2015-7-3 xlpeng 1 stat 4060 design and analysis of surveys exam: 60% mid test: 20% mini project: 10%...
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STAT 4060 Design and Analysis of Surveys
Exam: 60% Mid Test: 20% Mini Project: 10% Continuous assessment: 10%
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What we have learned:
1. Simple random sampling, confidence interval and choice of sample size.
2. Ratio and regression estimators, systematic sampling.
3. Stratified random sampling, allocation of stratum weights.
4. Cluster sampling.
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Population Parameter
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Sample Statistics
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Simple random sampling
We shall consider the use of simple random samples for estimating the three population characteristics:
the population mean
the population total
and the proportion P.
We shall discuss how any estimators behave in terms of their sampling distributions. The variance is often a crucial measure.
1
1, denoted , ;
N
jj
Y Y YN
1
, denoted , ;N
T T jj
Y Y Y
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Proof of (1.9)
n
SfS
Nn
nS
Nn
N
yynnyVarn
YnYyynnYyVarnn
YnyEynnEyn
YyyyEn
YnyEyEyEyVar
jii
jii
jii
jijii
n
ii
222
22222
2222
222
22
1
22
)1(11
)),cov()1()((1
})),)(cov(1())(({1
})1({1
)(1
)/()()()(
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Confidence interval for the population mean
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Ratio Estimation and Regression Estimation(Chapter 4, Textbook, Barnett, V., 1991)
2.1 Estimation of a population ratio: The ratio estimator In some situations it is useful to estimate a (positive) ratio of two
population characteristics: the totals, or means, of two (positive) variables X and Y.
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The sample average of ratio
unbiased for estimating the population mean
Two obvious estimators of R are
The ratio of the sample averages
is widely used.
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1 1
1 1( / )
n n
i i ii i
r y x rn n
/ /T Tr y x y x
1 1
1 1( / )
N N
j j jj j
R R Y XN N
but biased for estimating R
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The bias in estimating R by r
The bias in estimating R by r is the expectation of the following difference:
(2.3)
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( ) /r R y Rx x 1
1y Rx x X
X X
2
1 .y Rx x X x X
X X X
2
[( )( )]( )
y Rx E y Rx x XE r R E
X X
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Discussion about the bias
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≈
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(2.5)
2
21
2 2 22
( )1
1
12
Nj j
j
Y YX X
Y RXf
nX N
fS RS R S
nX
( ) ( )j j j j jZ Y RX Y Y RX RX
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2.2 Ratio estimation of a population mean or total
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( / )Ry rX X x y
( / )TR T Ry rX NX x y Ny
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Variance of ratio estimator
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The estimate of the ratio R of the present weight to prestudy weight for the herd is:
Solution:
000929.012
646.848,8)
500
121(
880
11)(
22
2
rSXn
frVar
030485.0000929.0)( rse
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This examines when the variance of (2.10) could be less or greater than that of (1.9)
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2.3 Regression estimation
Condition (2.15.1) demands that X and Y be linearly related, but, if the linear relationship does not pass through the origin, then, it suggests considering an alternative estimator known as regression estimator.
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2.3 Regression estimation
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A practicable simple linear regression model is (2.17)
.
An ideal (perfect) linear relationship is
(2.16)
)( jj XXbYY
(2.18)
jjj EXXbYY )(
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2.3 Regression estimation
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Consider the average (mean) of either (2.16) or (2.17),
( )Ly y b X x (2.19)
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2.3 Regression estimation
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2( ) [( ) ]L LVar y E y Y 2
2 2 2
2 2
{[( ) ( )] }
1( 2 )
1(1 )
L
Y YX X
Y YX
E y Y b x X
fS bS b S
nfS
n
21( )Y
fS Var y
n
(2.20)
y
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2.3 Regression estimation
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From (2.20),
2 2 21min { ( )} min ( 2 )b L b Y YX X
fVar y S bS b S
n
2 21(1 )Y YX
fS
n
The minimum is obtained with 2min / /YX X YX Y Xb b S S S S
Y
Thus the most efficient regression estimator of is
( / )( )L YX Y Xy y S S X x
(2.22)
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2.3 Regression estimation
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The optimal value of b of (2.22) suggests the obvious estimate:
1min 2 2
1
( )( )( )
( )
n
i iyx in
x ii
y y x xsb b
s x x
(2.24)
( )Ly y b X x (2.25)
which enjoys the following asymptotic properties:
1( ) ( )LE y Y O n
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2.3 Regression estimation
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Asymptotic properties:
( )LVar y
2 2 2 3/21( / ) ( )Y YX X
fS S S O n
n
21( ) ( )L y yx
fV y s bs
n
(2.27)
(2.26) )()1(1 2/322
nOSn
fXYX
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2.4 Comparison of ratio and regression estimators
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2.4 Comparison of ratio and regression estimators
2 2 2 21( ) ( ) 2R L X YX Y X YX Y
fV y Var y R S R S S S
n
21X YX Y
fRS S
n
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Stratified Simple Random Sampling(Chapter 5, Textbook, Barnett, V., 1991)
Consider another sampling method:
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Some Notations
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To estimate the population mean of a finite population, we assume that the population is stratified, that is to say it has been divided into k non-overlapping groups, or strata, of sizes:
The stratum means and variances are denoted by
and
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Estimation of Population Characteristicsin Stratified Populations
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Estimating
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The stratified sample mean is defined as
Here we assume the weights Wi=Ni /N is given (known).
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The mean and variance of
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Note that
Since
Because it is assumed that “sampling in different strata are independent”, that is
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Simple random sampling
Stratified sampling with proportional allocation
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(a) When stratum size is large enough:
N
N i
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(b) When stratum size is not large enough:
The stratified sample mean will be more efficient than the s.r. sample mean
If and only if variation between the stratum means is sufficiently large
compared with within-strata variation!
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Optimum Choice of Sample Size
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To achieve required precision of estimation Some cost limitation
The simplest form assumes that there is some overhead cost, c0 of administering
The survey, and that individual observations from the ith stratum each cost an
Amount ci. Thus the total cost is:
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I. Minimum variance for fixed cost (Cont.)
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I. Minimum variance for fixed cost (Cont.)
Then
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II. Minimum cost for fixed variance
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Consider to satisfy for the minimum possible total cost.
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iii nwnwGiven ,
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Comparison of proportional allocation and optimum allocation
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Thus the extent of the potential gain from optimum (Neyman) allocation
Compared with proportional allocation depends on the variability of the
stratum variances: the larger this is, the greater the relative advantage
Of optimum allocation.
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Cluster Sampling(Chapter 6, Textbook, Barnett, V., 1991)
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Comparison of s.r. sampling with cluster sampling
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Systematic Sampling
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Systematic sample can be viewed as a cluster sample of size m=1!
Systematic sample mean
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Systematic Sampling
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Comparison of s.r. sampling with systimatic sampling
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Two ways of estimating ---
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Y
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n
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