Adding variables to your multiple regression model

Linear regression is considered to be simple regression if only one explanatory variable is used and multiple regression if the model includes more than one explanatory or independent variable. In this video we explore how to add additional categorical variables and numeric variable to your linear regression model. If you are interested in statistical analysis then learning how to undertake a multiple regression analysis will be of interest.

[@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

[@wizardaka]

Exceptional explanation, this is how this should always be taught

[@John-xi2im]

very good lecture, really enjoyed it ! thanks a lot !

[@RProgramming101]

glad you liked it

[@pipertripp]

Hiya Greg, any reason why you're still using maggritr pipes instead of native pipes? Great programme, btw. I'm looking forward to the rest of this series! One other thing, the initial linear model you had with the penguin data (not including species predictor in the model) is that a good example of Simpson's paradox? There is a downward trend when species is not considered, but once species is taken into account, the correlation between bill_length and bill_depth becomes positive.

[@hypercrack7440]

Hey Greg, love your video but PLEASE STOP THE SCRATCHING NOISES everytime you try to underline or point out something it nags me, idk if anyone's noticed but as a headphones users it feels like scratching with your nails on a chalkboard.
Thank you. 😅

[@cheninitayeb6590]

please where is pdf file .thank you

[@yordanosgizachew9356]

I am wondering the same thing.

[@CanDoSo_org]

Hi, Greg, how to plot the figure at 4:10 please. Thanks.