1 1 slide © 2007 thomson south-western. all rights reserved opim 303-lecture #5 jose m. cruz...
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© 2007 Thomson South-Western. All Rights Reserved© 2007 Thomson South-Western. All Rights Reserved
OPIM 303-Lecture #5OPIM 303-Lecture #5
Jose M. CruzJose M. Cruz
Assistant ProfessorAssistant Professor
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Chapter 7Chapter 7Sampling and Sampling DistributionsSampling and Sampling Distributions
xx Sampling Distribution ofSampling Distribution of
Introduction to Sampling DistributionsIntroduction to Sampling Distributions
Point EstimationPoint Estimation
Simple Random SamplingSimple Random Sampling
Other Sampling MethodsOther Sampling Methods
pp Sampling Distribution ofSampling Distribution of
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The purpose of The purpose of statistical inferencestatistical inference is to obtain is to obtain information about a population from informationinformation about a population from information contained in a sample.contained in a sample.
The purpose of The purpose of statistical inferencestatistical inference is to obtain is to obtain information about a population from informationinformation about a population from information contained in a sample.contained in a sample.
Statistical InferenceStatistical Inference
A A populationpopulation is the set of all the elements of interest. is the set of all the elements of interest. A A populationpopulation is the set of all the elements of interest. is the set of all the elements of interest.
A A samplesample is a subset of the population. is a subset of the population. A A samplesample is a subset of the population. is a subset of the population.
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The sample results provide only The sample results provide only estimatesestimates of the of the values of the population characteristics.values of the population characteristics. The sample results provide only The sample results provide only estimatesestimates of the of the values of the population characteristics.values of the population characteristics.
A A parameterparameter is a numerical characteristic of a is a numerical characteristic of a population.population. A A parameterparameter is a numerical characteristic of a is a numerical characteristic of a population.population.
With With proper sampling methodsproper sampling methods, the sample results, the sample results can provide “good” estimates of the populationcan provide “good” estimates of the population characteristics.characteristics.
With With proper sampling methodsproper sampling methods, the sample results, the sample results can provide “good” estimates of the populationcan provide “good” estimates of the population characteristics.characteristics.
Statistical InferenceStatistical Inference
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Simple Random Sampling:Simple Random Sampling:Finite PopulationFinite Population
Finite populationsFinite populations are often defined by lists such as: are often defined by lists such as:
• Organization membership rosterOrganization membership roster
• Credit card account numbersCredit card account numbers
• Inventory product numbersInventory product numbers
A A simple random sample of size simple random sample of size nn from a from a finitefinite
population of size population of size NN is a sample selected is a sample selected suchsuch
that each possible sample of size that each possible sample of size nn has has the samethe same
probability of being selected.probability of being selected.
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Simple Random Sampling:Simple Random Sampling:Finite PopulationFinite Population
In large sampling projects, computer-generatedIn large sampling projects, computer-generated random numbersrandom numbers are often used to automate the are often used to automate the sample selection process.sample selection process.
Sampling without replacementSampling without replacement is the procedure is the procedure used most often.used most often.
Replacing each sampled element before selectingReplacing each sampled element before selecting subsequent elements is called subsequent elements is called sampling withsampling with replacementreplacement..
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Infinite populations are often defined by an Infinite populations are often defined by an ongoing processongoing process whereby the elements of the whereby the elements of the population consist of items generated as though population consist of items generated as though the process would operate indefinitely.the process would operate indefinitely.
Simple Random Sampling:Simple Random Sampling:Infinite PopulationInfinite Population
A A simple random sample from an infinite populationsimple random sample from an infinite population is a sample selected such that the following conditionsis a sample selected such that the following conditions are satisfied.are satisfied.
• Each element selected comes from the sameEach element selected comes from the same population.population.
• Each element is selected independently.Each element is selected independently.
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Simple Random Sampling:Simple Random Sampling:Infinite PopulationInfinite Population
The random number selection procedure cannot beThe random number selection procedure cannot be used for infinite populations.used for infinite populations.
