Regression Analysis (Evaluate Predicted Linear Equation, R-Squared, F-Test, T-Test, P-Values, Etc.)

Multiple Linear Regression Analysis, Evaluating Estimated Linear Regression Function (Looking at a single Independent Variable), basic approach to test relationships, (1) 𝐑^𝟐 Correlation between X (Independent Variable) & Y (Dependent Variable), F-Test, (2) Regression Analysis: If there is a significant relationship between X (Independent Variable) & Y (Dependent Variable), T-Test, (3) Explaining how to calculate the Degrees Of Freedom for the F-Test & T-Test, detailed discussion comparing two different regression equations to see which best predicts the dependent variable by Allen Mursau

[@johng5295]

Thanks in a million! Very well explained. This is the nth time that I am watching this again. Great content. Awesome. I couldn't find this explanation--simply put anywhere else. “Great teachers are hard to find”. Grade: A++ 💥

[@alsrong4816]

Very detailed and vivid explanation! Thank you!

[@diaconescutiberiu7535]

Best video on RUclips, which explains a lot more than others

[@ibijolastephen1172]

Thank you for this well explained lecture.
I really got the solution to my problems on linear regression.

[@demetriosfr]

One of the best explinations on this topic. thanks.

[@johnmarkatom606]

Great job! An awesome lesson. Well done sir!

[@TheArtsAtUT01]

Very thorough! Thanks :)

[@maiden5427]

Probably one of the best explanation of regression output

[@leonardocabrera9253]

This is the best lecture in statistics on my entire life.

[@RaulPelcastreRealEstate]

Thank you for your time Mr. Mursau. 🙏

[@Duxaization]

Good slides, not overloaded. Because you can clearly see where everything comes from.
You get my like

[@Misha.Zoey.2024]

Great! Solved my numerous queries!

[@bonganemamba9690]

Been looking for such explanation, thanks

[@ephares1988]

Thank you for sharing this, it is really helpful

[@Umar_P]

Thank you Allen for this wonderful session ..... explained nicely and very helpful Thank you again :-)

[@MrHugosky1]

Great Lecture! Thank you very much in deed.

[@Privacy-LOST]

Very nice video. I would love to get more insight about the explanations on "why is it so" and no just what to apply. I'm reading more theoretical material but I'm still trying to connect the dots.

[@41abhishek]

Best.. The best tutorial in depth.. Thanks a lot sir

[@nilushikaperera4664]

It is a perfect explanation. Thank you, sir.

[@MrGanesh63]

Thanks Allen. Very useful.

[@mayankverma6325]

Good and to the point explanation sir. Thanks.

[@neodrone1]

Very helpful video. Thank you.

[@trinhvu9118]

Thank you, Sir!

[@mmtx180]

Thank you. You make this all seems so easy.

[@abualia922]

Great job, thanks a lot

[@mrakash21]

Excellent Video. Thanks a lot

[@izzyn9579]

Thank you, best ever!

[@dhananjaykansal8097]

Absolutely out of this world. I can’t explain them In words. God Bless you Sir!!!!! Cheers!,,,,

[@johannrahman9513]

vry greatful to u

[@PUTHENTHOPE]

Thank you!

[@krish_krish354]

Explained well sir

[@paramjitsingh9722]

Thanks for video....

[@savailahunzai7005]

Thanks! awesomely explained. please make a lecture for heteroscedicity Tests and remedies too. Thanks in advance.

[@MrSupernova111]

You're wrong sweetheart.

[@singmelyrics2061]

@@MrSupernova111 hahaha not everyone good at English words

[@yashsrivastav3859]

Thanks a lot !

[@47548556]

Valuable video!!
Can you please explain how the regression results in excel can be used to tell If the coefficient sufficiently different from zero or it is sufficiently different from one

[@abhijeetlage8145]

wonderful

[@kumarnn6115]

Thank you very much

[@tindyebwadeuson6716]

Thanks!

[@xinxinwang2246]

good. I like it.

[@JS-jk5kp]

Really informative slides thank you, what would have helped is at the end if you slowed down and went into a bit more detail as to what exactly each of the tests were used for and what they meant during our conclusion, this is what usually comes up in exams. Great work nonetheless!

[@emantoraih7700]

Thank you

[@gajendrakumar8436]

Thanks Sir

[@treblecovers8758]

Genius!

