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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc.. Chap 5-1 Chapter 5 Some Important Discrete Probability Distributions Basic Business Statistics 10 th Edition

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-2 Learning Objectives In this chapter, you learn: The properties of a probability distribution To calculate the expected value and variance of a probability distribution To calculate the covariance and its use in finance To calculate probabilities from binomial, Poisson distributions and hypergeometric How to use the binomial, hypergeometric, and Poisson distributions to solve business problems

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-3 Introduction to Probability Distributions Random Variable Represents a possible numerical value from an uncertain event Random Variables Discrete Random Variable Continuous Random Variable Ch. 5Ch. 6

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-4 Discrete Random Variables Can only assume a countable number of values Examples: Roll a die twice Let X be the number of times 4 comes up (then X could be 0, 1, or 2 times) Toss a coin 5 times. Let X be the number of heads (then X = 0, 1, 2, 3, 4, or 5)

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-5 Experiment: Toss 2 Coins. Let X = # heads. T T Discrete Probability Distribution 4 possible outcomes T T H H HH Probability Distribution X X Value Probability 0 1/4 = /4 = /4 = Probability

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-6 Discrete Random Variable Summary Measures Expected Value (or mean) of a discrete distribution (Weighted Average) Example: Toss 2 coins, X = # of heads, compute expected value of X: E(X) = (0 x 0.25) + (1 x 0.50) + (2 x 0.25) = 1.0 X P(X)

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-7 Variance of a discrete random variable Standard Deviation of a discrete random variable where: E(X) = Expected value of the discrete random variable X X i = the i th outcome of X P(X i ) = Probability of the i th occurrence of X Discrete Random Variable Summary Measures (continued)

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-8 Example: Toss 2 coins, X = # heads, compute standard deviation (recall E(X) = 1) Discrete Random Variable Summary Measures (continued) Possible number of heads = 0, 1, or 2

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-9 二元隨機變數之機率分配

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-10 事件機率

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-11 表 5.6 一般化的聯合機率分配表

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-12 Expected Value

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-13 The Covariance The covariance measures the strength of the linear relationship between two variables The covariance: where:X = discrete variable X X i = the i th outcome of X Y = discrete variable Y Y i = the i th outcome of Y P(X i Y i ) = probability of occurrence of the i th outcome of X and the i th outcome of Y

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-14 Computing the Mean for Investment Returns Return per $1,000 for two types of investments P(X i Y i ) Economic condition Passive Fund X Aggressive Fund Y 0.2 Recession- $ 25 - $ Stable Economy Expanding Economy Investment E(X) = μ X = (-25)(0.2) +(50)(0.5) + (100)(0.3) = 50 E(Y) = μ Y = (-200)(0.2) +(60)(0.5) + (350)(0.3) = 95

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-15 Computing the Standard Deviation for Investment Returns P(X i Y i ) Economic condition Passive Fund X Aggressive Fund Y 0.2 Recession- $ 25 - $ Stable Economy Expanding Economy Investment

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-16 Computing the Covariance for Investment Returns P(X i Y i ) Economic condition Passive Fund X Aggressive Fund Y 0.2 Recession- $ 25 - $ Stable Economy Expanding Economy Investment

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-17 Interpreting the Results for Investment Returns The aggressive fund has a higher expected return, but much more risk μ Y = 95 > μ X = 50 but σ Y = > σ X = The Covariance of 8250 indicates that the two investments are positively related and will vary in the same direction

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-18 The Sum of Two Random Variables Expected Value of the sum of two random variables: Variance of the sum of two random variables: Standard deviation of the sum of two random variables:

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-19 Portfolio Expected Return and Portfolio Risk Portfolio expected return (weighted average return): Portfolio risk (weighted variability) Where w = portion of portfolio value in asset X (1 - w) = portion of portfolio value in asset Y

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-20 Portfolio Example Investment X: μ X = 50 σ X = Investment Y: μ Y = 95 σ Y = σ XY = 8250 Suppose 40% of the portfolio is in Investment X and 60% is in Investment Y: The portfolio return and portfolio variability are between the values for investments X and Y considered individually

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-21 Probability Distributions Continuous Probability Distributions Binomial Hypergeometric Poisson Probability Distributions Discrete Probability Distributions Normal Uniform Exponential Ch. 5Ch. 6

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-22 The Binomial Distribution Binomial Hypergeometric Poisson Probability Distributions Discrete Probability Distributions

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-23 Binomial Probability Distribution A fixed number of observations, n e.g., 15 tosses of a coin; ten light bulbs taken from a warehouse Two mutually exclusive and collectively exhaustive categories e.g., head or tail in each toss of a coin; defective or not defective light bulb Generally called “success” and “failure” Probability of success is p, probability of failure is 1 – p Constant probability for each observation e.g., Probability of getting a tail is the same each time we toss the coin

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-24 Binomial Probability Distribution (continued) Observations are independent The outcome of one observation does not affect the outcome of the other Two sampling methods Infinite population without replacement Finite population with replacement

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-25 Possible Binomial Distribution Settings A manufacturing plant labels items as either defective or acceptable A firm bidding for contracts will either get a contract or not A marketing research firm receives survey responses of “yes I will buy” or “no I will not” New job applicants either accept the offer or reject it

