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Transcript of 6 - 1 © 1998 Prentice-Hall, Inc. Chapter 6 Sampling Distributions.
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Chapter 6Chapter 6
Sampling DistributionsSampling Distributions
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Learning ObjectivesLearning Objectives
1.1. Describe the properties of estimatorsDescribe the properties of estimators
2.2. Explain sampling distributionExplain sampling distribution
3.3. Describe the relationship between Describe the relationship between populations & sampling distributionspopulations & sampling distributions
4.4. State the Central Limit TheoremState the Central Limit Theorem
5.5. Solve a probability problem involving Solve a probability problem involving sampling distributionssampling distributions
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Inferential StatisticsInferential Statistics
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Types of Types of Statistical Statistical
ApplicationsApplications
StatisticalMethods
DescriptiveStatistics
InferentialStatistics
StatisticalMethods
DescriptiveStatistics
InferentialStatistics
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Inferential StatisticsInferential Statistics
1.1. InvolvesInvolves EstimationEstimation Hypothesis Hypothesis
testingtesting
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Inferential StatisticsInferential Statistics
1.1. InvolvesInvolves EstimationEstimation Hypothesis Hypothesis
testingtesting
Population?Population?
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Inferential StatisticsInferential Statistics
1.1. InvolvesInvolves EstimationEstimation Hypothesis Hypothesis
testingtesting
2.2. PurposePurpose Make decisions Make decisions
about population about population characteristicscharacteristics
Population?Population?
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Inference ProcessInference Process
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Inference ProcessInference Process
PopulationPopulation
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Inference ProcessInference Process
PopulationPopulation
SampleSample
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Inference ProcessInference Process
PopulationPopulation
SampleSample
Sample Sample statistic statistic
((XX))
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Inference ProcessInference Process
PopulationPopulation
SampleSample
Sample Sample statistic statistic
((XX))
Estimate Estimate & test & test populatiopopulation n parameterparameter
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
1.1. Random variables used to estimate a Random variables used to estimate a population parameterpopulation parameter Sample mean, sample proportion, sample Sample mean, sample proportion, sample
medianmedian
2.2. Example: Sample meanExample: Sample meanxx is an estimator is an estimator of population mean of population mean IfIfxx = 3 then 3 is the = 3 then 3 is the estimateestimate of of
3.3. Theoretical basis is sampling distributionTheoretical basis is sampling distribution
EstimatorsEstimators
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Sampling DistributionsSampling Distributions
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
1.1. TheoreticalTheoretical probability distribution probability distribution
2.2. Random variable is Random variable is sample statisticsample statistic Sample mean, sample proportion etc.Sample mean, sample proportion etc.
3.3. Results from drawing Results from drawing allall possible possible samples of a samples of a fixedfixed size size
4.4. List of all possible [List of all possible [x, x, PP((x) ] pairsx) ] pairs Sampling distribution of meanSampling distribution of mean
Sampling Sampling DistributionDistribution
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
DevelopingDevelopingSampling Sampling
DistributionsDistributions
Population size, Population size, NN = 4 = 4
Random variable, Random variable, xx, , is # televisions ownedis # televisions owned
Values of Values of xx: 1, 2, 3, 4: 1, 2, 3, 4
Equally distributed (p=1/4)Equally distributed (p=1/4)
© 1984-1994 T/Maker Co.
Suppose there’s a Suppose there’s a population ...population ...
