ECON 2500 Lecture Notes - Lecture 8: Central Limit Theorem, Standard Deviation, Statistic

• School: York University
• Department: Economics
• Course: ECON 2500
• Professor: Andrei Semenov

Document Summary

It might appear that resampling creates new data out of nothing. First of all, (cid:449)e do(cid:374)"t rel(cid:455) o(cid:374) the for(cid:373)ula s/ (cid:374) to estimate the standard deviation of x. Find the mean and standard deviation of the x "s in the usual way: these formulas go all the way back to chapter 1. To make clear that these are the mean and standard deviation of the means of the b resamples: we will often apply the boots trap to statistics other than the sample mean. Here is the general de (cid:374)itio(cid:374): a(cid:374)other thi(cid:374)g that is (cid:374)e(cid:449) is that (cid:449)e do(cid:374)"t appeal to the (cid:272)e(cid:374)tral li(cid:373)it theore(cid:373) or other theor(cid:455) to tell us that a sampling distribution is roughly normal. We look at the bootstrap distribution to see if it is roughly normal (or not). Save the resample mean into a variable. : make a histogram and normal quantile plot of the 1000 means.