You have a wonderful teaching ability. From watching your other vids I’ve noticed you’re very good at conveying complex concepts. Thanks for the great explanation in this one
This video has much better audio than the original! @Dane I would add an annotation on the orginal video to redirect people to this one! Thank you for the reupload and the wonderful explanation!
I didn't get how that data of estimators is plotted. my understanding-- let us take sample mean estimator (x1+x2+...../n), as x1,x2,x3.. all are random variables and have different values for each, I am asuming that we plotted estimator by giving the all possible values (one at a time) and plotting them. correct me
Loved the video❤, but I have a question, between an unbiased estimator that has a huge variance and a an biased estimator with little variance, which one is preferred?
I think I would choose a third option. I would create a new estimator using the biased one plus (or minus the bias). Imagine if you new the estimator over estimates by 50. You’d naturally subtract 50 from any estimate given to you by the estimator. For example, the estimator tells you a paint takes on average 2hrs and 50 minutes to dry. In your head you’d say the average is around 2hrs then.
I've taken four or five college-level courses on statistics and this is the first time I've really understood what 'unbiased' means. 2:48. Thank you!
That’s great to hear! Glad I could help!
+1
Wut lol, were you asleep in class the entire time?
Well explained, perfect timing, and concise! Bravo. This is how classes/youtube videos should be.
Thank you!!
Great explanation. Even struggling in Stats, I could follow along easily. Thank you
Great explanation Highly appreciated Professor ❤❤❤
You have a wonderful teaching ability. From watching your other vids I’ve noticed you’re very good at conveying complex concepts. Thanks for the great explanation in this one
Thanks so much!
This video has much better audio than the original! @Dane I would add an annotation on the orginal video to redirect people to this one! Thank you for the reupload and the wonderful explanation!
Nice explanation. Liked the Bullseye analogy for explaining the spread.
Yes, very tangible. Relates to everyday understanding.
I didn't get how that data of estimators is plotted.
my understanding--
let us take sample mean estimator (x1+x2+...../n), as x1,x2,x3.. all are random variables and have different values for each, I am asuming that we plotted estimator by giving the all possible values (one at a time) and plotting them. correct me
Nice explanation of the concept. Thank you.
Loved the video❤, but I have a question, between an unbiased estimator that has a huge variance and a an biased estimator with little variance, which one is preferred?
I think I would choose a third option. I would create a new estimator using the biased one plus (or minus the bias). Imagine if you new the estimator over estimates by 50. You’d naturally subtract 50 from any estimate given to you by the estimator. For example, the estimator tells you a paint takes on average 2hrs and 50 minutes to dry. In your head you’d say the average is around 2hrs then.
Great explanation! Thank you!
Excellent!
Thanks mate!
You're welcome
SUPER LIKE!!
Thank you!!
Thank you!
You’re welcome!