(3) 18 pts] Let Ya and Y, denote Bernoulli random variables from two different populations, denoted...
(3) 18 pts] Let Ya and Y, denote Bernoulli random variables from two different populations, denoted a and b. Suppose that E(%)-Pa and E(%)-pb. A random sample of size na is chosen from population a, with sample average denoted pa, and a random sample of size nb is chosen from population b, with sample average denoted Suppose the sample from population a is independent of the sample from population b (a) Show that E(Pi) Pi and var(Pi)-P( pi)/n, for j...
Let X1 Xn be a random sample of size n from a Bernoulli population with parameter p. Show that p= X is the UMVUE for p. 5.4.22 Let X1 Xn be a random sample of size n from a Bernoulli population with parameter p. Show that p= X is the UMVUE for p. 5.4.22
Question 3 [25] , Yn denote a random sample of size n from a Let Y, Y2, population with an exponential distribution whose density is given by y > 0 if o, otherwise -E70 cumulative distribution function f(y) L ..,Y} denotes the smallest order statistics, show that Y1) = min{Y1, =nYa) 3.1 show that = nY1) is an unbiased estimator for 0. /12/ /13/ 3.2 find the mean square error for MSE(e). 2 f-llays Iat-k)-at 1-P Question 4[25] 4.1 Distinguish...
9.6 in order to compare the means of two populations, inde- NW pendent random samples of 400 observations are selected from each population, with the following results Sample 1 Sample 2 $.240 s2 200 5,275 1150 a. Use a 95% confidence interval to estimate the dif- ference between the population means (μ,-μ Interpret the confidence interval. b. Test the null hypothesis Ho (μι-μ)--0 versus the c. Suppose the test in part b were conducted with the d. Test thenull hypothesis...
1) [6 pts] Let Y be a Bernoulli random variable with success probability Pr (Y 1 )p, and let Y, Yn be iid draws from this distribution. Let p be the fraction of successes (1's) in this sample. (a) Show that p Y. (b) Show that p is an unbiased estimator of p. (c) (1-p)/n Show that var (p)-p
Question 3: Bernoulli distribution (23/100 points) Consider a random sample X1,...,Xn from a Bernoulli distribution with unknown parameter p that describes the probability that Xi is equal to 1. That is, Bernoulli(p), i = 1, ..., n. (10) The maximum likelihood (ML) estimator for p is given by ÔML = x (11) n It holds that NPML BIN(n,p). (12) 3.a) (1 point) Give the conservative 100(1 – a)% two-sided equal-tailed confidence interval for p based on ÔML for a given...
Let X1, X2, .., Xn be a random sample from Binomial(1,p) (i.e. n Bernoulli trials). Thus, п Y- ΣΧ i=1 is Binomial (n,p). a. Show that X = ± i is an unbiased estimator of p. Р(1-р) b. Show that Var(X) X(1-X (п —. c. Show that E P(1-р) d. Find the value of c so that cX(1-X) is an unbiased estimator of Var(X): п
Basic Computation: Confidence Interval for My – M2 Consider two inde- pendent normal distributions. A random sample of size n = 20 from the fire distribution showed x = 12 and a random sample of size n2 = 25 from the second distribution showed X2 = 14. We were unable to transcribe this image(a) Check Requirements If o, and on are known, what distribution does 1 - X, follow? Explain. (b) Given o = 3 and 0 2 = 4,...
3) Suppose X,,X,,X, (n > 1) is a random sample from Bernoulli distribution with Circle out your Class: Mon&Wed or Mon.Evening p.mf. p(x)=p"(I-p)'-x , x = 0,1, , thenyi follows ( ). Binomial distribution B(a.p) eNormal distribution N(p,mp(- O Poisson distribution P(np) Dcan not be determined. 4) Suppose X-N(0,1) and Y~N(24), they are independent, then )is incorrect. X+Y N(2, 5) C X-Y-NC-2,5) BP(Y <2)>0.5 D Var(X) < Var(Y) x,X,, ,X, (n>1) is a random sample from N(μσ2), let-1ΣΧί 5) Suppose...
Advanced Statistics, I need help with (c) and (d) 2. Let X1, X2, ..., Xn be a random sample from a Bernoulli(6) distribution with prob- ability function Note that, for a random variable X with a Bernoulli(8) distribution, E [X] var [X] = θ(1-0) θ and (a) Obtain the log-likelihood function, L(0), and hence show that the maximum likelihood estimator of θ is 7l i= I (b) Show that dE (0) (c) Calculate the expected information T(e) EI()] (d) Show...