chapter 5 discrete random variables and probability distributions ©
TRANSCRIPT
Chapter 5Chapter 5
Discrete Random Discrete Random Variables and Variables and
Probability Probability DistributionsDistributions
©
Random VariablesRandom Variables
A random variablerandom variable is a variable that takes on numerical values determined by the outcome of a random experiment.
Discrete Random VariablesDiscrete Random Variables
A random variable is discrete discrete if it can take on no more than a countable number of values.
Discrete Random VariablesDiscrete Random Variables(Examples)(Examples)
1. The number of defective items in a sample of twenty items taken from a large shipment.
2. The number of customers arriving at a check-out counter in an hour.
3. The number of errors detected in a corporation’s accounts.
4. The number of claims on a medical insurance policy in a particular year.
Continuous Random Continuous Random VariablesVariables
A random variable is continuous continuous if it can take any value in an interval.
Continuous Random Continuous Random VariablesVariables
(Examples)(Examples)
1. The income in a year for a family.2. The amount of oil imported into the U.S.
in a particular month.3. The change in the price of a share of IBM
common stock in a month.4. The time that elapses between the
installation of a new computer and its failure.
5. The percentage of impurity in a batch of chemicals.
Discrete Probability Discrete Probability DistributionsDistributions
The probability distribution function probability distribution function (DPF),(DPF), P(x), of a discrete random variable expresses the probability that X takes the value x, as a function of x. That is
. of valuesallfor ),()( xxXPxP
Discrete Probability Discrete Probability DistributionsDistributions
(Example 5.1)(Example 5.1)
Graph the probability distribution function for the roll of a single six-sided die.
1 2 3 4 5 6
1/6
P(x)
x
Figure 5.1Figure 5.1
Required Properties of Required Properties of Probability Distribution Probability Distribution
Functions of Discrete Random Functions of Discrete Random VariablesVariables
Let X be a discrete random variable with probability distribution function, P(x). Then
i. P(x) 0 for any value of xii. The individual probabilities sum to 1; that is
Where the notation indicates summation over all possible values x.
x
xP 1)(
Cumulative Probability Cumulative Probability FunctionFunction
The cumulative probability function,cumulative probability function, F(x0), of a random variable X expresses the probability that X does not exceed the value x0, as a function of x0. That is
Where the function is evaluated at all values x0
)()( 00 xXPxF
Derived Relationship Between Derived Relationship Between Probability Function and Cumulative Probability Function and Cumulative
Probability FunctionProbability Function
Let X be a random variable with probability function P(x) and cumulative probability function F(x0). Then it can be shown that
Where the notation implies that summation is over all possible values x that are less than or equal to x0.
0
)()( 0xx
XPxF
Derived Properties of Derived Properties of Cumulative Probability Cumulative Probability
Functions for Discrete Random Functions for Discrete Random VariablesVariables
Let X be a discrete random variable with a cumulative probability function, F(x0). Then we can show that
i. 0 F(x0) 1 for every number x0
ii. If x0 and x1 are two numbers with x0 < x1, then F(x0) F(x1)
Expected ValueExpected Value
The expected value, E(X),expected value, E(X), of a discrete random variable X is defined
Where the notation indicates that summation extends over all possible values x.The expected value of a random variable is called its meanmean and is denoted xx.
x
xxPXE )()(
Expected Value: Functions Expected Value: Functions of Random Variablesof Random Variables
Let X be a discrete random variable with probability function P(x) and let g(X) be some function of X. Then the expected value, E[g(X)], of that function is defined as
x
xPxgXgE )()()]([
Variance and Standard Variance and Standard DeviationDeviation
Let X be a discrete random variable. The expectation of the squared discrepancies about the mean, (X - )2, is called the variancevariance, denoted 2
x and is given by
The standard deviationstandard deviation, x , is the positive square root of the variance.