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Population Population CharacteristicsCharacteristics
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Population Population CharacteristicsCharacteristics
Summary MeasuresSummary Measures
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Population Population CharacteristicsCharacteristics
Summary MeasuresSummary Measures
12.1)(
5.2)(
1
2
1
)(
ipi
ip
N
ii
N
iii
XX
XX
12.1)(
5.2)(
1
2
1
)(
ipi
ip
N
ii
N
iii
XX
XX
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Population Population CharacteristicsCharacteristics
.0
.1
.2
.3
1 2 3 4
.0
.1
.2
.3
1 2 3 4
Population DistributionPopulation DistributionSummary MeasuresSummary Measures
12.1
5.2
12.1
5.2
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Let’s Draw All Let’s Draw All Possible Samples of Possible Samples of
Size Size nn = 2 = 2
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Let’s Draw All Let’s Draw All Possible Samples of Possible Samples of
Size Size nn = 2 = 2
1st 2nd ObservationObs 1 2 3 4
1 1,1 1,2 1,3 1,4
2 2,1 2,2 2,3 2,4
3 3,1 3,2 3,3 3,4
4 4,1 4,2 4,3 4,4
1st 2nd ObservationObs 1 2 3 4
1 1,1 1,2 1,3 1,4
2 2,1 2,2 2,3 2,4
3 3,1 3,2 3,3 3,4
4 4,1 4,2 4,3 4,4
16 Samples16 Samples
Sample with replacementSample with replacement
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Let’s Draw All Let’s Draw All Possible Samples of Possible Samples of
Size n=2Size n=2
1st 2nd ObservationObs 1 2 3 4
1 1,1 1,2 1,3 1,4
2 2,1 2,2 2,3 2,4
3 3,1 3,2 3,3 3,4
4 4,1 4,2 4,3 4,4
1st 2nd ObservationObs 1 2 3 4
1 1,1 1,2 1,3 1,4
2 2,1 2,2 2,3 2,4
3 3,1 3,2 3,3 3,4
4 4,1 4,2 4,3 4,4
1st 2nd ObservationObs 1 2 3 4
1 1.0 1.5 2.0 2.5
2 1.5 2.0 2.5 3.0
3 2.0 2.5 3.0 3.5
4 2.5 3.0 3.5 4.0
1st 2nd ObservationObs 1 2 3 4
1 1.0 1.5 2.0 2.5
2 1.5 2.0 2.5 3.0
3 2.0 2.5 3.0 3.5
4 2.5 3.0 3.5 4.0
16 Samples16 Samples 16 Sample Means16 Sample Means
Sample with replacementSample with replacement
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Sampling Sampling DistributionDistribution
of All Sample of All Sample MeansMeans
.0
.1
.2
.3
1.0 1.5 2.0 2.5 3.0 3.5 4.0
X
P(X)
.0
.1
.2
.3
1.0 1.5 2.0 2.5 3.0 3.5 4.0
X
P(X)
1st 2nd ObservationObs 1 2 3 4
1 1.0 1.5 2.0 2.5
2 1.5 2.0 2.5 3.0
3 2.0 2.5 3.0 3.5
4 2.5 3.0 3.5 4.0
1st 2nd ObservationObs 1 2 3 4
1 1.0 1.5 2.0 2.5
2 1.5 2.0 2.5 3.0
3 2.0 2.5 3.0 3.5
4 2.5 3.0 3.5 4.0
16 Sample Means16 Sample Means Sampling Sampling DistributionDistribution
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Summary Measures Summary Measures ofof
All Sample Means All Sample Means (n=16)(n=16)
5.216
0.45.10.11
n
Xn
ii
x 5.216
0.45.10.11
n
Xn
ii
x
79.0
16
5.20.45.25.15.20.1 222
1
2
n
Xn
ixi
x
79.0
16
5.20.45.25.15.20.1 222
1
2
n
Xn
ixi
x
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Comparison of Comparison of Population & Sampling Population & Sampling
DistributionDistribution
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Comparison of Comparison of Population & Sampling Population & Sampling
DistributionDistribution
.0
.1
.2
.3
1 2 3 4
P(X)
.0
.1
.2
.3
1 2 3 4
P(X)
PopulationPopulation
112. 112.
2 5. 2 5.
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Comparison of Comparison of Population & Sampling Population & Sampling
DistributionDistribution
.0
.1
.2
.3
1 1.5 2 2.5 3 3.5 4
X
P(X)
.0
.1
.2
.3
1 1.5 2 2.5 3 3.5 4
X
P(X)
.0
.1
.2
.3
1 2 3 4
P(X)
.0
.1
.2
.3
1 2 3 4
P(X)
PopulationPopulation Sampling DistributionSampling Distribution
x 2 5. x 2 5.
x 0 79. x 0 79. 112. 112.
2 5. 2 5.
