I recommend watching the regression sections multiple times; tons of points to learn and to look in the references. Extraordinary tutorials, thanks Steve.
9:18 I think b = a*x would be more accurate than b = anoisy*x. After all, it is the true factor a that has an influence on b, not the noisy measurement we took of a. I think this is the reason why the plot looked so much better than expected, also at 7:58, the noise was added to a before calculating b from a, so the noise didn't influence the relationship between a and b.
How can one calculate the uncertainty with svd? Say that we have uncertainty in both a and b, based on the uncertainty the slope and intercept of y should have uncertainty as well, can you please elaborate how to calculate the uncertainty in the slope and intercept. Thanks in advance.
the first method demon [time 6:45 - 7:19] is problematic explained as following: [U,S,V]=svd(a,'econ') input a doesn't change slope, so [U,S,V] won't change characteristics, xtilde=V*inv(S)*U'*b won't change its distribution from b's characteristics
When using the SVD command, you input your array a as a 1d array. This doesn't work in python. Does anyone know how to do this in python with a 1d array? Thank you
![Linear Regression 2 [Matlab]](http://i.ytimg.com/vi/_HTjxl_sdUA/mqdefault.jpg)
![Linear Regression 2 [Matlab]](/img/tr.png)
![Principal Component Analysis (PCA) 1 [Python]](http://i.ytimg.com/vi/Oi4SJqJIL2E/mqdefault.jpg)
I recommend watching the regression sections multiple times; tons of points to learn and to look in the references. Extraordinary tutorials, thanks Steve.
i replay the video after reading your comment I need to watch it again after testing on the real data
wow, good job! I take back what i said about you lacking examples. this is top stuff
Just got your book! can't wait to dig in
Awesome! Thank you!
9:18 I think b = a*x would be more accurate than b = anoisy*x.
After all, it is the true factor a that has an influence on b, not the noisy measurement we took of a.
I think this is the reason why the plot looked so much better than expected, also at 7:58, the noise was added to a before calculating b from a, so the noise didn't influence the relationship between a and b.
Thank you for the excellent explaination, looking Forward for more Video
Thank you ❤
Really useful, thanks!
How can one calculate the uncertainty with svd?
Say that we have uncertainty in both a and b, based on the uncertainty the slope and intercept of y should have uncertainty as well, can you please elaborate how to calculate the uncertainty in the slope and intercept.
Thanks in advance.
Great video!
9:18 More like "the danger of mutable variables" :) Thanks for sharing, keep it up
just out of curious. Is that a 13'' macbook or 16'' macbook?
the first method demon [time 6:45 - 7:19] is problematic explained as following: [U,S,V]=svd(a,'econ') input a doesn't change slope, so [U,S,V] won't change characteristics, xtilde=V*inv(S)*U'*b won't change its distribution from b's characteristics
When using the SVD command, you input your array a as a 1d array. This doesn't work in python. Does anyone know how to do this in python with a 1d array?
Thank you
I understood what the problem was when watching the corresponding video in Python.
x=a\b
First!
After we added some noise to a, should we run SVD with the noisy a rather than a in the 11th commend of the MatLab?