In the case of infinite populations, it is impossible toIn the case of infinite populations, it is impossible to obtain a list of all elements in the population.obtain a list of all elements in the population.
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ss is the is the point estimatorpoint estimator of the population standard of the population standard deviation deviation .. ss is the is the point estimatorpoint estimator of the population standard of the population standard deviation deviation ..
In In point estimationpoint estimation we use the data from the sample we use the data from the sample to compute a value of a sample statistic that servesto compute a value of a sample statistic that serves as an estimate of a population parameter.as an estimate of a population parameter.
In In point estimationpoint estimation we use the data from the sample we use the data from the sample to compute a value of a sample statistic that servesto compute a value of a sample statistic that serves as an estimate of a population parameter.as an estimate of a population parameter.
Point EstimationPoint Estimation
We refer to We refer to as the as the point estimatorpoint estimator of the population of the population mean mean .. We refer to We refer to as the as the point estimatorpoint estimator of the population of the population mean mean ..
xx
is the is the point estimatorpoint estimator of the population proportion of the population proportion pp.. is the is the point estimatorpoint estimator of the population proportion of the population proportion pp..pp
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Sampling ErrorSampling Error
Statistical methods can be used to make probabilityStatistical methods can be used to make probability statements about the size of the sampling error.statements about the size of the sampling error.
Sampling error is the result of using a subset of theSampling error is the result of using a subset of the population (the sample), and not the entirepopulation (the sample), and not the entire population.population.
The absolute value of the difference between anThe absolute value of the difference between an unbiased point estimate and the correspondingunbiased point estimate and the corresponding population parameter is called the population parameter is called the sampling errorsampling error..
When the expected value of a point estimator is equalWhen the expected value of a point estimator is equal to the population parameter, the point estimator is saidto the population parameter, the point estimator is said to be to be unbiasedunbiased..
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Sampling ErrorSampling Error
The sampling errors are:The sampling errors are:
| |p p| |p p for sample proportionfor sample proportion
| |s | |s for sample standard deviationfor sample standard deviation
| |x | |x for sample meanfor sample mean
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Example: St. Andrew’sExample: St. Andrew’s
St. Andrew’s College receivesSt. Andrew’s College receives
900 applications annually from900 applications annually from
prospective students. Theprospective students. The
application form contains application form contains
a variety of informationa variety of information
including the individual’sincluding the individual’s
scholastic aptitude test (SAT) score and whether scholastic aptitude test (SAT) score and whether or notor not
the individual desires on-campus housing.the individual desires on-campus housing.
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Example: St. Andrew’sExample: St. Andrew’s
The director of admissionsThe director of admissions
would like to know thewould like to know the
following information:following information:
• the average SAT score forthe average SAT score for
the 900 applicants, andthe 900 applicants, and
• the proportion ofthe proportion of
applicants that want to live on campus.applicants that want to live on campus.
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Example: St. Andrew’sExample: St. Andrew’s
We will now look at twoWe will now look at two
alternatives for obtaining thealternatives for obtaining the
desired information.desired information. Conducting a census of theConducting a census of the entire 900 applicantsentire 900 applicants Selecting a sample of 30Selecting a sample of 30
applicants, using Excelapplicants, using Excel
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Conducting a CensusConducting a Census
If the relevant data for the entire 900 applicants If the relevant data for the entire 900 applicants were in the college’s database, the population were in the college’s database, the population parameters of interest could be calculated using parameters of interest could be calculated using the formulas presented in Chapter 3.the formulas presented in Chapter 3.
We will assume for the moment that conducting We will assume for the moment that conducting a census is practical in this example.a census is practical in this example.
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990900
ix 990
900ix
2( )80
900ix
2( )80
900ix
Conducting a CensusConducting a Census
648.72
900p
648.72
900p
Population Mean SAT ScorePopulation Mean SAT Score
Population Standard Deviation for SAT ScorePopulation Standard Deviation for SAT Score
Population Proportion Wanting On-Campus HousingPopulation Proportion Wanting On-Campus Housing
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Simple Random SamplingSimple Random Sampling
The applicants were numbered, from 1 to 900, asThe applicants were numbered, from 1 to 900, as their applications arrived.their applications arrived.