x
xxx xPxXE )()()( 222
VarianceVariance(Alternative Formula)(Alternative Formula)
The variancevariance of a discrete random variable X can be expressed as
22
222
)(
)(
xx
xx
xPx
XE
Expected Value and Variance for Expected Value and Variance for Discrete Random Variable Using Discrete Random Variable Using
Microsoft ExcelMicrosoft Excel(Figure 5.4)(Figure 5.4)
Sales P(x) Mean Variance0 0.15 0 0.5703751 0.3 0.3 0.270752 0.2 0.4 0.00053 0.2 0.6 0.22054 0.1 0.4 0.420255 0.05 0.25 0.465125
1.95 1.9475
Expected Value = 1.95 Variance = 1.9475
Summary of Properties for Summary of Properties for Linear Function of a Random Linear Function of a Random
VariableVariable
Let X be a random variable with mean x , and variance 2
x ; and let a and b be any constant fixed numbers. Define the random variable Y = a + bX. Then, the meanmean and variancevariance of Y are
and
so that the standard deviation of Ystandard deviation of Y is
XY babXaE )(
XY bbXaVar 222 )(
XY b
Summary Results for the Mean Summary Results for the Mean and Variance of Special Linear and Variance of Special Linear
FunctionsFunctions
a) Let b = 0 in the linear function, W = a + bX. Then W = a (for any constant a).
If a random variable always takes the value a, it will have a mean a and a variance 0.
b) Let a = 0 in the linear function, W = a + bX. Then W = bX.
0)()( aVarandaaE
22)()( XX baVarandbbXE
Mean and Variance of ZMean and Variance of Z
Let a = -X/X and b = 1/ X in the linear function Z = a + bX. Then,
so that
and
X
XXbXaZ
01
X
XX
X
X
XXE
11 2
2
X
XX
XXVar
Bernoulli DistributionBernoulli Distribution
A Bernoulli distributionBernoulli distribution arises from a random experiment which can give rise to just two possible outcomes. These outcomes are usually labeled as either “success” or “failure.” If denotes the probability of a success and the probability of a failure is (1 - ), the the Bernoulli probability function is )1()1()0( PandP
Mean and Variance of a Mean and Variance of a Bernoulli Random VariableBernoulli Random Variable
The meanmean is:
And the variancevariance is:
)1()1)(0()()( xPxXEX
X
)1()1()1()0(
)()(])[(
22
222
X
XXX xPxXE
Sequences of Sequences of xx Successes in Successes in nn Trials Trials
The number of sequences with x successes number of sequences with x successes in n independent trialsin n independent trials is:
Where n! = n x (x – 1) x (n – 2) x . . . x 1 and 0! = 1.
)!(!
!
xnx
nC n
x
time. samethe at occur can them of two no since
exclusive,mutually are sequencesC These nx
Binomial DistributionBinomial Distribution
Suppose that a random experiment can result in two possible mutually exclusive and collectively exhaustive outcomes, “success” and “failure,” and that is the probability of a success resulting in a single trial. If n independent trials are carried out, the distribution of the resulting number of successes “x” is called the binomial binomial distributiondistribution. Its probability distribution function for the binomial random variable X = x is:
P(x successes in n independent trials)=
for x = 0, 1, 2 . . . , n
)()1()!(!
!)( xnx
xnx
nxP
Mean and Variance of a Mean and Variance of a Binomial Probability Binomial Probability
DistributionDistributionLet X be the number of successes in n independent trials, each with probability of success . The x follows a binomial distribution with meanmean,
and variancevariance,
nXEX )(
)1(])[( 22 nXEX
Binomial ProbabilitiesBinomial Probabilities- An Example –- An Example –
(Example 5.7)(Example 5.7)
An insurance broker, Shirley Ferguson, has five contracts, and she believes that for each contract, the probability of making a sale is 0.40.
What is the probability that she makes at most one sale?
P(at most one sale) = P(X 1) = P(X = 0) + P(X = 1)
= 0.078 + 0.259 = 0.337
259.0)6.0()4.0(1!4!
5! P(1) sale) P(1
0.078 )6.0()4.0(0!5!
