英文商務(wù)統(tǒng)計(jì)學(xué)-第五章ch05

1、Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-1Chapter 5Some Important Discrete Probability DistributionsBusiness Statistics:A First Course5th EditionBusiness Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-2Learning ObjectivesIn this chapter, you learn: The pr

2、operties of a probability distributionTo calculate the expected value and variance of a probability distributionTo calculate probabilities from binomial and Poisson distributionsHow to use the binomial and Poisson distributions to solve business problemsBusiness Statistics: A First Course, 5e 2009 P

3、rentice-Hall, Inc.Chap 5-3DefinitionsRandom VariablesA random variable represents a possible numerical value from an uncertain event.Discrete random variables produce outcomes that come from a counting process (e.g. number of courses you are taking this semester).Continuous random variables produce

4、outcomes that come from a measurement (e.g. your annual salary, or your weight). Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-4DefinitionsRandom VariablesRandom VariablesDiscrete Random VariableContinuousRandom VariableCh. 5Ch. 6DefinitionsRandom VariablesRandom VariablesDi

5、screte Random VariableContinuousRandom VariableCh. 5Ch. 6DefinitionsRandom VariablesRandom VariablesDiscrete Random VariableContinuousRandom VariableCh. 5Ch. 6Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-5Discrete Random VariablesCan only assume a countable number of values

6、Examples: Roll a die twiceLet X be the number of times 4 occurs (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)Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-6Probability Distribution For A Discrete Random Var

7、iableA probability distribution for a discrete random variable is a mutually exclusive listing of all possible numerical outcomes for that variable and a probability of occurrence associated with each outcome.Number of Classes TakenProbability20.230.440.2450.16Business Statistics: A First Course, 5e

8、 2009 Prentice-Hall, Inc.Chap 5-7Experiment: Toss 2 Coins. Let X = # heads.TTExample of a Discrete Random Variable Probability Distribution4 possible outcomesTTHHHHProbability Distribution 0 1 2 X X Value Probability Probability Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-

9、8Discrete Random VariablesExpected Value (Measuring Center) Expected Value (or mean) of a discrete random variable (Weighted Average)Example: Toss 2 coins, X = # of heads, compute expected value of X: E(X) = (0)(0.25) + (1)(0.50) + (2)(0.25) X P(X)Business Statistics: A First Course, 5e 2009 Prentic

10、e-Hall, Inc.Chap 5-9Variance of a discrete random variableStandard Deviation of a discrete random variablewhere:E(X) = Expected value of the discrete random variable X Xi = the ith outcome of XP(Xi) = Probability of the ith occurrence of XDiscrete Random Variables Measuring DispersionBusiness Statis

11、tics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-10Example: Toss 2 coins, X = # heads, compute standard deviation (recall E(X) = 1)Discrete Random Variables Measuring Dispersion(continued)Possible number of heads = 0, 1, or 2Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap

12、 5-11Probability DistributionsContinuous Probability DistributionsBinomialPoissonProbability DistributionsDiscrete Probability DistributionsNormalCh. 5Ch. 6Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-12Binomial Probability DistributionA fixed number of observations, ne.g.,

13、 15 tosses of a coin; ten light bulbs taken from a warehouseEach observation is categorized as to whether or not the “event of interest” occurrede.g., head or tail in each toss of a coin; defective or not defective light bulbSince these two categories are mutually exclusive and collectively exhausti

14、veWhen the probability of the event of interest is represented as , then the probability of the event of interest not occurring is 1 - Constant probability for the event of interest occurring () for each observationProbability of getting a tail is the same each time we toss the coinBusiness Statisti

15、cs: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-13Binomial Probability Distribution(continued)Observations are independentThe outcome of one observation does not affect the outcome of the otherTwo sampling methods deliver independenceInfinite population without replacementFinite population wit

16、h replacementBusiness Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-14Possible Applications for the Binomial DistributionA manufacturing plant labels items as either defective or acceptableA firm bidding for contracts will either get a contract or notA marketing research firm receive

17、s survey responses of “yes I will buy” or “no I will not”New job applicants either accept the offer or reject itBusiness Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-15The Binomial DistributionCounting TechniquesSuppose the event of interest is obtaining heads on the toss of a fair

