Refer to Exercise $3.64 .$ The maximum likelihood estimator for $p$ is $Y / n$ (note that $Y$ is the binomial random variable, not a particular value of it). a. Derive $E(Y / n)$. In Chapter 9 , we will see that this result implies that $Y / n$ is an unbiased estimator for $p.$ b. Derive $V(Y / n) .$ What happens to $V(Y / n)$ as $n$ gets large?

          Refer to Exercise $3.64 .$ The maximum likelihood estimator for $p$ is $Y / n$ (note that $Y$ is the binomial random variable, not a particular value of it).
a. Derive $E(Y / n)$. In Chapter 9 , we will see that this result implies that $Y / n$ is an unbiased estimator for $p.$
b. Derive $V(Y / n) .$ What happens to $V(Y / n)$ as $n$ gets large?

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Elementary Statistics a Step by Step Approach

Allan G. Bluman 9th Edition

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10/23/2022

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Refer to Exercise $3.64 .$ The maximum likelihood estimator for $p$ is $Y / n$ (note that $Y$ is the binomial random variable, not a particular value of it). a. Derive $E(Y / n)$. In Chapter 9 , we will see that this result implies that $Y / n$ is an unbiased estimator for $p.$ b. Derive $V(Y / n) .$ What happens to $V(Y / n)$ as $n$ gets large?