Multiple Linear Regression with Categorical Independent Variable along with Interpretation using R

[@SuvamoyPramanik]

Well Explained, Thanks

[@bm359]

You're saving my GPA with my current stats course - much thanks!

[@johaantomykalllany7989]

Great explanation ma'am 👌

[@fabiolaleal7492]

Thank you very much! It's all I needed

[@trishameifabila4979]

Thank you so much

[@SajidArt1]

great explanation

[@meriyaxetri1227]

was vry helpful. Have you got any video where u show the graphical ploting of this kind of data.
thank you very ,much.

[@thulfiqaralijasimal-graiti5077]

Hi-if I have a character variable, named season having 5 sampling dates, I changed the season to factor using as.factor option like what you did, but It showed in regression output NA for all dates and result for this factor? Do you have suggestion?

[@rehanhusain3673]

Maam do you also teach MCDM ??
Like dematel ??

[@salmachabib8863]

Thanks a lot for this great explanation!

[@MeftahSuchok]

Thank you for detailed explanation. But, I guess that you should centralized the data in order to get meaningful interpretations, otherwise the intercept terms are meaningless. Weight and age cannot be zero.

[@muhammadazkas2738]

Mam, does the variables that not significant include to the model?

[@hemlatajoshi28]

no..it just increases complexity nothing else. so better not to add insignificant variables

[@swarupghorai2560]

Please discuss ANOVA topic in this vdo

[@yuhangsun8977]

So,I have a question, lm function in R can recognizedummy variable automatically?

[@hemlatajoshi28]

For this u need to convert that variable in factor first

[@rayray0313]

Excellent!

[@istiyaque_ahmad]

Thank you so much.

[@shubamtarwey7837]

Thank you.

[@yuhangsun8977]

very clear

[@anamikagoswami6592]

Madam if i need help in data interpretation at what email address can i seek help.

[@kamalulhaq2361]

Great ❤😘