5. let f(x) be the probability density function of a random variable, check the correct alternative: (a) g(x)f(x)dx = Lie Xi, whereX; are random samples of the uniform distribution between 0 and 1, being an exact match when n 0o (b) g(x)f(x)dx = Lin, where X; are random samples of uniform distribution between O and 1, being an exact equality when n→ o (c) g(x)f(x)dx = Lisi,whereX;are random samples of the f(x)distribution, being an exact match when n00 (d) g(x)f(x)dx = Lisgtkn, where X; are random samples of distribution f (x), being an exact match when n 0 (e) , g(x)f(x)dx: L S(X;) where X; are random samples of f(x) distribution. being an exact match when n 0o.

5. let f(x) be the probability density function of a random variable, check the correct alternative: (a) g(x)f(x)dx = Lie Xi, whereX; are random samples of the uniform distribution between 0 and 1, being an exact match when n 0o (b) g(x)f(x)dx = Lin, where X; are random samples of uniform distribution between O and 1, being an exact equality when n→ o (c) g(x)f(x)dx = Lisi,whereX;are random samples of the f(x)distribution, being an exact match when n00 (d) g(x)f(x)dx = Lisgtkn, where X; are random samples of distribution f (x), being an exact match when n 0 (e) , g(x)f(x)dx: L S(X;) where X; are random samples of f(x) distribution. being an exact match when n 0o.

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Concept explainers

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!

Question

5. let f(x) be the probability density function of a random variable, check the correct
alternative:
(a) 8(x)f(x)dx = Lisi, whereX; are random samples of the uniform distribution
between 0 and 1, being an exact match when n→ o0
(b) g(x)f (x)dx - Li4n, where X; are random samples of uniform distribution between
O and 1, being an exact equality when n → 0
(c) g(x)f(x)dx Li,whereX;are random samples of the f(x)distribution, being an
exact match when n00
(d) g(x)f(x)dx - Li8t, where X; are random samples of distribution f(x), being an
exact match when n 200
(e) L, 8(x)f(x)dx =
S(X;)
where X; are random samples of f(x) distribution. being an
exact match when n → 0o.

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