Profam so happy I keep learning simpler methods to solve problems from u...kindly do a talk on bayesian filters and kalman filters..the very basics of the concepts
2:12 Why is your standard deviation for x1 = 0.61? If you sum [2.5, 2.0, 1.7, 1.4, 1.2, 0.9, 0.8] you get 10.5, then summing the squared differences gets you 0.32, then the square root of that is 0.56. I didn't get 0.61 for my standard deviation calculation of those samples.
If you compute the SD for a sample, you calculate the sample standard deviation by dividing by n-1 (not n): ruclips.net/video/pLH1QA4F9uE/видео.htmlsi=fxdiTBQDmUv3SlRo
This channel is the best. This is where I really learn Statistics.
Keep up the good work.
i think this channel should get more attention
it provides the core infos in statistic with subtitle in the vid
thanks asual
A very clear and well illustrated example and explanation. Great work!
Excelente explicación, gracias por compartir tu conocimiento, me ayudo a entender mejor la relación que existe entre dos variables continuas. 👍
Profam so happy I keep learning simpler methods to solve problems from u...kindly do a talk on bayesian filters and kalman filters..the very basics of the concepts
what a wonderful explanation, thank you so much!!!
These videos are very helpful!
Basic example but good ! thanks :)
I have never met a more goated individual.
2:12 Why is your standard deviation for x1 = 0.61? If you sum [2.5, 2.0, 1.7, 1.4, 1.2, 0.9, 0.8] you get 10.5, then summing the squared differences gets you 0.32, then the square root of that is 0.56. I didn't get 0.61 for my standard deviation calculation of those samples.
If you compute the SD for a sample, you calculate the sample standard deviation by dividing by n-1 (not n):
ruclips.net/video/pLH1QA4F9uE/видео.htmlsi=fxdiTBQDmUv3SlRo
@@tilestats Oh! Good to know, thanks!
I get different standard deviation values, the means are in line. Great video by the way...
Can you show your calculations?
Here you go, I may be wrong of course
np.sqrt(clf_gsn.fit(X_cancer, y_cancer).sigma_))
@@tilestats
Simply calculate the SD of each group like:
import numpy as np
X_cancer = np.array([4.1, 3.4, 2.9, 2.8, 2.7, 2.1, 1.6])
y_cancer = np.array([2.5, 2.0, 1.7, 1.4, 1.2, 0.9, 0.8])
print(np.std(X_cancer))
print(np.std(y_cancer))
I get 0.756 and 0.566 the same as before, but that is not what is in the video, they are 0.82 and 0.61 there@@tilestats