She decides a sample of 30 applicants will be used.She decides a sample of 30 applicants will be used.
Furthermore, the Director of Admissions must obtainFurthermore, the Director of Admissions must obtain estimates of the population parameters of interest forestimates of the population parameters of interest for a meeting taking place in a few hours.a meeting taking place in a few hours.
Now suppose that the necessary data on theNow suppose that the necessary data on the current year’s applicants were not yet entered in thecurrent year’s applicants were not yet entered in the college’s database.college’s database.
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Taking a Sample of 30 ApplicantsTaking a Sample of 30 Applicants
Excel’s RAND function generatesExcel’s RAND function generates random numbers between 0 and 1random numbers between 0 and 1
Excel’s RAND function generatesExcel’s RAND function generates random numbers between 0 and 1random numbers between 0 and 1
Simple Random Sampling:Simple Random Sampling:Using ExcelUsing Excel
Step 1:Step 1: Assign a random number to each of the 900 Assign a random number to each of the 900 applicants.applicants.
Step 2:Step 2: Select the 30 applicants corresponding to the Select the 30 applicants corresponding to the 30 smallest random numbers.30 smallest random numbers.
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Using Excel to SelectUsing Excel to Selecta Simple Random Samplea Simple Random Sample
Excel Formula WorksheetExcel Formula Worksheet
Note: Rows 10-901 are not shown.Note: Rows 10-901 are not shown.
A B C D
1Applicant Number
SAT Score
On-Campus Housing
Random Number
2 1 1008 Yes =RAND()3 2 1025 No =RAND()4 3 952 Yes =RAND()5 4 1090 Yes =RAND()6 5 1127 Yes =RAND()7 6 1015 No =RAND()8 7 965 Yes =RAND()9 8 1161 No =RAND()
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Using Excel to SelectUsing Excel to Selecta Simple Random Samplea Simple Random Sample
Excel Value WorksheetExcel Value Worksheet
Note: Rows 10-901 are not shown.Note: Rows 10-901 are not shown.
A B C D
1Applicant Number
SAT Score
On-Campus Housing
Random Number
2 1 1008 Yes 0.610213 2 1025 No 0.837624 3 952 Yes 0.589355 4 1090 Yes 0.199346 5 1127 Yes 0.866587 6 1015 No 0.605798 7 965 Yes 0.809609 8 1161 No 0.33224
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Put Random Numbers in Ascending OrderPut Random Numbers in Ascending Order
Using Excel to SelectUsing Excel to Selecta Simple Random Samplea Simple Random Sample
Step 4Step 4 When the When the SortSort dialog box appears: dialog box appears:
Choose Choose Random Numbers Random Numbers in in thethe
Sort by Sort by text boxtext box
Choose Choose AscendingAscending
Click Click OKOK
Step 3Step 3 Choose the Choose the SortSort option optionStep 2Step 2 Select the Select the DataData menu menuStep 1Step 1 Select cells A2:A901Select cells A2:A901
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Using Excel to SelectUsing Excel to Selecta Simple Random Samplea Simple Random Sample
Excel Value Worksheet (Sorted)Excel Value Worksheet (Sorted)
Note: Rows 10-901 are not shown.Note: Rows 10-901 are not shown.
A B C D
1Applicant Number
SAT Score
On-Campus Housing
Random Number
2 12 1107 No 0.000273 773 1043 Yes 0.001924 408 991 Yes 0.003035 58 1008 No 0.004816 116 1127 Yes 0.005387 185 982 Yes 0.005838 510 1163 Yes 0.006499 394 1008 No 0.00667
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as Point Estimator of as Point Estimator of xx
as Point Estimator of as Point Estimator of pppp
29,910997
30 30ix
x 29,910997
30 30ix
x
2( ) 163,99675.2
29 29ix x
s
2( ) 163,99675.2
29 29ix x
s
20 30 .68p 20 30 .68p
Point EstimationPoint Estimation
Note:Note: Different random numbers would haveDifferent random numbers would haveidentified a different sample which would haveidentified a different sample which would haveresulted in different point estimates.resulted in different point estimates.
