5-1 Business Statistics Chapter 5 Discrete Distributions

5-2 Learning Objectives Distinguish between discrete random variables and continuous random variables. Know how to determine the mean and variance of a discrete distribution. Identify the type of statistical experiments that can be described by the binomial distribution, and know how to work such problems.

5-3 Learning Objectives -- Continued Decide when to use the Poisson distribution in analyzing statistical experiments, and know how to work such problems. Decide when binomial distribution problems can be approximated by the Poisson distribution, and know how to work such problems. Decide when to use the hypergeometric distribution, and know how to work such problems.

5-4 Discrete vs Continuous Distributions Random Variable -- a variable which contains the outcomes of a chance experiment Discrete Random Variable -- the set of all possible values is at most a finite or a countably infinite number of possible values –Number of new subscribers to a magazine –Number of bad checks received by a restaurant –Number of absent employees on a given day Continuous Random Variable -- takes on values at every point over a given interval –Current Ratio of a motorcycle distributorship –Elapsed time between arrivals of bank customers –Percent of the labor force that is unemployed

5-5 Some Special Distributions Discrete –binomial –Poisson –hypergeometric Continuous –normal –uniform –exponential –t –chi-square –F

5-6 Discrete Distribution -- Example Number of Crises Probability Distribution of Daily Crises ProbabilityProbability Number of Crises

5-7 Requirements for a Discrete Probability Function Probabilities are between 0 and 1, inclusively Total of all probabilities equals 1

5-8 Requirements for a Discrete Probability Function -- Examples XP(X) XP(X) XP(X)

5-9 Mean of a Discrete Distribution X P(X) XPX  ()

5-10 Variance and Standard Deviation of a Discrete Distribution X P(X) X 

5-11 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

5-12 Binomial Distribution Probability function Mean value Variance and standard deviation

5-13 Binomial Table n = 20PROBABILITY X

5-14 Using the Binomial Table Demonstration Problem 5.4 n = 20PROBABILITY X

5-15 Binomial Distribution using Table: Demonstration Problem 5.3 n = 20PROBABILITY X ……… …

5-16 Excel’s Binomial Function n =20 p =0.06 XP(X) 0=BINOMDIST(A5,B$1,B$2,FALSE) 1=BINOMDIST(A6,B$1,B$2,FALSE) 2=BINOMDIST(A7,B$1,B$2,FALSE) 3=BINOMDIST(A8,B$1,B$2,FALSE) 4=BINOMDIST(A9,B$1,B$2,FALSE) 5=BINOMDIST(A10,B$1,B$2,FALSE) 6=BINOMDIST(A11,B$1,B$2,FALSE) 7=BINOMDIST(A12,B$1,B$2,FALSE) 8=BINOMDIST(A13,B$1,B$2,FALSE) 9=BINOMDIST(A14,B$1,B$2,FALSE)

5-17 Poisson Distribution Describes discrete occurrences over a continuum or interval A discrete distribution Describes rare events Each occurrence is independent any other occurrences. The number of occurrences in each interval can vary from zero to infinity. The expected number of occurrences must hold constant throughout the experiment.

5-18 Poisson Distribution: Applications Arrivals at queuing systems –airports -- people, airplanes, automobiles, baggage –banks -- people, automobiles, loan applications –computer file servers -- read and write operations Defects in manufactured goods –number of defects per 1,000 feet of extruded copper wire –number of blemishes per square foot of painted surface –number of errors per typed page

5-19 Poisson Distribution Probability function nMean value nStandard deviation nVariance

5-20 Poisson Distribution: Demonstration Problem 5.7

5-21 Poisson Distribution: Probability Table X

5-22 Poisson Distribution: Using the Poisson Tables X

5-23 Poisson Distribution: Using the Poisson Tables X

5-24 Poisson Distribution: Using the Poisson Tables X

5-25 Poisson Distribution: Graphs

5-26 Excel’s Poisson Function = 1.6 XP(X) 0=POISSON(D5,E$1,FALSE) 1=POISSON(D6,E$1,FALSE) 2=POISSON(D7,E$1,FALSE) 3=POISSON(D8,E$1,FALSE) 4=POISSON(D9,E$1,FALSE) 5=POISSON(D10,E$1,FALSE) 6=POISSON(D11,E$1,FALSE) 7=POISSON(D12,E$1,FALSE) 8=POISSON(D13,E$1,FALSE) 9=POISSON(D14,E$1,FALSE)

5-27 Poisson Approximation of the Binomial Distribution Binomial probabilities are difficult to calculate when n is large. Under certain conditions binomial probabilities may be approximated by Poisson probabilities. Poisson approximation

5-28 Poisson Approximation of the Binomial Distribution XError XError