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Chapter 6
Sampling Distribution
Prepared by Chhay Phang
H/P: 012 84 01 02
E-mail:[email protected]
Chapter Blueprint
Sampling Distributions
For sample mean
For sample proportion
Sampling procedures
Sampling Error
Sampling Error
Error and bias
The mean of the
sample mean
The standard error
Method of Sampling
The standard Error
Applications to a
normal distribution
Applications to a
normal distribution
The Central Limit
Theorem
The Central Limit
Theorem
Finite population
correction factor
Finite population
correction factor
Simple random
sampling
Systematic
sampling
Stratified
Cluster
sampling
6.1 INTRODUCTION
Inferential Statistics
Inferential Statistics involves the use of
statistic to form a conclusion, or
inference, about the corresponding
parameter.
6.2 Sampling Distributions
Sampling Error
The difference between population parameter and
sample statistic used to estimate the parameter
Sampling Distribution
A all of possible values for a statistic and
probability associate with each value.
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A. The Mean of the Sample Means
X
X


K
B. The Variance and the Standard Error
of the Sampling Distribution
Variance
x 
2
2
(
X

X
)

K

2
(
X


)

K
Standard Error
X  X
2
Standard Error & Standard Deviation
X 
2
X 

2
n

n
The finite population correction factor (fpc)
X 

n
N n
N 1
C. The Impact of Sample size on Standard
Error
D. The Central Limit Theorem
Central Limit Theorem as n large, the distribution of sample
mean will approach a normal distribution with a mean X = 
and a standard error

X 
n
6.4 Using the Sampling Distribution
Z 
6.5
X 
X
Sampling Distribution Proportions
The expected value of the Sampling distribution
p

E ( p) 
K
Standard Error
p 
p(1  p)
n
The finite population correction factor (fpc)
X 
p(1  p)
n
N n
N 1
Using the Sampling Distribution
Z 
pP
p
6.6 Sampling Methods
A.Simple Random Sample
B.Systematic Sampling
C.Stratified Sampling
D.Cluster Sampling
Chapter Exercises
1,2,6,8,9,10,11,20
Thank You!
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