Multiple Linear Regression using R ( All about it )

[@feyzanuronal2598]

Such a great and clear video! I watched so many videos but couldn't find something which clearly explains linear and multiple regression on R so clearly. Thanks a lot!

[@arunasaravanapandian3294]

Thanks for the explanation, it was really helpful ☺️

[@suriyanarayanaprabhuchandr7403]

Well Explained, simplified... but am having a doubt is this multiple regression or curvilinear regression.. Can you clear my doubt..

[@dataexplained7305]

Sorry missed this... whats your doubt ?

[@ahmed007Jaber]

thank u for this. how would you plot the regressed variable recommendation coded in 0 and 1, and other variables in values ranging from 1-16????

[@dataexplained7305]

Looks like you need to do categorization by probability of occurrence. This should follow the regression analysis..

[@ahmed007Jaber]

@@dataexplained7305 would you be able to explain how this would be done? appreciate your feedback

[@dataexplained7305]

Sorry for delay.. I haven't done this myself.. can check and make a video soon..

[@bernardraath7442]

Please can you tell me how to use very large amounts of variables?

[@dataexplained7305]

Target variable ~ * should help you...

[@masoomafarhang864]

How can we code this: rent price= b0+ b1sqrft+b2bdrms+b3sqrft^2+u Can we code it like: MLRhouseregression=lm(price~sqrft+bdrms+sqrft^2, data=hprice1)?

[@dataexplained7305]

I suggest try the predictor variables in different combinations by adding/dropping them and which ever gets best measures make the decision based on that..

[@mayradi7316]

Thank for the video. Can we find whether the error is homoskedastic using your table with coefficients? if the standard deviations for two variables are close to each other?

[@noeterry6637]

Instablaster

[@qamarshahzad3413]

Your video screen is not visible, could you please check it?

[@nad1165]

Can we find the R skript of the codes somewhere? Btw: Very good video

[@dataexplained7305]

Thanks.. you might have to scrape it from the video only..

[@nad1165]

Thank you, and may I ask if I can use the f-stat with only 1 restriction (q=number of restrictions=1?)

[@Sarrrrr_]

how can I prove that Residual standard error is equal to the square root of MSE? can i find this information using the linear regression?

[@dataexplained7305]

Calculate the error( i.e., PredictedY-ActualY) -> square that difference for each row -> then get the Mean of that whole column -> Square root of it. This will give you the Root Mean Square Error. When you stop before making the square root, it will give you the Mean Square Error. Now, you can show this in the script by using two functions RMSE(y_pred, y_true) and MSE(y_pred, y_true). Hope this helps !! Let me know

[@Sarrrrr_]

Thank you so much!!!

[@divyasukumar7324]

Thanks for the detailed and clean explanation

[@dataexplained7305]

You welcome...

[@HarpreetKaur-bx1ej]

I am getting r squared value 0.008569 which is about 0.9% and f stat value is 19.62. getting 0.9% squared values is good or bad please help. As i am confused in this

[@dataexplained7305]

Thanks for the Q..looks like there is very less correlation between your predictor and target .. try switching the features/variables.. and see if the Rsq.value Improves ?

[@HarpreetKaur-bx1ej]

@@dataexplained7305 sorry i tried but not getting any good value. i am working on Parkinson's dataset

[@dataexplained7305]

I can take a look if you can send me the details to my email.. [e-mail]

[@cengiz2645]

thank you so much! no where else on the internet could i find an answer to my question

[@dataexplained7305]

You wlcm

[@thejaskn2511]

bro why is the data partitioned and a testing data set is created ? @DataExplained

[@hafsashahzad7480]

Hi. Is there any way to perform multiple linear regression on raster time series images?

[@dataexplained7305]

Looks yes to me but haven't done it myself TBH..

[@hafsashahzad7480]

@@dataexplained7305 okayy thanx

[@krishnaswamyc9034]

Good content on contionus predictions

[@mostofakhaled9391]

how can i download these dataset?

[@dataexplained7305]

www.kaggle.com/c/boston-housing

[@Monkeysal07]

Thank you very much! You helped me so much

[@dataexplained7305]

You welcome...

[@pandemonium7617]

I like your video

[@dataexplained7305]

Thanks a lot...