Q3// 'The joint probability mass function of two discrete random variables X and Y is given (k(2x, + y) X, = 1, 2; y, = 1. 2 otherwise where k is a constant. (a) Find the value of k. (b) Find the marginal pmf's of X and Y. (c) Are X and Y independent?

Q: Y1 0 zero elsewhere. Find E(Y1)

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Q: Q2/A/ Two random variables X and Y with joint probability distribution given by: x + 2y f(x,y) = 60…

A: Hello! As you have posted 4 sub parts, we are answering the first 3 sub-parts.  In case you require…

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A:

Q: A random variable W has range RW = {1, 2, 3, . . . } and the probability mass function given by…

A: The provided information is as follows:The  is a random variable having a range .The probability…

Q: 3. x and y are independent zero mean Gaussian random variables with variance of and o3. Let z = (x +…

A:

Q: Sk(2x, + y;) x, = 1, 2; y; = 1, 2 Pxr(x1, y;) = otherwise where k is a constant. (a) Find the value…

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

Q: 5. (8 pts) X is a random variable with pdf f(x) = {} [Ae-Ax x>0 o/w 0 and let Y = 1 - X/2. Find the…

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Q: 2. Let X be a random variable with density function f(x) = = √ (2x3 -8x2 +5x+6), 1<x<2, 0, elsewhere…

A: given function is f(x)=

Q: None

A: Step 1:Step 2:Step 3:Step 4:Option(E) : correct

Q: (a) Let X be a random variable with PDF ƒ(x) = 1−|x|, |x| ≤ 1. Obtain the MGF of X. (b) Find the PDF…

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Q: If E(X) = 6 and E[(X)(X-1)] = 51,  a) find the second moment  b) find the variance of X Please…

A: Here we need to find the second moment and the variance.

Q: 2. The continuous random variable X has density ta f(x) = otherwise (a) What is a? (b) What is the…

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Q: Let the joint pdf for the continuous random variables X and Y be: f(x,y) = { 6x2 ; 0 0.8 | X =…

A: (i) To find P(X=0.6, Y=0.6), you need to calculate the probability that both X and Y equal 0.6.…

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.

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!