文档介绍：CHAPTER 6 Discrete Probability Distributions
to pany
Introduction to Business Statistics
fourth edition, by Ronald M. Weiers
Presentation by Priscilla Chaffe-Stengel
Donald N. Stengel
© 2002 The Wadsworth Group
Chapter 6 - Learning Objectives
Distinguish between discrete and continuous random variables.
Differentiate between the binomial and the Poisson discrete probability distributions and their applications.
Construct a probability distribution for a discrete random variable, determine its mean and variance, and specify the probability that a discrete random variable will have a given value or value in a given range.
© 2002 The Wadsworth Group
Chapter 6 - Key Terms
Random variables
Discrete
Continuous
Bernoulli process
Probability distributions
Binomial distribution
Poisson distribution
© 2002 The Wadsworth Group
Discrete vs Continuous Variables
Discrete Variables: Can take on only certain values along an interval
the number of sales made in a week
the volume of milk bought at a store
the number of defective parts
Continuous Variables: Can take on any value at any point along an interval
the depth at which a drilling team strikes oil
the volume of milk produced by a cow
the proportion of defective parts
© 2002 The Wadsworth Group
Describing the Distribution for a Discrete Random Variable
The probability distribution for a discrete random variable defines the probability of a discrete value x.
Mean: µ = E(x) =
Variance: s2 = E[(x –µ)2]
=
© 2002 The Wadsworth Group
The Bernoulli Process, Characteristics
There are two or more consecutive trials.
In each trial, there are just two possible es.
The trials are statistically independent.
The probability of ess remains constant trial-to-trial.
© 2002 The Wadsworth Group
The Binomial Distribution
The binomial probability distribution defines the probability of exactly x esses in n trials of the Bernoulli process.
for each value of x.
Mean: µ = E(x) = n p
Variance: s2 = E[(x –µ)2] = n p (1 –