Linear Regression - Least Squares Criterion Part 2

Linear Regression - Least Squares Criterion Part 2.
In this video, I actually find the equation of the linear model and show the formulas to get there. I go into quite a bit of detail about notation and a few other things so the video is a bit long! We are using the Least Squares Criteria in this video.
We use our data set in order to go through all of the computations so there is a bit of grinding to do. In general this is something you'd just use a calculator / software on but it never hurts to see it all the way at least once!
#stats #statistics #linearregression #leastsquares #maths #correction #correlationcoefficient #dataanalysis #datascience

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I basically know most of the things you did but what I didn't know was how to apply linear regression to prediction or estimation model. NOW it's very clear. Thanks a whole bunch for that.

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[@fmkeane]

Hi patrickJMT - I'm a big fan of your work - I find you to be very clear and easy to understand. I've a few issues with this video. I've been practicing using Excel to solve the regression, my answer is y = 8.6701x - 438.39. I got the same answer using the 'normal formulae' method. Using your least square formula I get 8.67 for the slope. Just a calculating error somewhere. Keep up the great work - Frank

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Thanks for putting "actual" numbers in the equations!! ;-) Very helpful..

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I really like the way you explained the whole scenario

[@VahidAryadoust]

Thanks for the videos. Very helpful. Can we say that the farthest the point from the regression line, the less accurate the prediction of the regression model for that point?

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I've learned more from you in 6 minutes than a single 2 hour lecture from my professor.

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finally someone according to whom I am able to write code for least square method

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Tomorrow I am going to have my pre-calc finals exam! Thanks for uploading your videos. Your videos help me alot and are very easier to understand, not like my teacher. I 'll try to let you know if I passed the exam.

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[@robertballance6409]

Hi, this is an excellent video, can you tell me where you found the formula? I need to cite it in my math IA (IB Math project) Thank you, this has been the easiest formula to follow, can not seem to find a similar formula to yours.

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Thanks :)

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[@patrickjmt]

thanks!

[@Anteater23]

A tip for people out there who can't remember the formula for b_1:
The bottom line is the same as the top line but with y's replaced with x's :)

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Brilliant!!! This video helps me a a lot.
Actually I'm now having problem with curve fitting by using least square. Could you elaborate on this topic? I'll be so thankful !

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How to find out beta coefficients and the intercept when there are more than one independent variable? Say the equation looks like: Y=Bo + Beta1(X1) + Beta 2 (X2)

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Wonder why the professors make it seem so hard though...

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really helpful, both this one and part 1. Thanks !!

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Thank you so much! This helped out in calculus soooo much

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any plan to make videos about multiple regression , logistic , and finally exponential regression ?

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where can I have more exercices please ?

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last part of example when you subtracted before dividing, isn't that against order of operations

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[@saitaro]

Is it garanteed that the sum of least squared is always not more than the sum of absolutes? Thank you

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Helped me a lot. Thx.

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[@smmuzza5595]

Thanks for this, works well in some code I wrote up! Do you have a derivation of b0 and b1 anywhere?

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Are b0 and b1 the same as θ0 and θ1 they teach in Machine Learning Linear Regression?

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Thank you so much, this was super helpful!!

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Perfect video. Thank you very much !

[@tobu4us]

I tried it using the other formula and my B1 = 0 and B0 = 120, did I do something wrong?

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Great job, really helpful.

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Do you mind if I ask which textbook you're referencing?

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i don't remember. any stats book would have this though

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When y=alnx+b How to calculate ?

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