Suppose that X has a hypergeometric distribution with N = 2,000, K = 800, and n = 200. Find the probability P(X ≤ 70) based on the corresponding hypergeometric distribution and approximate normal distribution. Is the normal approximation reasonable?

A First Course in Probability (10th Edition)
10th Edition
ISBN:9780134753119
Author:Sheldon Ross
Publisher:Sheldon Ross
Chapter1: Combinatorial Analysis
Section: Chapter Questions
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8. Suppose that X has a hypergeometric distribution with N = 2,000, K = 800, and n =
200. Find the probability P(X ≤70) based on the corresponding hypergeometric
distribution and approximate normal distribution. Is the normal approximation
reasonable?
Transcribed Image Text:8. Suppose that X has a hypergeometric distribution with N = 2,000, K = 800, and n = 200. Find the probability P(X ≤70) based on the corresponding hypergeometric distribution and approximate normal distribution. Is the normal approximation reasonable?
•Types of functions to express
probability distribution
(a) Probability Mass Function (PMF)
f(x) = P(x = x)
(b) Cumulative Distribution Functions (CDF)
f(x) = P(X ≤ x)
Mean
M = { xf(x)
Variance
0² = [x²f (x) - μ²
Standard Deviation.
0x Tot
Discrete Uniform Distribution
f(x) = //12
M =
0² =
b+ a
2
(b-a+1)²
12
•Binomial Distribution
(f(x) = (~) p² (1-P)^-x
с пр
np(1-P)
1• Hypergeometric Distribution
(*) (*)
(~)
f(x) =
и = пр
8² = np(1-P/(N=1)
where
K/N
P=
·Poisson Distribution
-^+ (^T)*
X!
f(x) = e
M = AT
0² = NT
Where
N = population size
sample size /no. of trials
P
probability
-
p = probability of success on a
single trial
K = no. of successes in the population
in the sample
of
X - no.
successes
Transcribed Image Text:•Types of functions to express probability distribution (a) Probability Mass Function (PMF) f(x) = P(x = x) (b) Cumulative Distribution Functions (CDF) f(x) = P(X ≤ x) Mean M = { xf(x) Variance 0² = [x²f (x) - μ² Standard Deviation. 0x Tot Discrete Uniform Distribution f(x) = //12 M = 0² = b+ a 2 (b-a+1)² 12 •Binomial Distribution (f(x) = (~) p² (1-P)^-x с пр np(1-P) 1• Hypergeometric Distribution (*) (*) (~) f(x) = и = пр 8² = np(1-P/(N=1) where K/N P= ·Poisson Distribution -^+ (^T)* X! f(x) = e M = AT 0² = NT Where N = population size sample size /no. of trials P probability - p = probability of success on a single trial K = no. of successes in the population in the sample of X - no. successes
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