ss as Point Estimator of as Point Estimator of
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PopulationPopulationParameterParameter
PointPointEstimatorEstimator
PointPointEstimateEstimate
ParameterParameterValueValue
= Population mean= Population mean SAT score SAT score
990990 997997
= Population std.= Population std. deviation for deviation for SAT score SAT score
8080 s s = Sample std.= Sample std. deviation fordeviation for SAT score SAT score
75.275.2
pp = Population pro- = Population pro- portion wantingportion wanting campus housing campus housing
.72.72 .68.68
Summary of Point EstimatesSummary of Point EstimatesObtained from a Simple Random SampleObtained from a Simple Random Sample
= Sample mean= Sample mean SAT score SAT score xx
= Sample pro-= Sample pro- portion wantingportion wanting campus housing campus housing
pp
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Process of Statistical InferenceProcess of Statistical Inference
The value of is used toThe value of is used tomake inferences aboutmake inferences about
the value of the value of ..
xx The sample data The sample data provide a value forprovide a value for
the sample meanthe sample mean . .xx
A simple random sampleA simple random sampleof of nn elements is selected elements is selected
from the population.from the population.
Population Population with meanwith mean
= ?= ?
Sampling Distribution of Sampling Distribution of xx
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The The sampling distribution of sampling distribution of is the probability is the probability
distribution of all possible values of the sample distribution of all possible values of the sample
mean .mean .
xx
xx
Sampling Distribution of Sampling Distribution of xx
where: where: = the population mean= the population mean
EE( ) = ( ) = xx
xxExpected Value ofExpected Value of
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Sampling Distribution of Sampling Distribution of xx
Finite PopulationFinite Population Infinite PopulationInfinite Population
x n
N nN
( )1
x n
N nN
( )1
x n
x n
• is referred to as the is referred to as the standard standard error of theerror of the meanmean..
x x
• A finite population is treated as beingA finite population is treated as being infinite if infinite if nn//NN << .05. .05.
• is the finite correction factor.is the finite correction factor.( ) / ( )N n N 1( ) / ( )N n N 1
xxStandard Deviation ofStandard Deviation of
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Form of the Sampling Distribution of Form of the Sampling Distribution of xx
If we use a large (If we use a large (nn >> 30) simple random sample, the 30) simple random sample, thecentral limit theoremcentral limit theorem enables us to conclude that the enables us to conclude that thesampling distribution of can be approximated bysampling distribution of can be approximated bya normal distribution.a normal distribution.
x
When the simple random sample is small (When the simple random sample is small (nn < 30), < 30),the sampling distribution of can be consideredthe sampling distribution of can be considerednormal only if we assume the population has anormal only if we assume the population has anormal distribution.normal distribution.
x
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8014.6
30x
n
80
14.630
xn
( ) 990E x ( ) 990E x xx
Sampling Distribution of Sampling Distribution of for SAT Scoresfor SAT Scoresxx
SamplingSamplingDistributionDistribution
of of xx
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What is the probability that a simple random sampleWhat is the probability that a simple random sample
of 30 applicants will provide an estimate of theof 30 applicants will provide an estimate of the
population mean SAT score that is within +/population mean SAT score that is within +/10 of10 of
the actual population mean the actual population mean ? ?
In other words, what is the probability that will beIn other words, what is the probability that will be
between 980 and 1000?between 980 and 1000?
xx
Sampling Distribution of Sampling Distribution of for SAT Scoresfor SAT Scoresxx
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Step 1: Step 1: Calculate the Calculate the zz-value at the -value at the upperupper endpoint of endpoint of the interval.the interval.
zz = (1000 = (1000 990)/14.6= .68 990)/14.6= .68
PP((zz << .68) = .7517 .68) = .7517
Step 2:Step 2: Find the area under the curve to the left of the Find the area under the curve to the left of the upperupper endpoint. endpoint.
