Question

1. Give the experimental line a real test. Come up with an n so that if the experimental line produces n chips with failure rate 6/38 or less, then the probability of getting a failure rate 6/38 or less under the original production system is less than 0.01.

2. If two random variables have the same generating function, must they have the same cumulative distribution function?

L.9) Central Limit Theorem Central Limit Theorem Version 1 says Go with independent random variables XI, X2, X3, , Xn, all with the same cumulative distribution function so that μ-Expect(Xi] = Expect [X] and r'-var[X]=Var[x,] for all i and j Put s[n] = As n gets large, the cumulative distribution function of S[n] is well approximated by the Normal[0, 1] cumulative distribution function. Another version of the Central Limit Theorem used often in statistics says Go with independent random variables (Xi, X2. X3, ..., Xn, ...] all with the same cumulative distribution function so that: μ-Expect[Xi]-Expect[x,] and σ2-Var[Xi]=Var[X] for all i and j . Put SampleAverage n] As n gets large, the cumulative distribution function of SampleAverage[n] is well approximated by the Normal μ cumulative distribution function. Explain how the second version is a direct consequence of the first version.

Answer 1

note: as i don't have knowledge i could not solve first bit for answer please paste it separately thank soo much