Multiple Regression - Interpretation (3of3)

[@odaialzrigat]

Thanks Dr. Pat Obi , unbelievably clear explanation , don't know why others over complicate these concepts!

[@amantekle127]

Watching this lecture is much better than attending my actual lectures online. Thank you!

[@PatObi]

Thanks for your kind comment :-)

[@herodmoonga4799]

Yeah am grateful for your presentation, will fail no exam on this topic, thanks once more Sir, you are indeed a genius

[@aishakendrick4044]

Thank you for breaking this down. I feel like partial cost of my PhD program should be paid to KZbin (that's bad I know).

[@PatObi]

You're very welcome!

[@aidylmorningdew195]

@@PatObi me too. KZbin has helped me a lot during my PhD journey

[@mukailarafiu2718]

This presentation is phenominal. May God bless you, sir.

[@PatObi]

Thank you. And you too.

[@hendrika526]

Seriously, these explanations are so clear and simply brilliant! Thank you so much!

[@andreatragni7528]

You are a wonderful and intelligent human being. Thank you

[@Guilopes99]

Probably the best clear yet more in depth explanation on the interpretation. So good! Do you normalize data before the regression? Does it matter? Thanks

[@PatObi]

Thanks for your kind comments. No, I don't think it's necessary or even appropriate to normalize regression data. Just my opinion :)

[@PatObi]

No need to normalize unless you wish to. Sorry for late response :-)

[@toseefdin5521]

Oh My - so nice to see this video- you were in the strathmore uni orientation day...i am happy to see you here professor

[@itismitabiswal7675]

Thank you so much really.....so simple explanation..that no body can give..

[@markosmanaye5578]

Very helpful presentation. Thankyou sir!

[@evelynfurtado1240]

If i have a model where the p value as a whole is not significant, but one independent variable is significant, can it still be reported?

[@Rajwaffle]

i am doing a question comparing 2 models which each have 3 independent variables. Can i use the fact that Model 2 has a lower standard error of the estimate than Model 1 as a predictor of the dependent variable?.. since this would mean narrow the width of the confidence interval and therefore give smaller margin of error in my prediction of the dependent variable? Thanks

[@42laar]

thank you so much for the thorough explanation!

[@SandeepKumar-sg7ou]

To estimate the Marginal Value Product (MVP) and Marginal factor cost (MFC) equated to allocative efficiency ratio or from regression coefficient of the variables

[@kerrisonmarange3946]

Honestly i have been helped...thank you for providing such great clarity on the topic.#preps for my next month exams

[@iamnotCorinne]

You, Sir, are a lifesaver. Thank you so much!

[@Guilopes99]

Do you have any tips on excel to: - select the right variables - check if the required assumptions are match - checking outliers and missing values - how to test several combinations of variables

[@shanuibra]

Sir if R square in regression model will around . 2 or. 3 is it problematic?

[@malloryalvacastaneda2547]

Hello Sir, where did you get the number x1=52 and x2=17? maybe i have missed it on your presentation. thank you

[@wengelawitbisrat8934]

Thank you. I was doing analysis and i got beta value positive 0.026 but the sig.value is 0.6. So should i reject the hypothesis ? Does it mean x and y are negatively related ?

[@brendanyarko2536]

what do i do if my r square is less 10% but the model is significant?

[@beckienantongo2424]

Thnx. Very helpful video

[@collinsagayi2233]

Wow. Thanks a lot. Now I can interpret my data very well

[@PatObi]

You're welcome!

[@AKAxdkid_]

Thanx a lot , i can now her heart❤️

[@patricialuize]

Thank you so much, Sir! Your videos are very helpful!

[@PatObi]

You're welcome!

[@francisbenath2309]

thanks lo for every words...

[@gardeningtourfamilycelebra8268]

sir please help me my problem is - Another persons explain his model - RSEI = 0.098*NDVI + 1.019*WET - 0.025*NDSI - 0.001*LST + 0.007 (R2 =0.998) 1. The WET must increase by 0.098 if the RSEI increase by 0.1. Nevertheless, the increased WET and the decreased LST occurred at the same time . 2. Therefore, RSEI increase will be more than 0.1. If the LST decrease by 0.098 and the WET increased by 0.098 the RSEI will increase by 0.10001. How its explain? My model is - RSEI = 0.9396*NDVI - 0.0074*WET - 0.22496*NDSI + 0.04665*LST + 0.0664 (R2 =1) So,1. If the RSEI increase by 0.1 then NDVI must increase ................? 2. RSEI will increase by 0.10001, Then LST decrease by .................? and NDVI increased by ..................................? How this model explain above mention two point? Please answer me. and help me.

[@PatObi]

Please watch my short video, Regression - In Brief 2of3, for interpretation of regression result. In your study, RSEI rises(falls) by 0.098 unit for every 1 unit increase(decrease) in NDVI, with the other variables held constant.

[@gardeningtourfamilycelebra8268]

Sir please send your 2 of 3 video link. because I want to interpreted my model. thank you sir.

[@gardeningtourfamilycelebra8268]

Sir RSEI is the dependent veritable and other WET, LST, NDSI, and NDVI is the independent veritable. I am calculated multi regression. but i can not explain the model. sir I want to explain my model.

[@tsehayenegash8394]

How can we calculate the error term

[@lorenzomiguelgarcia842]

Is there a way to determine if one of the independent variables moderates the relationship of the other independent variable and the outcome using multiple linear regression?

[@PatObi]

I'm not sure, Lorenzo, except perhaps in the context of colinearity and in a different case, inclusion of interaction terms. Please ask others.

[@Gorrdo4]

Thank you Sir.

[@somcana]

Thanks Pat!

[@ronelnature1792]

Good work 🤝

[@dinaraniyazova7323]

Hello! How to calculate p test manually? is there is formula in multiple regression?

[@PatObi]

You can watch the 2nd half of this video: kzbin.info/www/bejne/oWGag4iCpsZ5hc0

[@thebiglad]

Top guy Pat

[@ladoucefleur1748]

Thank you so much

[@ttanchor3254]

Verry helpful

[@Mandla89]

Thank you so much for this

[@davinsanch]

I owe you!

[@kemenuwapaul5761]

my god will bless u

[@PatObi]

Thank you.

[@SmartLearn-Everything]

Can you send me this ppt or docx