Sampling Distribution of Sampling Distribution of for SAT Scoresfor SAT Scoresxx
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Sampling Distribution of Sampling Distribution of for SAT Scoresfor SAT Scoresxx
Cumulative Probabilities forCumulative Probabilities for the Standard Normal the Standard Normal
DistributionDistributionz .00 .01 .02 .03 .04 .05 .06 .07 .08 .09
. . . . . . . . . . .
.5 .6915 .6950 .6985 .7019 .7054 .7088 .7123 .7157 .7190 .7224
.6 .7257 .7291 .7324 .7357 .7389 .7422 .7454 .7486 .7517 .7549
.7 .7580 .7611 .7642 .7673 .7704 .7734 .7764 .7794 .7823 .7852
.8 .7881 .7910 .7939 .7967 .7995 .8023 .8051 .8078 .8106 .8133
.9 .8159 .8186 .8212 .8238 .8264 .8289 .8315 .8340 .8365 .8389
. . . . . . . . . . .
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Sampling Distribution of Sampling Distribution of for SAT Scoresfor SAT Scoresxx
xx990990
SamplingSamplingDistributionDistribution
of of xx14.6x 14.6x
10001000
Area = .7517Area = .7517
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Step 3: Step 3: Calculate the Calculate the zz-value at the -value at the lowerlower endpoint of endpoint of the interval.the interval.
Step 4:Step 4: Find the area under the curve to the left of the Find the area under the curve to the left of the lowerlower endpoint. endpoint.
zz = (980 = (980 990)/14.6= - .68 990)/14.6= - .68
PP((zz << -.68) = .2483 -.68) = .2483
Sampling Distribution of Sampling Distribution of for SAT Scoresfor SAT Scoresxx
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Sampling Distribution of Sampling Distribution of for SAT Scoresfor SAT Scoresxx
xx980980 990990
Area = .2483Area = .2483
SamplingSamplingDistributionDistribution
of of xx14.6x 14.6x
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Sampling Distribution of Sampling Distribution of for SAT Scoresfor SAT Scoresxx
Step 5: Step 5: Calculate the area under the curve betweenCalculate the area under the curve between the lower and upper endpoints of the interval.the lower and upper endpoints of the interval.
PP(-.68 (-.68 << zz << .68) = .68) = PP((zz << .68) .68) PP((zz << -.68) -.68)
= .7517 = .7517 .2483 .2483= .5034= .5034
The probability that the sample mean SAT The probability that the sample mean SAT score willscore willbe between 980 and 1000 is:be between 980 and 1000 is:
PP(980 (980 << << 1000) = .5034 1000) = .5034xx
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xx10001000980980 990990
Sampling Distribution of Sampling Distribution of for SAT Scoresfor SAT Scoresxx
Area = .5034Area = .5034
SamplingSamplingDistributionDistribution
of of xx14.6x 14.6x
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Relationship Between the Sample SizeRelationship Between the Sample Size and the Sampling Distribution of and the Sampling Distribution of xx
Suppose we select a simple random sample of 100Suppose we select a simple random sample of 100 applicants instead of the 30 originally considered.applicants instead of the 30 originally considered.
EE( ) = ( ) = regardless of the sample size. In regardless of the sample size. In ourour example,example, E E( ) remains at 990.( ) remains at 990.
xxxx
Whenever the sample size is increased, the standardWhenever the sample size is increased, the standard error of the mean is decreased. With the increaseerror of the mean is decreased. With the increase in the sample size to in the sample size to nn = 100, the standard error of the = 100, the standard error of the mean is decreased to:mean is decreased to:
xx
808.0
100x
n
80
8.0100
xn
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Relationship Between the Sample SizeRelationship Between the Sample Size and the Sampling Distribution of and the Sampling Distribution of xx
( ) 990E x ( ) 990E x xx
14.6x 14.6x With With nn = 30, = 30,
8x 8x With With nn = 100, = 100,
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Recall that when Recall that when nn = 30, = 30, PP(980 (980 << << 1000) = .5034. 1000) = .5034.xx
Relationship Between the Sample SizeRelationship Between the Sample Size and the Sampling Distribution of and the Sampling Distribution of xx
We follow the same steps to solve for We follow the same steps to solve for PP(980 (980 << << 1000) 1000) when when nn = 100 as we showed earlier when = 100 as we showed earlier when nn = 30. = 30.