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Standard Error of Standard Error of MeanMean
1.1. Standard deviation of all possible Standard deviation of all possible sample means,sample means,xx Measures scatter in all sample means,Measures scatter in all sample means,xx
2.2. Less than pop. standard deviationLess than pop. standard deviation
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Standard Error of Standard Error of MeanMean
1.1. Standard deviation of all possible Standard deviation of all possible sample means,sample means,xx Measures scatter in all sample means,Measures scatter in all sample means,xx
2.2. Less than pop. standard deviationLess than pop. standard deviation
3.3. Formula (sampling with replacement)Formula (sampling with replacement)
x n
x n
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Properties of Sampling Properties of Sampling Distribution of MeanDistribution of Mean
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Properties of Properties of Sampling Sampling
Distribution of MeanDistribution of Mean1.1. UnbiasednessUnbiasedness
Mean of sampling distribution equals population Mean of sampling distribution equals population meanmean
2.2. EfficiencyEfficiency Sample mean comes closer to population mean Sample mean comes closer to population mean
than any other unbiased estimatorthan any other unbiased estimator
3.3. ConsistencyConsistency As sample size increases, variation of sample As sample size increases, variation of sample
mean from population mean decreasesmean from population mean decreases
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
UnbiasednessUnbiasedness
X
P(X)
CA
UnbiasedUnbiased BiasedBiased
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
EfficiencyEfficiency
X
P(X)
A
B
Sampling Sampling distribution distribution of medianof median
Sampling Sampling distribution distribution
of meanof mean
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
ConsistencyConsistency
X
P(X)
A
B
Smaller Smaller sample sample
sizesize
Larger Larger sample sample
sizesize
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Sampling from Sampling from Normal PopulationsNormal Populations
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Sampling from Sampling from Normal PopulationsNormal Populations
Central TendencyCentral TendencyCentral TendencyCentral Tendency
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Sampling from Sampling from Normal PopulationsNormal Populations
Central TendencyCentral TendencyCentral TendencyCentral Tendency x x
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Sampling from Sampling from Normal PopulationsNormal Populations
Central TendencyCentral Tendency
DispersionDispersion
Sampling Sampling withwith replacementreplacement
Central TendencyCentral Tendency
DispersionDispersion
Sampling Sampling withwith replacementreplacement
x n
x n
x x
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
= 50
= 10
X = 50
= 10
X
Sampling from Sampling from Normal PopulationsNormal Populations
Central TendencyCentral Tendency
DispersionDispersion
Sampling Sampling withwith replacementreplacement
Central TendencyCentral Tendency
DispersionDispersion
Sampling Sampling withwith replacementreplacement
Population DistributionPopulation DistributionPopulation DistributionPopulation Distribution
x n
x n
x x
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
= 50
= 10
X = 50
= 10
X
Sampling from Sampling from Normal PopulationsNormal Populations
Central TendencyCentral Tendency
DispersionDispersion
Sampling Sampling withwith replacementreplacement
Central TendencyCentral Tendency
DispersionDispersion
Sampling Sampling withwith replacementreplacement
Population DistributionPopulation DistributionPopulation DistributionPopulation Distribution
Sampling DistributionSampling DistributionSampling DistributionSampling Distribution
x n
x n
x x
X = 50- XX = 50- X
n =16n =16
XX = 2.5 = 2.5
n = 4n = 4
XX = 5 = 5
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Standardizing Standardizing Sampling Distribution Sampling Distribution
of Meanof Mean
Suppose you want to make probability statements about the sampling distribution...
Suppose you want to make probability statements about the sampling distribution...
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Standardizing Standardizing Sampling Distribution Sampling Distribution
of Meanof Mean
XX
X
XX
X
Sampling DistributionSampling Distribution
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Standardizing Standardizing Sampling Distribution Sampling Distribution
of Meanof Mean
XX
X
XX
X
Sampling DistributionSampling Distribution
= 0
= 1
Z = 0
= 1
Z
Standardized Normal Distribution
Standardized Normal Distribution
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Standardizing Standardizing Sampling Distribution Sampling Distribution
of Meanof Mean
XX
X
XX
X
Sampling DistributionSampling Distribution
= 0
= 1
Z = 0
= 1
Z
ZX X
n
x
x
Z
X X
n
x
x
Standardized Normal Distribution
Standardized Normal Distribution
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© 1998 Prentice-Hall, Inc.© 1998 Prentice-Hall, Inc.
