Multiple Regression from beginning to end in 30 minutes.

Multiple regression is linear regression analysis using more than one explanatory variable. Regression analysis involves creating a model that can be used to predict the value of an outcome variable from an independent variable. This video uses R programming to to illustrate multiple regression and the diagnostics required to ensure that the assumptions are met. These include that the residual values are are normally distributed, that the residual values are independent and that there is a linear relationship between the explanatory variable and the outcome variable. R programming represents an extremely powerful tool to undertake linear regression analysis including multiple regression.

[@RuhulAmin-tx8xi]

This guy is one of the very rare among the statisticians who knows how to tell stat to people!

[@coomtothebroom778]

Always great to see a new video from you and I’m excited to dive into this. Thanks for all the hard work you put into these videos!

[@RProgramming101]

Thanks for the great feedback. Am delighted that you enjoy the videos.

[@anir9101]

Thank you so much for these amazing tutorials!🙏I have recently found your channel and they are EXTREMELY helpful. They are helping me a lot in my data analysis in my PhD. If possible, could you kindly make a tutorial video on how to use RSA R package to do polynomial regression and response surface
analysis to test congruence/incongruence (Edwards 2002). Thank you and please keep making these amazing tutorials!🙏🙂

[@marianam5181]

Thank you for another clear explanation!!

[@davehussey4024]

Without doubt one of the most clearly explained videos on this topic. Well done. Love your content. 👏

[@RProgramming101]

thanks so much for the lovely feedback.

[@gianluca.pastorelli]

Great video as always! You covered a lot of complicated aspects in a clear way and in just half an hour. And by the way, I do like your new thing "happy days" 😂 The sound effects don't bother me personally, they make your videos sound more fun and they draw the attention to the details you want to highlight. Keep up the good work!

[@RProgramming101]

Awesome, thank you! (I appreciate the feedback)

[@matthewdinh6688]

Love your content, this helps alot. I have a question about the residuals vs fitted values plot, if those values are heavily clumped up or concentrated, wouldnt it mean that the model's outputs are very close to the real values making it a strong model fit in correlation to the real data? Just looking to understand better, thank you!

[@ericsmithschannel867]

Another fantastic video. Thank you! (Personally, I prefer the audio cues).
My question: what is the R code to generate the plots and stats to test assumptions of linearity, independence, homoscedasticity, and normality of residuals?

[@RProgramming101]

hi there - I'm about to post a video that goes into that (in the next few days)

[@Ammarsays]

Very clear explanation of the basics. I often see in research papers that multiple regression models are "adjusted" for some variables like age and gender. Is adjustment the same thing as adding the variable as an explanatory variable in the model? In other words is it same as "weight ~ height + age + gender" ?

[@RProgramming101]

hi there - yes, that's right. We adjust for confounders but adding them into the model. I have another video coming out about this very soon

[@happysmiker]

thank you sir

[@RProgramming101]

Most welcome

[@guidocaru]

thanks! could you share the code for this?

[@pmfelix]

Thank you for the summary, which is excellent! However, taking the example with the interaction, which is relatively complicated to read because Transmission is a categorical term, you said (and well) that for every unitary increase in weight, manual cars, have a decrease of 5.2984 when compared to if the car was automatic. But, my question is, can you state with this output that, for every unitary weight, Automatic cars also show a significant decrease in efficiency?
Cheers.

[@RProgramming101]

That's a good question - I'll need to look at the video again and get back to you. I have more detailed videos about this coming up too though (so keep an eye out for them)

[@MrVigon]

Grateful for the videos, but i have to say i dislike the sound effects, especially the ones like dice-in-the-box when you zoom in and out or when they just parallel some visual cue like circling. I think they are redundant and too frequent, so the distraction they bring outweighs the intended emphasis.

[@RProgramming101]

thanks for the feedback - will look into making a change.

[@ChrisH-yg8ll]

Thanks for the video - but please lose the sound effects! They're unnecessary and deeply distracting.

[@myshinything1]

The sound effects are distracting. You are quite softly spoken and the rattles and clicks are loud enough that they pull my attention away from what is being said. Which is probably the opposite of your goal as the noises coincide with when you are trying to highlight something important. It is like trying to have an interesting conversation while someone rattles ice in a glass next to my ear. If you absolutely have to include the sounds (and I don't think you do) make them much quieter and increase the volume of your voice track.

[@RProgramming101]

will do (thanks for the feedback)

[@orthodoxNPC]

cacophony, no thanks