Rectangular Distribution MCQs Quiz Online PDF Download

Learn rectangular distribution MCQs, online MBA business statistics test for distance education, online data analytics courses prep. Practice probability distributions multiple choice questions (MCQs), rectangular distribution quiz questions and answers. GMAT test prep on binomial distribution, standard normal probability distribution, rectangular distribution tutorials for online what is probability courses distance learning.

Study bachelor in business administration and executive MBA degree MCQs: variance of random variable x of gamma distribution can be calculated as, for online data analytics courses with choices var(x) = n + 2 ⁄ μsup2;, var(x) = n ⁄ μsup2;, var (x) = n * 2 ⁄ μsup2;, var(x) = n - 2 ⁄ μsup3; for business administration degree preparation with trivia questions and answers online. Free skills assessment test is for online learn rectangular distribution quiz questions with MCQs, exam preparation questions and answers to prepare entrance exam for admission in top executive MBA programs.

MCQs on Rectangular DistributionQuiz PDF Download

MCQ: Variance of random variable x of gamma distribution can be calculated as

  1. Var(x) = n + 2 ⁄ μsup2;
  2. Var(x) = n ⁄ μsup2;
  3. Var (x) = n * 2 ⁄ μsup2;
  4. Var(x) = n - 2 ⁄ μsup3;

B

MCQ: If value of m in beta distribution is 35 and value of n in beta distribution is 50 then expected value of random variable x in beta distribution is

  1. 0.411
  2. 0.311
  3. 0.511
  4. 0.211

A

MCQ: Formula of mean of uniform or rectangular distribution is as

  1. mean = 4(b + a) ⁄ 2b
  2. mean = (b + a) ⁄ 2
  3. mean = (b - 2a) ⁄ 4
  4. mean = (2a + 2b) ⁄ 2a

B

MCQ: Formula such as mn ⁄ (m + n)² (m + n + 1) is used to calculate

  1. variance in exponential distribution
  2. variance in alpha distribution
  3. variance in gamma distribution
  4. variance in beta distribution

D

MCQ: Formula of calculating expected value of random variable x of gamma distribution is as

  1. E(x) = n ⁄ μ
  2. E(x) = pq ⁄ μ
  3. E(x) = μ ⁄ np
  4. E(x) = α ⁄ μ

A