So White Noise is about 5% Unpredictable so is considered Unpredictable. My question is WHEN is it actually unpredictable in an example? When does this 5% show up as unpredictable? Thank you.
Thank you for these amazing videos, please may I request if possible, can these videos related to Time Series be put under one playlist so there is a pattern that can be followed to understand the concepts & thank you once again for these simple explanations - amazing.
First time someone using words for explaining something in statistic and not only symbols. Maths is not difficult the only problem there are teach for some kind mind...
This video really allowed me to connect some dots here - this concept is also why the assumptions in linear regression are so concerned with the residuals (which was counterintuitive to me when I first learned about it - "why make assumptions about the random component?")... but now it makes sense - if you create a model and are only left with white noise in the residuals, you can say that your model doesn't omit some pattern in the data and thus it captures all the key information you can get from your data. Thanks!
No advanced presentation software, no Python or R and the simplicity that you've used to illustrate the idea is fantastic. Thank you for the clarity.
So White Noise is about 5% Unpredictable so is considered Unpredictable. My question is WHEN is it actually unpredictable in an example? When does this 5% show up as unpredictable? Thank you.
The sum of 2 white noises is a white noise ?
Thank you for these amazing videos, please may I request if possible, can these videos related to Time Series be put under one playlist so there is a pattern that can be followed to understand the concepts & thank you once again for these simple explanations - amazing.
If a white noise is stationary, doesn't that mean we can capture and model it? And isn't stationary signal predictable?
What's lag😭
amaizing hope you were my teacher instead of my actual one
but how do you find out the white noise?
Is white noice and iid noise is same?
First time someone using words for explaining something in statistic and not only symbols. Maths is not difficult the only problem there are teach for some kind mind...
HOW - CAN - YOU - EXPLAIN - IT - SO - WELL (T_T)
I haven't seen in any of his videos requesting for like and subscribe. His work pretty much gets that!! Hats off to you man!
You explain really well these concepts. They actually sink in, thanks a lot!
This video really allowed me to connect some dots here - this concept is also why the assumptions in linear regression are so concerned with the residuals (which was counterintuitive to me when I first learned about it - "why make assumptions about the random component?")... but now it makes sense - if you create a model and are only left with white noise in the residuals, you can say that your model doesn't omit some pattern in the data and thus it captures all the key information you can get from your data. Thanks!
Thank you very much for carring out and uploading this very useful video!
After three weeks of trying to understand this... all I needed was this video! Thank you!
was about to cry over my time series hw then i found these vids. feeling a lil better now thank you
You cannot imagine how it took for others to explain it. They ended up with something that cannot be understandable though! Thank you
every your video is so clear. Straight to points! Thanks
This is by far the greatest explanation of White Noise present over the Internet !