[@WahranRai]

Overloaded slides !!!!

[@Privacy-LOST]

I liked that personally, because I could get everything at one glance and follow

[@jmariebugingokagenza6868]

Very helpfully

[@statisticslearning]

Good

[@morganyakkov5804]

I think there is small mistake on the slide. SSE(yellow) is actually distance from the actual point to regression line, and SSR(green) is supposed to be from mean line to regression line.

[@MrSupernova111]

Have you tried adding a few more things per slide? To make sure nothing gets missed. Thanks!

[@cruisemissle87]

Very good, so what if one of the coefficients fail the test? Do you have to redo the model without that variable then?

[@anneiza8953]

Hello, your video is very useful. I have a doubt when 0 or value less than cero appears in the confidence interval, You mention that there is no linear relationship between x and y values. If this happens can a quadratic or cubic model be given? or what could be the model?
Please, woul you explain me.
Thanks.

[@gauravstube]

Very helpfull, would it be possible to get the excel

[@jameshu6915]

why n-k-1, my test book does not include 1

[@doctorwonderful]

muy bueno

[@krissdbpc]

Please create a few playlists. It's impossible to find these videos in your channel. :/

[@kellydonahue2332]

The highlight yellow circle keeps changing the color from blue to green, since yellow + blue = green

[@dsklife]

Please let me know if you can help me with review literature .. of course, i'll respect your time and efforts ..

[@samkab677]

shouldnt the finv and fdist formula be for 2 tail test ? Please assist

[@brainfields7943]

How did you make those graphs i have all my data but i dont knoe how to make those graphs

[@nedralim9237]

what if i got a t-stat value that is exactly 2.28 is it still passed?

[@michelleliu4868]

does anyone know how the estimated variance of the estimated coefficient is calculated??

[@menaka857]

Sir i carried out multiple regression. and my f value for 6,7 degree of freedom at 5% significance level came out 2186010... isn't it too large?

[@BoseSumantraa]

is the regression analysis using Excel available for download ?

[@abbaskhanyousafzai1]

a Biggggggggggggggggggggggggggggg B Sir your are the best

[@ganesharya3512]

what do u mean by 5 percent probability

[@prachigupta9060]

correct me if I'm wrong but I'm a bit confused here. Doesn't the rule say that if the t-statistic or the calculated value is more than the critical value, we reject the null hypothesis? Then how are we passing the test here?
According to the curve, the t value should lie between -2.228

[@shannykse7110]

yes we reject the null hypothesis.. meaning we pass the alternative hypothesis.. he was referring to the alternative test when he said pass/fail.

[@diaconescutiberiu7535]

Null hypothesis means "m" is = 0 so y would become y=b which means x is not related to y. We don't want that...it would mean no relationship between x and y, thus we reject this and accept alternative... meaning m different from zero...thus there is a relationship between y and x...that would be y =mx+b

[@basavarajag1901]

Hi , can i know how you got F value 10.89?

[@musfiqurrahman6517]

calculate critical and" f " statistic value considering the following information :
Significance level 0.05
Numerator 11.
denominator 3.
Total sum of Square 160.
Treatment sum of square 60.
Observation number 25.
treatment number 5.
By solving this someone help me.

[@tloco9551]

Understandable, but the graphics are really kind of messy. Thousand colourful things at one page.

[@krish_krish354]

Formula not working T.INV.2T

[@ldpy33]

Here's the question: IS THE ANSWER E?)
Which of the following tells us how strong the relationship is between two variables?
a) the slope of a line
b) the intercept of a line
c) the coefficient of determination
d) the coefficient of correlation
e) both C and D are correct

[@yennieeeee]

still cant understand...

[@ArifkSani]

He sounds like kripky from big bang theory

[@SMHLK218]

You made my day :D

[@nielroosenboom1155]

sorry, but the t distrubution, you explain it wrong... if it is inside the numbers, your h0 will be stay... learn again

[@Duxaization]

RIP Headphone users

[@dr.kingschultz]

Several minor mistakes, but...

[@user-ns3jr3jd6u]

You should be looking at Adjusted-R2 and not R2 for multiple regression

[@konstapelwalander]

This is probably the mushiest explanation ever. Way to many colors that are completely unrelated..