Can you explain more precisely why the P[Y=g^-1(x)], for a decreasing function? Visually it seems to make sense, but is there a geometric and/or calculus explanation? PS. Great video!
The last example is intuitively interesting: if your lambda is 1, then f_{Y}(y) = 1/3*e^{-1/3 * y}. It's like the new lambda = 1/3, which is scaling the mean by 3.
please can you solved this to me i love the approach of teaching. A continues uniformly distributed random variable X has two parameter which are the end point its range(a,b) if E(X) =5 and var (X) =3 find the value of a, and b
I am stuck at this question for my next video. This question came in GATE exam in INDIA. "A Zero mean Gaussian Noise of variance N is applied to a half wave rectifier.The mean squared value of the rectifier output will be???" i think in this question we would have to use the transformation output Y= X when x>0 and Y=0 when x
Great video, thank you for your positive energy.
Blessings and Love,
Dashama
You explained to me what no one could my entire semester.
I love you
best explanation on youtube (and I've done a lot of searching!)
This is great. Please keep this going!
This is better explained than the lecture in the university. Well done and thx!
best explained in YT. thank you
HUGE SHOUTOUT TO YOU! HELPED ME ON THIS ONE!!!!!!!!!!!!!!!!!
Thanks! Can you make a video for a transformation of a joint distribution of a random variable?
I'll make one. You just need to learn about area between two vectors.
Thank you for the video. Well explained! =)
Very well explained. I finally understood, thanks to you.
Can i find the decreasing case example?
@8:54, on left side, how come differentiating F_X with respect to y, gives PDF f_X. There should be some counter term like, dx/dy.
Got it. dx/dy is d(g-1(y))/dy
Thank you so much, you explain everything very well. Do you have any videos on joint distributions and joint distributions involving transformations?
This is the only video that clicked for me. I feel like all other explanations for the continuous case skip some essential steps.
Can you explain more precisely why the P[Y=g^-1(x)], for a decreasing function? Visually it seems to make sense, but is there a geometric and/or calculus explanation?
PS. Great video!
Because it is a decreasing function, chance of Y = x.
realy thanks. it help me alot.
Why do we even need to use cdf? I mean, why can't we simply replace X in the equation by g^(-1)(y) in the pdf?
The last example is intuitively interesting: if your lambda is 1, then f_{Y}(y) = 1/3*e^{-1/3 * y}. It's like the new lambda = 1/3, which is scaling the mean by 3.
awesome video!
perfect explaination. thanks a lot
Really Helpful!!!! Thanks
Thank you! very helpful
This is excellent!
Thank you ❤.
Ur sickkkkk thank you very much
wish i saw this years ago without using the terms monotonic and laplace .. the power of simple sketches
Sir please do tutorial on hypothesis and statistics related topics 🇧🇩
thnk you... so much
Exactly what I needed
please can you solved this to me i love the approach of teaching. A continues uniformly distributed random variable X has two parameter which are the end point its range(a,b) if E(X) =5 and var (X) =3 find the value of a, and b
Chukwuemeka Chukwudi hycent this is a simultaneous equation. Just set 5= (a+b)/2 and 3=(b-a)^2/(12).
I am stuck at this question for my next video. This question came in GATE exam in INDIA.
"A Zero mean Gaussian Noise of variance N is applied to a half wave rectifier.The mean squared value of the rectifier output will be???"
i think in this question we would have to use the transformation output Y= X when x>0 and Y=0 when x