Let X and Y be continuous random variables with a joint probability density function (pdf) of the form f(x,y) = {k(x+y), 0≤x≤ysi 0, elsewhere Find: a) Show that the value of k = 2 so that f(x, y) is a joint pdf. b) the marginal of X and Y. c) the joint cumulative density function (CDF), F(x,y). d) the conditional pdf of Y given X.
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- Let X,Y be two random variables with joint probability density function f(x, y) =0< XThe function f(x) is the probability density function, that describes the random variable travel time X. f(x) = {xe®, x>0 0, X ≤0 a.) What is the probability that the travel time is less than 2.5 hours? b.) What is the probability that the travel time is between 1 to 2.5 hours? c.) Find the mean and variance of the given probability density functionLet X and Y be two random variables that are independent and have the same probability density function. If U = max (X, Y) V = min (X, Y), find the distribution function of the U random variable using the distribution function technique.Let X be a random variable with density function - 1< x < 2, fn) = fx*/3, f(x) = 0, elsewhere. Find the expected value and the covariance of g(X) = 4X + 3.Let X and Y continuous random variable with joint pdf fx,y) = 24xy, for 0The life lengths of two transistors in an electronic circuit is a random vector (X; Y) where X is the life length of transistor 1 and Y is the life length of transistor 2. The joint probability density function of (X; Y) is given by x 2 0, y 2 0 fx.,fx.v) = 20 else Then the probability that the first transistor burned during half hour given that the second one lasts at least half hour equals Select one: a. 0.606 b. 0.3935 C. 0.6318 d. 0.3669 e. 0.7772Let X and Y be continuous random variables with joint distribution function, F (x,y). Let g (X,Y) and h (X,Y) be functions of X and Y. PROVE Var(a + X) = Var (X)Let X be a uniformly distributed continuous random variable that can take values in the interval (-1,1). Y Let Y=1-X2 be defined as a function of the random variable X. a) Mathematically find the probability density function (PDF) of Y. b) Find mathematically the covariance, Cov[X,Y], of X and Y. X and Y are uncorrelated (uncorrelated)? Are X and Y independent? c) With the help of the "rand" command in MATLAB, the random variable X is randomized into 1,000,000 random variables. and the corresponding Y=1-X2 values. Plot the histogram of Y. The histogram you found in (a) Does it match the PDF? Comment. d) Estimate Cov[X,Y] using the 1,000,000 X and Y values you generated in MATLAB obtain For this, from the definition Cov[X,Y] = E[(X-E[X])(Y-E[Y])] and the "mean" command Does it match the value you found in (b)? Comment.Determine the conditional probability distribution of Y given that X = 2. Where the joint probability density function is given by f(x,y)= 1 - (x + y) for 1 < x < 4 and 0 < y < 3.Recommended textbooks for youCalculus For The Life SciencesCalculusISBN:9780321964038Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.Publisher:Pearson Addison Wesley,Calculus For The Life SciencesCalculusISBN:9780321964038Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.Publisher:Pearson Addison Wesley,