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A final exam for a statistics course, covering topics such as probability density functions, cumulative distribution functions, variance, multiple choice tests, independent variables, poisson processes, normal variables, and beta distributions.
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(a) Find c as a decimal. (b) Give a formula for the cumulative distribution function of X. (c) Find Var(X) as a decimal. (d) Let Y = X^2. Find the probability density function of Y.
(a) Describe the distribution of X by a formula. (b) Give a numerical expression for P (X ≥ 19). (c) Evaluate E(X) as a decimal. (d) Evaluate Var(X) as a decimal.
(a) Find P (X < Y ). (b) Find the probability density function for Z := min(X, Y ), the mini- mum of X and Y.
(a) Describe the distribution of R 1.
∗Transcribed by Chris Pennello on December 21, 2006
(b) Describe the distribution of X 1. (c) Find P (R 1 + R 2 + R 3 + R 4 = 7). (d) Find Var
100 i=1 Xi
(e) Find the approximate value of P
100 i=1 Xi^ ≤^3
(a) E(XY ). (b) The marginal density of X. (c) E(Y |X = x) for 0 < x < 1.
(a) What is the probability that exactly 10 vehicles arrive in a two minute interval? (b) Consider the first vehicle to arrive after time t = 0. What is the probability that this vehicle arrives after time t = 1 and is a truck? (c) Give a formula for the probability density of the length of time be- tween arrivals of the 5th and 7th cars after some fixed time. (d) Sketch a graph of the density found in c), with a properly labeled horizontal axis.
(a) Find P (S < 1). (b) Find the covariance between S and D. (c) Find E(S|D). (d) Find Var(S|D). (e) What is the conditional distribution of S given D = 1?
(a) Give an explicit example of a distribution of X consistent with these properties. (b) What does Markov’s inequaltiy say about P (X ≥ 400)? (c) What does Chebychev’s inequality say about P (X ≥ 400)? (d) Let Sn be the sum of n independent variables, each with the same distribution as X. Find a sequence xn so that P (Sn/n > 100 + xn) converges to 1/4 as n → ∞.