Multiple regression - making sure that your assumptions are met

[@haraldurkarlsson1147]

Greg,
Thanks. Great stuff.
Now the question what to do if all but one of conditions for a linear model are met. That is the teaching moment.

[@RProgramming101]

Get my FREE cheat sheets for R programming and statistics (including transcripts of these lessons) here: www.learnmore365.com/pages/membership-r-programming-data-visualization-and-research-methods

[@tsolmonboldoo7733]

Thanks a lot Greg. I love your videos and reels. Can you exlpain bit about Logistic regression?

[@RProgramming101]

Sure thing! - Will make some videos about that soon

[@yordanosgizachew9356]

Thank you for all this, But I can't see the PDF or any document to download, even after subscribing

[@frankpaulschulte]

As usual: Very nice vid. Thanks A LOT.

[@RProgramming101]

So nice of you

[@haraldurkarlsson1147]

Just a note to those using the native pipe. In lm() it will not work using data = . . There are several solution a) data = data b) pick(x_var), or c) switch to %>%. I like the pick(x_var) option. There is a fourth option just put the model name into predict().

[@elmehdizabar]

Helpful! Thank you

[@RProgramming101]

Glad it was helpful!

[@teilzeitamigo]

Thank you, very helpful!

[@RProgramming101]

Glad it was helpful!

[@juliusirungu1363]

Nice content.... performance package can also achieve the same with ease.

[@fodaybassieturay8962]

Introduction to Periodic chemistry

[@pattheitguy]

First!