Suppose that, for some N, the continuous random variables X₁, X2,..., XN are independently drawn from the uniform distribution on [−1,1]. That is, each Xį ~ Uniform([−1,1]). Let Sn = X₁ + X2 + ... + XN denote the sum of these uniform random variables. The Central Limit Theorem allows us to approximate the true sum Sy as a Gaussian random variable we'll call G. (It is up to you to find the mean and variance of G.) Notice that since each X₂ is bounded, Sy is also bounded. In particular, the true sum SN can only take values between -N and N. However, the Gaussian approximation to Sy is not bounded: G has some small probability of taking values greater than N (or less than -N). Find an expression for P(|G| > N). Your answer should be in terms of N, and it should involve the Gaussian cdf function (). Hint: for large values of N, P(|G| > N) goes to 0.

College Algebra (MindTap Course List)
12th Edition
ISBN:9781305652231
Author:R. David Gustafson, Jeff Hughes
Publisher:R. David Gustafson, Jeff Hughes
Chapter8: Sequences, Series, And Probability
Section8.7: Probability
Problem 39E: Assume that the probability that an airplane engine will fail during a torture test is 12and that...
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Suppose that, for some N, the continuous random variables X₁, X2,..., XN
are independently drawn from the uniform distribution on [1,1]. That is, each X₂
Uniform ([-1,1]). Let SN = X₁ + X₂ + ... + XN denote the sum of these uniform random
variables.
The Central Limit Theorem allows us to approximate the true sum SN as a Gaussian random
variable we'll call G. (It is up to you to find the mean and variance of G.)
Notice that since each X, is bounded, Sy is also bounded. In particular, the true sum SN
can only take values between -N and N. However, the Gaussian approximation to Sy is not
bounded: G has some small probability of taking values greater than N (or less than -N).
Find an expression for P(|G| > N). Your answer should be in terms of N, and it should
involve the Gaussian cdf function Þ(.). Hint: for large values of N, P(|G| > N) goes to 0.
Transcribed Image Text:Suppose that, for some N, the continuous random variables X₁, X2,..., XN are independently drawn from the uniform distribution on [1,1]. That is, each X₂ Uniform ([-1,1]). Let SN = X₁ + X₂ + ... + XN denote the sum of these uniform random variables. The Central Limit Theorem allows us to approximate the true sum SN as a Gaussian random variable we'll call G. (It is up to you to find the mean and variance of G.) Notice that since each X, is bounded, Sy is also bounded. In particular, the true sum SN can only take values between -N and N. However, the Gaussian approximation to Sy is not bounded: G has some small probability of taking values greater than N (or less than -N). Find an expression for P(|G| > N). Your answer should be in terms of N, and it should involve the Gaussian cdf function Þ(.). Hint: for large values of N, P(|G| > N) goes to 0.
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