not make a significant contribution and are therefore not statistically significant, so reading/watching about stepwise regression may do the thing for me?

why is the results of the tail predicting the linear regression of weight and vice versa?

What if the relationship between Y and one of the Xs is not linear?

StatQuest is TOTES CRAY CRAY🤣

how to transform large data to be like the smaller values in teh video?

the best

hey! should i perform any tests beforehand ? or not ?

Matlab?