5! P(0) sales) P(no
41
50
Binomial Probabilities, n = 100, Binomial Probabilities, n = 100, =0.40=0.40
(Figure 5.10)(Figure 5.10)
Sample size 100Probability of success 0.4Mean 40Variance 24Standard deviation 4.898979
Binomial Probabilities TableX P(X) P(<=X) P(<X) P(>X) P(>=X)
36 0.059141 0.238611 0.179469 0.761389 0.82053137 0.068199 0.30681 0.238611 0.69319 0.76138938 0.075378 0.382188 0.30681 0.617812 0.6931939 0.079888 0.462075 0.382188 0.537925 0.61781240 0.081219 0.543294 0.462075 0.456706 0.53792541 0.079238 0.622533 0.543294 0.377467 0.45670642 0.074207 0.69674 0.622533 0.30326 0.37746743 0.066729 0.763469 0.69674 0.236531 0.30326
Hypergeometric DistributionHypergeometric DistributionSuppose that a random sample of n objects is chosen from a group of N objects, S of which are successes. The distribution of the number of X successes in the sample is called the hypergeometric distributionhypergeometric distribution. Its probability function is:
Where x can take integer values ranging from the larger of 0 and [n-(N-S)] to the smaller of n and S.
)!(!!
)!()!()!(
)!(!!
)(
nNnN
xnSNxnSN
xSxS
C
CCxP
Nn
SNxn
Sx
Poisson Probability Poisson Probability DistributionDistribution
Assume that an interval is divided into a very large number of subintervals so that the probability of the occurrence of an event in any subinterval is very small. The assumptions of a Poisson Poisson probability distributionprobability distribution are:
1) The probability of an occurrence of an event is constant for all subintervals.
2) There can be no more than one occurrence in each subinterval.
3) Occurrences are independent; that is, the number of occurrences in any non-overlapping intervals in independent of one another.
Poisson Probability Poisson Probability DistributionDistribution
The random variable X is said to follow the Poisson probability distribution if it has the probability function:
whereP(x) = the probability of x successes over a given
period of time or space, given = the expected number of successes per time or
space unit; > 0e = 2.71828 (the base for natural logarithms)
The mean and variance of the Poisson probability mean and variance of the Poisson probability distribution aredistribution are:
1,2,... 0,xfor,!
)(
x
exP
x
])[()( 22 XEandXE xx
Partial Poisson Probabilities for Partial Poisson Probabilities for = = 0.03 Obtained Using Microsoft 0.03 Obtained Using Microsoft
Excel Excel PPHStatHStat(Figure 5.14)(Figure 5.14)
Poisson Probabilities TableX P(X) P(<=X) P(<X) P(>X) P(>=X)0 0.970446 0.970446 0.000000 0.029554 1.0000001 0.029113 0.999559 0.970446 0.000441 0.0295542 0.000437 0.999996 0.999559 0.000004 0.0004413 0.000004 1.000000 0.999996 0.000000 0.0000044 0.000000 1.000000 1.000000 0.000000 0.000000
Poisson Approximation to Poisson Approximation to the Binomial Distributionthe Binomial Distribution
Let X be the number of successes resulting from n independent trials, each with a probability of success, . The distribution of the number of successes X is binomial, with mean n. If the number of trials n is large and n is of only moderate size (preferably n 7), this distribution can be approximated by the Poisson distribution with = n. The probability function of the approximating distribution is then:
1,2,... 0,xfor,!
)()(
x
nexP
xn
Joint Probability FunctionsJoint Probability Functions
Let X and Y be a pair of discrete random variables. Their joint probability functionjoint probability function expresses the probability that X takes the specific value x and simultaneously Y takes the value y, as a function of x and y. The notation used is P(x, y) so,
)(),( yYxXPyxP
Joint Probability FunctionsJoint Probability Functions
Let X and Y be a pair of jointly distributed random variables. In this context the probability function of the random variable X is called its marginal probability functionmarginal probability function and is obtained by summing the joint probabilities over all possible values; that is,
Similarly, the marginal probability functionmarginal probability function of the random variable Y is
y
yxPxP ),()(
x
yxPyP ),()(
Properties of Joint Properties of Joint Probability FunctionsProbability Functions
Let X and Y be discrete random variables with joint probability function P(x,y). Then
1.1. P(x,y) P(x,y) 0 for any pair of values x and y 0 for any pair of values x and y
2.2. The sum of the joint probabilities P(x, The sum of the joint probabilities P(x, y) over all possible values must be 1.y) over all possible values must be 1.
