Question

Let $X_1,...,X_n$ be $n$ independent Bernoulli random variables. (We don't assume that $X_1,...,X_n$ have the same distribution!) Let $Y_1,..., Y_n$ be another $n$ independent Bernoulli random variables. (We don't assume that $Y_1,..., Y_n$ have the same distribution, either!) Let $X = X_1 +....+X_n$ and $Y = Y_1 +....+Y_n$. Suppose that $P(X_i = 1) ge P(Y_i = 1)$ for all $i = 1, 2,...,n$. Does this guarantee that $P(X ge k) ge P(Y ge k)$ for all $k = 1,2,...,n$? If your answer is yes, prove this statement. If your answer is no, give a counterexample.

          Let $X_1,...,X_n$ be $n$ independent Bernoulli random variables. (We don't assume that $X_1,...,X_n$ have the same distribution!) Let $Y_1,..., Y_n$ be another $n$ independent Bernoulli random variables. (We don't assume that $Y_1,..., Y_n$ have the same distribution, either!) Let $X = X_1 +....+X_n$ and $Y = Y_1 +....+Y_n$. Suppose that $P(X_i = 1) ge P(Y_i = 1)$ for all $i = 1, 2,...,n$. Does this guarantee that $P(X ge k) ge P(Y ge k)$ for all $k = 1,2,...,n$? If your answer is yes, prove this statement. If your answer is no, give a counterexample.
        
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Elementary Statistics a Step by Step Approach
Elementary Statistics a Step by Step Approach
Allan G. Bluman 9th Edition
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Let $X_1,...,X_n$ be $n$ independent Bernoulli random variables. (We don't assume that $X_1,...,X_n$ have the same distribution!) Let $Y_1,..., Y_n$ be another $n$ independent Bernoulli random variables. (We don't assume that $Y_1,..., Y_n$ have the same distribution, either!) Let $X = X_1 +....+X_n$ and $Y = Y_1 +....+Y_n$. Suppose that $P(X_i = 1) ge P(Y_i = 1)$ for all $i = 1, 2,...,n$. Does this guarantee that $P(X ge k) ge P(Y ge k)$ for all $k = 1,2,...,n$? If your answer is yes, prove this statement. If your answer is no, give a counterexample.
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00:01 In this question, we have been given that x1, x2, so on, till x1 is a random sample from continuous uniform population with 0 comma 1.
00:10 And the probability distribution is given us p x of x is equal to 1 when x lies between 0 and 1 and 0 in all other cases.
00:19 Now we can calculate f x of x is equal to x in the interval when x lies between 0 and 1.
00:28 Under joint probability distribution function, joint probability distribution function or the probability density function of y comma yn is written as fy comma ym of y comma y2 is equal to n times of n minus 1 multiplied to fx times of ym 5x times of ym, fx times of ym, power n minus 2 multiplied to fx times of y 1 and f x times of y n.
01:12 Now on further simplifying this we get f y 2 comma yn is calculated as ym.
01:26 Which is equal to n times of n 1 n minus 1 multiplied to y n minus 1 and here, 0 is less than y1, less than yn, which is less than 1.
01:43 Now this is calculated as e .r...
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