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.
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
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.
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)
![Linear Regression 1 [Python]](http://i.ytimg.com/vi/q6ksri0LeDE/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
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.
Just got your book! can't wait to dig in
Awesome! Thank you!
wow, good job! I take back what i said about you lacking examples. this is top stuff
Thank you for the excellent explaination, looking Forward for more Video
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
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.
9:18 More like "the danger of mutable variables" :) Thanks for sharing, keep it up
Really useful, thanks!
just out of curious. Is that a 13'' macbook or 16'' macbook?
Thank you ❤
Great video!
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?