Conditional Probability Conditional Probability FunctionsFunctions
Let X and Y be a pair of jointly distributed discrete random variables. The conditional probability functionconditional probability function of the random variable Y, given that the random variable X takes the value x, expresses the probability that Y takes the value y, as a function of y, when the value x is specified for X. This is denoted P(y|x), and so by the definition of conditional probability:
Similarly, the conditional probability functionconditional probability function of X, given Y = y is:
)(
),()|(
xP
yxPxyP
)(
),()|(
yP
yxPyxP
Independence of Jointly Independence of Jointly Distributed Random Distributed Random
VariablesVariables
The jointly distributed random variables X and Y are said to be independentindependent if and only if their joint probability function is the product of their marginal probability functions, that is, if and only if
And k random variables are independent if and only if
y. and x valuesof pairs possible allfor )()(),( yPxPyxP
)()()(),,,( 2121 kk xPxPxPxxxP
Expected Value Function of Expected Value Function of Jointly Distributed Random Jointly Distributed Random
VariablesVariables
Let X and Y be a pair of discrete random variables with joint probability function P(x, y). The expectation of any function g(x, y)expectation of any function g(x, y) of these random variables is defined as:
x y
yxPyxgYXgE ),(),()],([
Stock Returns, Marginal Stock Returns, Marginal Probability, Mean, VarianceProbability, Mean, Variance
(Example 5.16)(Example 5.16)
Y Return
X Retur
n
0% 5% 10% 15%
0% 0.0625
0.0625
0.0625
0.0625
5% 0.0625
0.0625
0.0625
0.0625
10% 0.0625
0.0625
0.0625
0.0625
15% 0.0625
0.0625
0.0625
0.0625
Table 5.6
CovarianceCovariance
Let X be a random variable with mean X , and let Y be a random variable with mean, Y . The expected value of (X - X )(Y - Y ) is called the covariance covariance between X and Y, denoted Cov(X, Y).For discrete random variables
An equivalent expression is
x y
yxYX yxPyxYXEYXCov ),())(()])([(),(
x y
yxyx yxxyPXYEYXCov ),()(),(
CorrelationCorrelation
Let X and Y be jointly distributed random variables. The correlation between X and Y is:
YX
YXCovYXCorr
),(
),(
Covariance and Statistical Covariance and Statistical IndependenceIndependence
If two random variables are statistically statistically independentindependent, the covariance between them is 0. However, the converse is not necessarily true.
Portfolio AnalysisPortfolio Analysis
The random variable X is the price for stock A and the random variable Y is the price for stock B. The market value, W, for the portfolio is given by the linear function,
Where, a, is the number of shares of stock A and, b, is the number of shares of stock B.
bYaXW
Portfolio AnalysisPortfolio Analysis
The mean value for Wmean value for W is,
The variance for Wvariance for W is,
or using the correlation,
YX
W
ba
bYaXEWE
][][
),(222222 YXabCovba YXW
YXYXW YXabCorrba ),(222222
Key WordsKey Words Bernoulli Random
Variable, Mean and Variance
Binomial Distribution Conditional Probability
Function Continuous Random
Variable Correlation Covariance Cumulative Probability
Function
Differences of Random Variables
Discrete Random Variable Expected Value Expected Value: Functions
of Random Variables Expected Value: Function of
Jointly Distributed Random Variable
Hypergeometric Distribution Independence of Jointly
Distributed Random Variables
Key WordsKey Words(continued)(continued)
Joint Probability Function Marginal Probability
Function Mean of Binomial
Distribution Mean: Functions of
Random Variables Poisson Approximation
to the Binomial Distribution
Poisson Distribution Portfolio Analysis
Portfolio, Market Value Probability Distribution
Function Properties: Cumulative
Probability Functions Properties: Joint
Probability Functions Properties: Probability
Distribution Functions Random Variable
Key WordsKey Words(continued)(continued)
Relationships: Probability Function and Cumulative Probability Function
Standard Deviation: Discrete Random Variable
Sums of Random Variables
Variance: Binomial Distribution
Variance: Discrete Random Variable
Variance: Discrete Random Variable (Alternative Formula)
Variance: Functions of Random Variables