a) Find the value of the constant k. b) P (X> 1⁄2) =?

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A: Let X be a random variable. That has the cumulative distribution function FXx=1-e-x-abx>a0x≤a.…

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A: Sol

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Q: For a random variable which can be defined using the probability density function below, what is the…

A: It is an important part of statistics. It is widely used.

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A: Solution :-

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A: As per guidelines only 3 sub parts can be solved, please upload the remaining separately. The…

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A: Given :  X P(X) 200 0.6 500 0  Expected value ,E(X) = μ=∑X.P(X)…

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A: xF(x)f(x)x3+4x3+4x3+4f(x3+4)40.110.11688.24620.907150.210.1012911.35781.135860.540.3322014.83244.894…

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A: As per bartlby guidelines we can answer only first three subpart and rest can be reposted

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Q: a- Find the joint probability density function of (X(1), X(n).

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Q: The weekly demand for Cola, in thousands of liters, from a local chain of efficiency stores, is a…

A: In this case, the random variable X defines the weekly demand of cola.

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A: Hello! As you have posted more than 3 sub parts, we are answering the first 3 sub-parts.  In case…

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A: Farmula used E(Y)=∑YP(Y=y) Variance=V(Y)=E(Y2)-(E(Y))2

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A:

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Q: the joint density: fX,Y(x,y) = 2−x−y,    for 0<x<1, 0<y<1                   0,…

A: Dear students as per guidelines we answer the first three subparts only.

Q: The joint probability mass function is given below: Y = 0 Y = 1 Y = 2 X = 0 X = 1 P(X|Y=2) = ? 1/6

A:

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Q: O The probability density function is f(x) = 2e-kx %3D i) Standard deviation of x = ?. %3D

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Q: The probability mass function of the r.v.x is -2 f(x) = P(X=x) 0.15 0.45 0.3 0.10 %3! The standard…

A: E(X)=∑iXi P(X)E(X)=(-2×0.15)+(2×0.45)+(1×0.3)+(3×0.10)E(X)=-0.3+0.9+0.3+0.3E(X)=1.2

Q: x, e 2a f(x; λ) ,x > 0 e » otherwise

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Q: The expected values of the independent random variables x and y (E(x) and E(y)) for a joint density…

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Continuous Probability Distributions

Probability distributions are of two types, which are continuous probability distributions and discrete probability distributions. A continuous probability distribution contains an infinite number of values. For example, if time is infinite: you could count from 0 to a trillion seconds, billion seconds, so on indefinitely. A discrete probability distribution consists of only a countable set of possible values.

Normal Distribution

Suppose we had to design a bathroom weighing scale, how would we decide what should be the range of the weighing machine? Would we take the highest recorded human weight in history and use that as the upper limit for our weighing scale? This may not be a great idea as the sensitivity of the scale would get reduced if the range is too large. At the same time, if we keep the upper limit too low, it may not be usable for a large percentage of the population!