Bias and MSE

[@davidbalakirev5963]

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

[@Maha_s1999]

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

[@heidiclayton9612]

Clear with good examples. Thank you!

[@ActuaryEducation]

I'm so pleased you found it helpful.

[@theuser810]

This video is a lifesaver!

[@muhammadaliimran2331]

Short and to the point. Thank you

[@julioarciga7196]

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

[@PeachyGamingg]

Thank you for this wonderful video!

[@j83lin]

Very informative video! Thank you so much!

[@Mahlodi_Makobe]

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

[@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

[@PedroRibeiro-zs5go]

Thanks for the video! It was pretty good!!

[@amitpadgaonkar8830]

Amazing explanation. Thank you

[@zgbjnnw9306]

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

[@noahrubin375]

This was premium content. New sub :)

[@ActuarialTutor]

So pleased you found it helpful.

[@maxwinmax]

Thank you, this video was very clear and short!

[@ActuaryEducation]

I'm glad you found it helpful.

[@cratersonmoon5399]

Thanks ,you explained it very well

[@Lovelifeplease]

A very good video

[@ActuaryEducation]

I'm so pleased you found it helpful.

[@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.

[@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?

[@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

[@DenisG631]

Simply golden! Thank you!

[@ActuaryEducation]

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

[@MasterPsyi]

This is gold!

[@MIKE-cs3dk]

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

[@gustavoborba8112]

Awesome vid

[@priyak2303]

Thanks a lot❤

[@janetchela7585]

Where is the example for the biased estimators?

[@the_milkyway6494]

Thanks a milion!

[@roottwo5459]

great stuff

[@morakano3355]

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

[@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?

[@mesydiahariani9570]

great, it is easy to understand

[@ayesigasteven8058]

A real professor

[@madwichery]

Thank you!

[@sadrafattouh]

thank you so much!!

[@boyawei15]

save my life!!

[@Kyaint]

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

[@MuslimaAhamed-w8q]

Clark Robert Lee Linda Thompson Anthony

[@rohitarora3814]

The way explained is very difficult to understand

[@dyyddson]

Thank you, an excellent video of the subject!