MATH 140 Lecture Notes - Lecture 11: Central Limit Theorem, Confidence Interval

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15 Dec 2016
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Central limit theory: if the number of units within a sample is large enough the distribution should be roughly normal. Chart to know when n is large enough: as long as: 0. 1(cid:1095)p(cid:1096)0. 9 the sample size of n=100 is enough. The e (cid:272)a(cid:374) (cid:271)e fou(cid:374)d with p, (cid:271)ut if (cid:455)ou do(cid:374)"t k(cid:374)ow what p is (cid:455)ou (cid:272)a(cid:374) use p in its place, to get the. Formula for the seest (cid:3020)(cid:3021)= (cid:4666)1 (cid:4667)(cid:1866) Seest: estimated standard of error (essentially like standard deviation) p : proportion of successes in the sample n: amount of samples in the sample. In a random sample you can be 95% confident that p and p are less than 1. 96 seest apart: to compute the 95% confidence interval, you need to use 2 different formulas: +1. 96 (cid:4666)1 (cid:4667: the values you get after computations show the interval of which p should fall in. How to get confidence interval on a ti-83/84.

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