The Euler totient function : N→ N is defined by o(n) = #{k≤n: gdc(n, k) = 1}. That is, (n) is the count of all positive integer k that is relatively prime to n. E.g., (9) = 6, (1) = 1. Let X be a random number uniformly chosen from {1,2,..., 20}, and Y = o(X). (a) Find the range of Y. (b) Find the probability mass function of Y. Present your answer in a table, e.g., ke range(Y) Py (k) 1 2 ⠀ (c) Compute the expected value and variance of Y. (Hint: excel could be helpful.)
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- Let X1,X2,... be a sequence of identically distributed random variables with E|X1|<∞ and let Yn = n−1max1≤i≤n|Xi|. Show that limnE(Yn) = 0Let X1, X2, X3, . . . be a sequence of independent Poisson distributed random variables with parameter 1. For n ≥ 1 let Sn = X1 + · · · + Xn. (a) Show that GXi(s) = es−1.(b) Deduce from part (a) that GSn(s) = ens−n.Sketch the ensemble, that is, realizations of the random process X(t) = A cos(2πft), where f is a uniform random variable U(200, 300). That is, f is uniformly distributed in the range [200, 300] Hz and A = 7 is a constant.
- Use the moment generating function to solve. Let X1, . . . , Xn be independent random variables, such that Xi ∼ Poiss(λi), for i = 1, . . . , n.Find the distribution of Y = X1 + · · · + Xn.Let Xi be arandom sample from U(0,1)prove that Xn’ convarges in probability to 0.50According to the Central Limit Theorem, the distribution of sample means is NOT normal if _________________. A) The population from which the samples are selected is not normally distributed and the sample size is small (n < 30). B) The population from which the samples are selected is not normally distributed and the sample size is large (n >= 30). C) The population from which the samples are selected is normally distributed and the sample size is small (n < 30). D) The population from which the samples are selected is normally distributed and the sample size is large (n >= 30).
- For any continuous random variables X, Y , Z and any constants a, b, show the following from the definition of the covariance:Let U₁,..., Un be i.i.d. random variables uniformly distributed in [0, 1] and let Mn = max Ui . 1<i<n Find the cdf of Mn, which we denote by G (t), for t = [0, 1]. For t = [0, 1], G (t) = Now, let Fn (t) denote the cdf of n (1 - Mn); for t > 0, compute lim F₁ (t) =Suppose that {Xn, n ≥ 1} is a sequence of discrete random variables with distribution functionP(Xn =i/n) = 2i/(n(n + 1)), for i = 1, 2, ..., n.Let X be a continuous random variable with density functionfX(x) = 2x for 0 < x < 1,0 , otherwise(a) Compute P(X ≤ x) for 0 < x < 1;(b) Compute P(Xn ≤m/n) for some integer m such that 1 ≤ m ≤ n;(c) Compute P(Xn ≤ x) for 0 < x < 1;(d) Prove that Xnd→ X.
- Let X1,…,Xn be i.i.d. uniform random variables in [0,θ], for some θ>0. Denote by Mn=maxi=1,…,nXi. Compute the cumulative distribution function Fn(t) of n(1−Mn/θ) for fixed t∈[0,n] and any positive integer n. Fn(t)= Compute the following limit. limn→∞Fn(t)= Next, we will use the previous question to find an interval I of the form I=[Mn,Mn+c], that does not depend on θ and such that P[I∋θ]→.95, as n→∞. The strategy now is to use a plug-in estimator for θ to replace it in the expression for c. Parts (a) and (b) suggest that we use c of the form (tn)Mn, where t ought to equal a certain value in order for P[I∋θ]→.95. What is the appropriate numerical value of t? t=Let Xi be IID random variables which have the same law as X. Let L(t) = E(e^tX.) Suppose that this is well defined for t ∈ [−1, 1]. Express the moment generating function of the Sum from i=1 to k Xi in terms of k and LIf a random variable X has a discrete uniform distribution. fx(x)=1/k for x=1,2,..,k;0 otherwise. Derive P.G.F of X and compute E(2x+1)