xx
Now, with Now, with nn = 100, = 100, PP(980 (980 << << 1000) = .7888. 1000) = .7888.xx
Because the sampling distribution with Because the sampling distribution with nn = 100 has a = 100 has a smaller standard error, the values of have lesssmaller standard error, the values of have less variability and tend to be closer to the populationvariability and tend to be closer to the population mean than the values of with mean than the values of with nn = 30. = 30.
xx
xx
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Relationship Between the Sample SizeRelationship Between the Sample Size and the Sampling Distribution of and the Sampling Distribution of xx
xx10001000980980 990990
Area = .7888Area = .7888
SamplingSamplingDistributionDistribution
of of xx8x 8x
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A simple random sampleA simple random sampleof of nn elements is selected elements is selected
from the population.from the population.
Population Population with proportionwith proportion
pp = ? = ?
Making Inferences about a Population Making Inferences about a Population ProportionProportion
The sample data The sample data provide a value for provide a value for
thethesample sample
proportionproportion . .
pp
The value of is usedThe value of is usedto make inferencesto make inferences
about the value of about the value of pp..
pp
Sampling Distribution ofSampling Distribution ofpp
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E p p( ) E p p( )
Sampling Distribution ofSampling Distribution ofpp
where:where:pp = the population proportion = the population proportion
The The sampling distribution of sampling distribution of is the probability is the probabilitydistribution of all possible values of the sampledistribution of all possible values of the sampleproportion .proportion .pp
pp
ppExpected Value ofExpected Value of
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pp pn
N nN
( )11
pp pn
N nN
( )11
pp pn
( )1 pp pn
( )1
is referred to as the is referred to as the standard error standard error of theof theproportionproportion..
p p
Sampling Distribution ofSampling Distribution ofpp
Finite PopulationFinite Population Infinite PopulationInfinite Population
ppStandard Deviation ofStandard Deviation of
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The sampling distribution of can be approximatedThe sampling distribution of can be approximated by a normal distribution whenever the sample size by a normal distribution whenever the sample size is large.is large.
The sampling distribution of can be approximatedThe sampling distribution of can be approximated by a normal distribution whenever the sample size by a normal distribution whenever the sample size is large.is large.
pp
The sample size is considered large whenever The sample size is considered large whenever thesethese conditions are satisfied:conditions are satisfied:
The sample size is considered large whenever The sample size is considered large whenever thesethese conditions are satisfied:conditions are satisfied:
npnp >> 5 5 nn(1 – (1 – pp) ) >> 5 5andand
Form of the Sampling Distribution ofForm of the Sampling Distribution ofpp
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For values of For values of pp near .50, sample sizes as near .50, sample sizes as small as 10small as 10permit a normal approximation.permit a normal approximation.
For values of For values of pp near .50, sample sizes as near .50, sample sizes as small as 10small as 10permit a normal approximation.permit a normal approximation.
With very small (approaching 0) or very large With very small (approaching 0) or very large (approaching 1) values of (approaching 1) values of pp, much larger , much larger samples are needed.samples are needed.
With very small (approaching 0) or very large With very small (approaching 0) or very large (approaching 1) values of (approaching 1) values of pp, much larger , much larger samples are needed.samples are needed.
Form of the Sampling Distribution ofForm of the Sampling Distribution ofpp
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Recall that 72% of theRecall that 72% of the
prospective students applyingprospective students applying
to St. Andrew’s College desireto St. Andrew’s College desire
on-campus housing.on-campus housing.
Example: St. Andrew’s CollegeExample: St. Andrew’s College
Sampling Distribution ofSampling Distribution ofpp
What is the probability thatWhat is the probability that
a simple random sample of 30 applicants will providea simple random sample of 30 applicants will provide
an estimate of the population proportion of applicantan estimate of the population proportion of applicant
desiring on-campus housing that is within plus ordesiring on-campus housing that is within plus or
minus .05 of the actual population proportion?minus .05 of the actual population proportion?