Thinking ChallengeThinking Challenge
You’re an operations You’re an operations analyst for AT&T. Long-analyst for AT&T. Long-distance telephone calls distance telephone calls are normally distribution are normally distribution with with = 8 = 8 min. & min. & = 2 = 2
min. If you select random min. If you select random samples of samples of 25 25 calls, what calls, what percentage of the percentage of the samplesample means means would be between would be between 7.87.8 & & 8.2 8.2 minutes?minutes?
© 1984-1994 T/Maker Co.
AloneAlone GroupGroup Class Class
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Sampling Sampling Distribution Distribution
Solution*Solution*
8
X = .4
7.8 8.2 X8
X = .4
7.8 8.2 X
Sampling DistributionSampling Distribution
ZX
n
ZX
n
7 8 82 25
50
8 2 82 25
50
..
..
ZX
n
ZX
n
7 8 82 25
50
8 2 82 25
50
..
..
0
= 1
-.50 Z.500
= 1
-.50 Z.50
.3830.3830.3830.3830
.1915.1915.1915.1915
Standardized Normal Distribution
Standardized Normal Distribution
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Sampling from Sampling from Non-Normal Non-Normal PopulationsPopulations
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Sampling from Sampling from Non-Normal Non-Normal PopulationsPopulations
Central TendencyCentral TendencyCentral TendencyCentral Tendency
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Sampling from Sampling from Non-Normal Non-Normal PopulationsPopulations
Central TendencyCentral TendencyCentral TendencyCentral Tendency x x
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Sampling from Sampling from Non-Normal Non-Normal PopulationsPopulations
Central TendencyCentral Tendency
DispersionDispersion
Sampling Sampling withwith replacementreplacement
Central TendencyCentral Tendency
DispersionDispersion
Sampling Sampling withwith replacementreplacement
x n
x n
x x
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= 50
= 10
X = 50
= 10
X
Sampling from Sampling from Non-Normal Non-Normal PopulationsPopulations
Central TendencyCentral Tendency
DispersionDispersion
Sampling Sampling withwith replacementreplacement
Central TendencyCentral Tendency
DispersionDispersion
Sampling Sampling withwith replacementreplacement
Population DistributionPopulation DistributionPopulation DistributionPopulation Distribution
x n
x n
x x
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= 50
= 10
X = 50
= 10
X
Sampling from Sampling from Non-Normal Non-Normal PopulationsPopulations
Central TendencyCentral Tendency
DispersionDispersion
Sampling Sampling withwith replacementreplacement
Central TendencyCentral Tendency
DispersionDispersion
Sampling Sampling withwith replacementreplacement
Population DistributionPopulation DistributionPopulation DistributionPopulation Distribution
Sampling DistributionSampling DistributionSampling DistributionSampling Distribution
x n
x n
x x
X = 50- XX = 50- X
n =30n =30
XX = 1.8 = 1.8
n = 4n = 4
XX = 5 = 5
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Central Limit TheoremCentral Limit Theorem
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Central Limit Central Limit TheoremTheorem
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XX
Central Limit Central Limit TheoremTheorem
As As sample sample size gets size gets large large enough enough (n (n 30) ...30) ...
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XX
Central Limit Central Limit TheoremTheorem
As As sample sample size gets size gets large large enough enough (n (n 30) ...30) ...
sampling sampling distribution distribution becomes becomes almost almost normal.normal.
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XX
Central Limit Central Limit TheoremTheorem
As As sample sample size gets size gets large large enough enough (n (n 30) ...30) ...
sampling sampling distribution distribution becomes becomes almost almost normal.normal.
x n
x n
x x
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ConclusionConclusion
1.1. Described the properties of estimatorsDescribed the properties of estimators
2.2. Explained sampling distributionExplained sampling distribution
3.3. Described the relationship between Described the relationship between populations & sampling distributionspopulations & sampling distributions
4.4. Stated the Central Limit TheoremStated the Central Limit Theorem
5.5. Solved a probability problem involving Solved a probability problem involving sampling distributionssampling distributions
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This Class...This Class...
1.1. What was the most important thing you What was the most important thing you learned in class today?learned in class today?
2.2. What do you still have questions about?What do you still have questions about?
3.3. How can today’s class be improved?How can today’s class be improved?
Please take a moment to answer the following questions in writing:
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End of Chapter
Any blank slides that follow are blank intentionally.