18、coin. You are to toss the coin three times. In how many ways can you get two heads?Possible ways: HHT, HTH, THH, so there are three ways you can getting two heads.This situation is fairly simple. We need to be able to count the number of ways for more complicated situations.Business Statistics: A Fi

19、rst Course, 5e 2009 Prentice-Hall, Inc.Chap 5-16Counting TechniquesRule of CombinationsThe 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)Business Statistics: A First Course, 5e 2009

20、Prentice-Hall, Inc.Chap 5-17Counting TechniquesRule of CombinationsHow many possible 3 scoop combinations could you create at an ice cream parlor if you have 31 flavors to select from?The total choices is n = 31, and we select X = 3.Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Cha

21、p 5-18P(X) = probability of X events of interest in n trials, with the probability of an “event of interest” being for each trial X = number of “events of interest” in sample, (X = 0, 1, 2, ., n) n = sample size (number of trials or observations) = probability of “event of interest” P(X)nX !nX(1-)Xn

22、X!()!=-Example: Flip a coin four times, let x = # heads:n = 41 - X = 0, 1, 2, 3, 4Binomial Distribution FormulaBusiness Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-19Example: Calculating a Binomial ProbabilityWhat is the probability of one success in five observations if the probab

23、ility of an event of interest is .1? X = 1, n = 5, and Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-20The Binomial DistributionExampleSuppose the probability of purchasing a defective computer is 0.02. What is the probability of purchasing 2 defective computers in a group o

24、f 10? X = 2, n = 10, and = .02Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-21The Binomial DistributionShapen = 5 0.2.4.6012345XP(X)n = 5 .2.4.6012345XP(X)0The shape of the binomial distribution depends on the values of and nHere, n = 5 and = .1Here, n = 5 and = .5Business S

25、tatistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-22The Binomial DistributionUsing Binomial Tablesn = 10 x=.20=.25=.30=.35=.40=.45=.500123456789100.10740.26840.30200.20130.08810.02640.00550.00080.00010.00000.00000.05630.18770.28160.25030.14600.05840.01620.00310.00040.00000.00000.02820.121

26、10.23350.26680.20010.10290.03680.00900.00140.00010.00000.01350.07250.17570.25220.23770.15360.06890.02120.00430.00050.00000.00600.04030.12090.21500.25080.20070.11150.04250.01060.00160.00010.00250.02070.07630.16650.23840.23400.15960.07460.02290.00420.00030.00100.00980.04390.11720.20510.24610.20510.117

27、20.04390.00980.0010109876543210=.80=.75=.70=.65=.60=.55=.50 xExamples: n = 10, = .35, x = 3: P(x = 3|n =10, = .35) = .2522n = 10, = .75, x = 2: P(x = 2|n =10, = .75) = .0004Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-23Binomial Distribution CharacteristicsMeanVariance and

28、Standard DeviationWheren = sample size = probability of the event of interest for any trial(1 ) = probability of no event of interest for any trialBusiness Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-24The Binomial DistributionCharacteristicsn = 5 0.2.4.6012345XP(X)n = 5 .2.4.60123

29、45XP(X)0ExamplesBusiness Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-25Using Excel For TheBinomial DistributionBusiness Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-26The Poisson DistributionDefinitionsYou use the Poisson distribution when you are interested in the

30、 number of times an event occurs in a given area of opportunity.An area of opportunity is a continuous unit or interval of time, volume, or such area in which more than one occurrence of an event can occur. The number of scratches in a cars paintThe number of mosquito bites on a personThe number of

31、computer crashes in a day Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-27The Poisson DistributionApply the Poisson Distribution when:You wish to count the number of times an event occurs in a given area of opportunityThe probability that an event occurs in one area of oppor

32、tunity 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 opportunityThe probability that two or more events occur in an area of opportunity approaches zero as the area of opportu

33、nity becomes smallerThe average number of events per unit is (lambda)Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-28Poisson Distribution Formulawhere:X = number of events in an area of opportunity = expected number of eventse = base of the natural logarithm system (2.71828.

34、)Business Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-29Poisson Distribution CharacteristicsMeanVariance and Standard Deviationwhere = expected number of eventsBusiness Statistics: A First Course, 5e 2009 Prentice-Hall, Inc.Chap 5-30Using Poisson TablesX0.100.200.300.400.500.600.700.800.90012345670.90480.0