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For our example, with For our example, with nn = 30 and = 30 and pp = .72, the normal distribution is an acceptable = .72, the normal distribution is an acceptable approximation because:approximation because:
nn(1 - (1 - pp) = 30(.28) = 8.4 ) = 30(.28) = 8.4 >> 5 5
andand
npnp = 30(.72) = 21.6 = 30(.72) = 21.6 >> 5 5
Sampling Distribution ofSampling Distribution ofpp
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p
.72(1 .72).082
30
p
.72(1 .72).082
30
( ) .72E p ( ) .72E p pp
SamplingSamplingDistributionDistribution
of of pp
Sampling Distribution ofSampling Distribution ofpp
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Step 1: Step 1: Calculate the Calculate the zz-value at the -value at the upperupper endpoint of endpoint of the interval.the interval.
zz = (.77 = (.77 .72)/.082 = .61 .72)/.082 = .61
PP((zz << .61) = .7291 .61) = .7291
Step 2:Step 2: Find the area under the curve to the left of the Find the area under the curve to the left of the upperupper endpoint. endpoint.
Sampling Distribution ofSampling Distribution ofpp
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Cumulative Probabilities forCumulative Probabilities for the Standard Normal the Standard Normal
DistributionDistribution
Sampling Distribution ofSampling Distribution ofpp
z .00 .01 .02 .03 .04 .05 .06 .07 .08 .09
. . . . . . . . . . .
.5 .6915 .6950 .6985 .7019 .7054 .7088 .7123 .7157 .7190 .7224
.6 .7257 .7291 .7324 .7357 .7389 .7422 .7454 .7486 .7517 .7549
.7 .7580 .7611 .7642 .7673 .7704 .7734 .7764 .7794 .7823 .7852
.8 .7881 .7910 .7939 .7967 .7995 .8023 .8051 .8078 .8106 .8133
.9 .8159 .8186 .8212 .8238 .8264 .8289 .8315 .8340 .8365 .8389
. . . . . . . . . . .
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.77.77.72.72
Area = .7291Area = .7291
pp
SamplingSamplingDistributionDistribution
of of pp
.082p .082p
Sampling Distribution ofSampling Distribution ofpp
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Step 3: Step 3: Calculate the Calculate the zz-value at the -value at the lowerlower endpoint of endpoint of the interval.the interval.
Step 4:Step 4: Find the area under the curve to the left of the Find the area under the curve to the left of the lowerlower endpoint. endpoint.
zz = (.67 = (.67 .72)/.082 = - .61 .72)/.082 = - .61
PP((zz << -.61) = .2709 -.61) = .2709
Sampling Distribution ofSampling Distribution ofpp
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.67.67 .72.72
Area = .2709Area = .2709
pp
SamplingSamplingDistributionDistribution
of of pp
.082p .082p
Sampling Distribution ofSampling Distribution ofpp
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PP(.67 (.67 << << .77) = .4582 .77) = .4582pp
Step 5: Step 5: Calculate the area under the curve betweenCalculate the area under the curve between the lower and upper endpoints of the interval.the lower and upper endpoints of the interval.
PP(-.61 (-.61 << zz << .61) = .61) = PP((zz << .61) .61) PP((zz << -.61) -.61)
= .7291 = .7291 .2709 .2709= .4582= .4582
The probability that the sample proportion of applicantsThe probability that the sample proportion of applicantswanting on-campus housing will be within +/-.05 of thewanting on-campus housing will be within +/-.05 of theactual population proportion :actual population proportion :
Sampling Distribution ofSampling Distribution ofpp
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.77.77.67.67 .72.72
Area = .4582Area = .4582
pp
SamplingSamplingDistributionDistribution
of of pp
.082p .082p
Sampling Distribution ofSampling Distribution ofpp
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Other Sampling MethodsOther Sampling Methods
Stratified Random SamplingStratified Random Sampling Cluster SamplingCluster Sampling Systematic SamplingSystematic Sampling Convenience SamplingConvenience Sampling Judgment SamplingJudgment Sampling