Yes, my boer brother! You give us a good name. Well educated
@marounsader318
3 ай бұрын
this what we call a math teacher!
@imdadood5705
3 жыл бұрын
I am data Science student. I wanted to see how this was derived. This summed it up perfectly! Thanks
@bencepaul3497
2 ай бұрын
Excellent explanation. Just what I needed!
@scorpio19771111
3 жыл бұрын
Excellent explanation and demo! 👏🏻👏🏻👏🏻 Thank you so much. This video deserves to be much much higher in youtube search results!
@viveksavita06
3 жыл бұрын
I have seen many videos but this one explain a bit in more details like formulas used in linear regression. Great work!
@chariezwane3981
3 жыл бұрын
Thank you so much for making this video. You just saved my Econometrics behind today.
@BoerCommander
3 жыл бұрын
Glad to help Charie! It brings me joy to know that I am helping.
@robharwood3538
3 жыл бұрын
Nice summarization and explanation of what the matrix form for simple linear regression model looks like, what it is made from, and how it can be constructed. Thanks! You have a good presentation/teaching style, IMO. I hope your channel grows, to help more people. Thanks again!
@pratik.patil87
Жыл бұрын
I was looking for this breakdown for a long time. Thanks a lot mate
@meghajessica
Жыл бұрын
One of the best education videos!!!!! GOD BLESSSS YOUU & YOUR BEAUTIFUL FAMILY BROTHER !!!!!!!!! PLS KEEP UP THE GOOD WORK!
@changeme454
10 ай бұрын
Wow! I have been searching this lessons! I just found it and understand your easy way of explanation. 🙏 I keep flowing your channel.
@liamhoward2208
3 жыл бұрын
Great and Elegant explanation. Thank you
@mannur2248
3 жыл бұрын
Keep up the neat work. Really good work.
@SNawaz-bk7py
Жыл бұрын
Thanks man. You are a lifesaver
@effortlessjapanese123
Жыл бұрын
hey! you were in my recommendation again!
@BoerCommander
4 жыл бұрын
0:00 Introduction and Design Matrix 02:00 Beta Hat Formula 02:42 The matrix X'X 06:24 Inverse of X'X 09:28 The matrix X'Y
@AakashSingh-qu8hk
Жыл бұрын
This helped a lot Thank you so much
@myeshafarzanatahi9154
10 ай бұрын
Thank you so much
@effortlessjapanese123
Жыл бұрын
this is amazing! where are you from?
@rachadlakis1
2 жыл бұрын
Thanks!!!!
@lofibeatz990
Жыл бұрын
I did not understand 8:52 how was sum of xi sqared and n times xbar squared equal to sum of xi-xbar squared it should be xi^2 -xbar only let me explain you with an example if suppose x is -2 and mean is 1 then xi^2 - xbar would give you 3 while (xi-xbar)^2 will give you 9 that is (-2-1)^2 ..... on expanding (xi-xbar)^2 would be xi^2 - 2xixbar +xbar^2 not xi^2 - xbar
@gcumauma3319
3 жыл бұрын
Thanks Boer
@BoerCommander
3 жыл бұрын
It is my pleasure to be of service gcuma.
@janslesp
2 жыл бұрын
Somethig strange: the equation ... BETA_HAT=X.I@Y ...in Python (Numpy) give us the same solution of .... BETA_HAT=(X.T@X).I@X.T@Y. Try it!
@BoerCommander
2 жыл бұрын
Hi janslesp, a good foray into the calculations and good work on applying the thinking in python. Have a go with a design matrix x that is not square and you will see that the equation does not work then. Your equation works if the matrix X is an invertible square matrix but it will not work when X is not square. try the example below. x = np.array([[1,2,3,3], [1,5,7,34], [1,4,3,6]]) y = np.array([2,6,7]) # beta_hat runs fine beta_hat = np.linalg.inv(x.T @ x) @ x.T @ y # this line of code will raise an error as x is not #square beta_hat_2 = np.linalg.inv(x) @ y
@janslesp
2 жыл бұрын
@@BoerCommander Hello Boer. I'm trying to understand these rules of this wonderful universe of linear algebra, Python and Machine Learning. You have no idea the pleasure of exchanging information with talents from other countries. I'm not a data science professional and I'm trying to overcome my difficulties. Thank you. Today I went back to playing a little on the computer. I used the code below which, as a rule, my X is not invertible matrix because X is not square and therefore not invertible. But I dont undestand: numpy makes the method ".I" in this case!! Does numpy have any algorithm to calculate a X.I matrix that simulates an inversion? Note that in the final result we get the same result: tetas_strange_equation = tetas_Normal_Equation The code is: import numpy as np X=np.matrix([ [1,35,70,0], [1,15,50,0], [1,42,80,0], [1,25,70,1], [1,28,90,1], [1,12,65,0], [1,34,72,0]]) y=np.matrix([90, 35, 98, 70, 62, 32,68]).T # Strange equation tetas_strange_equation=X.I@y print(tetas) # Normal equation tetas_Normal_Equation=(X.T@X).I@X.T@y print(tetas_Normal_Equation) OUTPUT: [[21.77437371] [ 2.47318883] [-0.37736477] [ 8.8753038 ]] [[21.77437371] [ 2.47318883] [-0.37736477] [ 8.8753038 ]]
Пікірлер: 29