Bias and MSE

[@Maha_s1999]

I have never seen MSE explained better! Thanks so much for the video - subbed 😀

[@davidbalakirev5963]

Finally, a video where instead of just reciting the definitions we see many examples. In less than 8 minutes. Wonderful!

[@maxwinmax]

Thank you, this video was very clear and short!

[@ActuaryEducation]

I'm glad you found it helpful.

[@PedroRibeiro-zs5go]

Thanks for the video! It was pretty good!!

[@DenisG631]

Simply golden! Thank you!

[@ActuaryEducation]

I'm so pleased you found it helpful in your studies.

[@cratersonmoon5399]

Thanks ,you explained it very well

[@Lovelifeplease]

A very good video

[@ActuaryEducation]

I'm so pleased you found it helpful.

[@priyak2303]

Thanks a lot❤

[@morakano3355]

What is q in npq at the end of the lecture?

[@mesydiahariani9570]

great, it is easy to understand

[@JohnnySylvia-x9s]

Lee Laura Jackson Michelle Lopez Steven

[@rohitarora3814]

The way explained is very difficult to understand

[@julioarciga7196]

Am I the only one that wishes their professor would just make it this simple in lecture?

[@gustavoborba8112]

Awesome vid

[@Test-ri2kr]

At 2:02 can someone please explain why Xbar is = mu/n? I know in the next example why as P=X/n but in this example it doesn’t looo like X=mu/n

[@j83lin]

Very informative video! Thank you so much!

[@heidiclayton9612]

Clear with good examples. Thank you!

[@ActuaryEducation]

I'm so pleased you found it helpful.

[@plttji2615]

Thank you for the video, can you help me how to prove that is unbiased in this question? Question: Compare the average height of employees in Google with the average height in the United States, do you think it is an unbiased estimate? If not, how to prove it is not mathced?

[@swaggy745]

if we are given a pdf of 4 values of x with their probabilities in terms of theta, then we find an estimator for the mean theta-hat and then we find the mean square error in terms of theta (should it be in terms of theta?), how can we find if it it mean square consistent. I am unsure because n=4 for my questions so I can't see how it makes sense to consider the limit as n goes to infinity. Please could someone shed some light. Thank you

[@zgbjnnw9306]

THis is so much helpful! Thank you for sharing :) subbed

[@PeachyGamingg]

Thank you for this wonderful video!

[@dyyddson]

Thank you, an excellent video of the subject!

[@Kyaint]

You would think he'd show a biased and unbiased example instead of just unbiased but nothing makes sense with teachers these days.

[@theuser810]

This video is a lifesaver!

[@MuslimaAhamed-w8q]

Clark Robert Lee Linda Thompson Anthony

[@GianaMaxwell-d8x]

Rodriguez Charles White Steven Miller Susan

[@NatashaHayes-dg6fp]

Perez Joseph Davis Ronald Martin Larry

[@SabihaKhatun-j1t]

Brown Jeffrey Anderson Cynthia Harris Scott

[@CamilleDoucet-k2v]

Wilson Cynthia Thompson Timothy Brown Susan

[@boyawei15]

save my life!!

[@haileybecker3621]

Hall Lisa Thomas David Walker James

[@Mahlodi_Makobe]

Short and sweet. I loved this video. may we please get more content like this?

[@alexanderjavadpeygambarian9878]

why do you square 1/n to make it 1/n^2 after taking it outside the Variance

[@ActuaryEducation]

It would be 1/n if it was standard deviation as that has the same dimensions as the data. However, variance is a squared measure.

[@pengtian9924]

According to the definition of variance, we have this formula: Var(aX)=a^2Var(X), where a is any constant.

[@JustifyArriving]

Thompson Donald Johnson Ronald Johnson Richard

[@janetchela7585]

Where is the example for the biased estimators?

[@muhammadaliimran2331]

Short and to the point. Thank you

[@MIKE-cs3dk]

Why do we square the 1/n outside of the variance?

[@amitpadgaonkar8830]

Amazing explanation. Thank you

[@roottwo5459]

great stuff

[@noahrubin375]

This was premium content. New sub :)

[@ActuarialTutor]

So pleased you found it helpful.

[@the_milkyway6494]

Thanks a milion!

[@MasterPsyi]

This is gold!

[@madwichery]

Thank you!

[@mohammedkhan8753]

for the last example the variance of a Bin(n,p) is v(x)=np(1-p) so MSE = p(1-p)/n

[@lokeshdaga8456]

Its same as (1-p) = q

[@inigohohmeyer1791]

At 5:58 why is the n squared?

[@sadrafattouh]

thank you so much!!

[@ayesigasteven8058]

A real professor