2. Let X₁, X2, ..., Am be a random sample from a distribution with mean μ and standard deviation ₁, and let Y₁, 2, ‚Y be a random sample from another distribution with mean ... ΣΧ ΣΥ represent the sample means. m n and standard deviation σ₂. Let X = and y = - a) Use rules of expected value to show that X - Ỹ is an unbiased estimator of μ₁ −μ₂. b) Use rules of variance to obtain an expression for the variance and standard deviation of the estimator X-Ỹ. (You may assume the samples are independent of one another.)

A First Course in Probability (10th Edition)
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ISBN:9780134753119
Author:Sheldon Ross
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Chapter1: Combinatorial Analysis
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2. Let X₁, X2, ..., X be a random sample from a distribution with mean μ, and standard
deviation o₁, and let Y₁, Y2, ..., Yn be a random sample from another distribution with mean
ΣΧ.
ΣΥ
represent the sample means.
m
n
and standard deviation σ₂. Let X :
=
and y
=
a) Use rules of expected value to show that X - Y is an unbiased estimator of µ₁ −μ₂.
b) Use rules of variance to obtain an expression for the variance and standard deviation of
the estimator X-Y. (You may assume the samples are independent of one another.)
Transcribed Image Text:2. Let X₁, X2, ..., X be a random sample from a distribution with mean μ, and standard deviation o₁, and let Y₁, Y2, ..., Yn be a random sample from another distribution with mean ΣΧ. ΣΥ represent the sample means. m n and standard deviation σ₂. Let X : = and y = a) Use rules of expected value to show that X - Y is an unbiased estimator of µ₁ −μ₂. b) Use rules of variance to obtain an expression for the variance and standard deviation of the estimator X-Y. (You may assume the samples are independent of one another.)
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