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-26 Rule of Combinations The number of combinations of selecting X objects out of n objects is where: n! =(n)(n - 1)(n - 2)... (2)(1) X! = (X)(X - 1)(X - 2)... (2)(1) 0! = 1 (by definition)

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-27 P(X) = probability of X successes in n trials, with probability of success p on each trial X = number of ‘successes’ in sample, (X = 0, 1, 2,..., n) n = sample size (number of trials or observations) p = probability of “success” P(X) n X ! nX p(1-p) X n X ! ()! Example: Flip a coin four times, let x = # heads: n = 4 p = p = ( ) = 0.5 X = 0, 1, 2, 3, 4 Binomial Distribution Formula

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-28 Example: Calculating a Binomial Probability What is the probability of one success in five observations if the probability of success is.1? X = 1, n = 5, and p = 0.1

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-29 n = 5 p = 0.1 n = 5 p = 0.5 Mean X P(X) X P(X) 0 Binomial Distribution The shape of the binomial distribution depends on the values of p and n Here, n = 5 and p = 0.1 Here, n = 5 and p = 0.5

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-30 Binomial Distribution Characteristics Mean Variance and Standard Deviation Wheren = sample size p = probability of success (1 – p) = probability of failure

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-31

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-32

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-33

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-34

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-35 The Bernoulli Distribution Suppose that a random experiment can give rise to just two possible mutually exclusive and collectively exhaustive outcomes, which for convenience we will label “ success ” and “ failure. ” Let p denote the probability of success, so that the probability of failure is (1-p). Now define the random variable X so that X takes the value 1 if the outcome of the experiment is success and 0 otherwise. The probability function of this random variable is then P x (0) = (1-p), P x (1) = p, This distribution is known as the Bernoulli distribution. Its mean and variance are E(X) = 0*(1-p)+1*(p) = p V(X) = (0-p) 2 (1-p)+(1-p) 2 p = p(1-p)

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-36 Binomial Distribution Experiment involves n identical trials Each trial has exactly two possible outcomes: success and failure Each trial is independent of the previous trials p is the probability of a success on any one trial q = (1-p) is the probability of a failure on any one trial p and q are constant throughout the experiment X is the number of successes in the n trials Applications Sampling with replacement Sampling without replacement -- n < 5% N

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-37 n = 5 p = 0.1 n = 5 p = 0.5 Mean X P(X) X P(X) 0 Binomial Characteristics Examples

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-38 Using Binomial Tables n = 10 x…p=.20p=.25p=.30p=.35p=.40p=.45p= ………………………………………………………… …p=.80p=.75p=.70p=.65p=.60p=.55p=.50x Examples: n = 10, p = 0.35, x = 3: P(x = 3|n =10, p = 0.35) = n = 10, p = 0.75, x = 2: P(x = 2|n =10, p = 0.75) =

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-39 The Hypergeometric Distribution Binomial Poisson Probability Distributions Discrete Probability Distributions Hypergeometric

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-40 The Hypergeometric Distribution “n” trials in a sample taken from a finite population of size N Sample taken without replacement Outcomes of trials are dependent Concerned with finding the probability of “X” successes in the sample where there are “A” successes in the population

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-41 Hypergeometric Distribution Formula Where N = population size A = number of successes in the population N – A = number of failures in the population n = sample size X = number of successes in the sample n – X = number of failures in the sample

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-42 Properties of the Hypergeometric Distribution The mean of the hypergeometric distribution is The standard deviation is Where is called the “Finite Population Correction Factor” from sampling without replacement from a finite population

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-43 Using the Hypergeometric Distribution ■Example: 3 different computers are checked from 10 in the department. 4 of the 10 computers have illegal software loaded. What is the probability that 2 of the 3 selected computers have illegal software loaded? N = 10n = 3 A = 4 X = 2 The probability that 2 of the 3 selected computers have illegal software loaded is 0.30, or 30%.

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-44 The Poisson Distribution Binomial Hypergeometric Poisson Probability Distributions Discrete Probability Distributions

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-45 The Poisson Distribution Apply the Poisson Distribution when: You wish to count the number of times an event occurs in a given area of opportunity The probability that an event occurs in one area of opportunity is the same for all areas of opportunity The number of events that occur in one area of opportunity is independent of the number of events that occur in the other areas of opportunity The probability that two or more events occur in an area of opportunity approaches zero as the area of opportunity becomes smaller The average number of events per unit is (lambda)

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-46 Poisson Distribution Formula where: X = number of events in an area of opportunity = expected number of events e = base of the natural logarithm system ( )

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-47 Poisson Distribution Characteristics Mean Variance and Standard Deviation where = expected number of events

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-48 Using Poisson Tables X Example: Find P(X = 2) if = 0.50

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-49 Graph of Poisson Probabilities X = P(X = 2) = Graphically: = 0.50

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-50 Poisson Distribution Shape The shape of the Poisson Distribution depends on the parameter : = 0.50 = 3.00

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-51

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-52

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-53 表 6.21 二項分配與泊松分 配

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-54 圖 6.15 二項分配 圖 6.16 泊松 分配

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-55 圖 6.17 二項分配與泊松分配

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Basic Business Statistics, 10e © 2006 Prentice-Hall, Inc. Chap 5-56 Chapter Summary Addressed the probability of a discrete random variable Defined covariance and discussed its application in finance Discussed the Binomial distribution Discussed the Hypergeometric distribution Discussed the Poisson distribution

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