BUSINESS STATISTICS (DCA 102)
CHAPTER 10 : SAMPLING AND ESTIMATION
PREPARED BY : MONA SEOW
Page 1 of 5
CHAPTER TEN : SAMPLING AND ESTIMATION
Introduction
•
To learn the population characteristics we have to use samples. A sample is a
proportion or subset of the population, and is used to predict the characteristics of
the population. Based on the principle of statistical regularity and the principle of
“Inertia of large numbers”, if the collected sample is not biased and is large enough,
to a certain extent the sample results will reveal the characteristics of the
population.
•
Population parameters can be predicted from samples. We can collect a sample
from the population and compute appropriate sample statistics. The sample statistics
is used to predict the value of the population parameters. This is known as
e
stimation.
Estimation
can be defined as “ techniques used to establish a value for
an unknown population parameter using the respective sample statistics”.
Population
Sample
Census
Random Sampling
Attribute Sampling
Population Parameter
is the value obtained from a set of data which represents all the
observations in the designated population.
Sample Statistic
is the value that describes the sample.
The table below shows the designated statistical value, the symbol used to describe a
sample and a population.
Symbols Used
Statistical value
Sample statistic
Population parameter
Mean
x
μ
Standard Deviation
s
σ
Variance
s
2
σ
2
Proportion
p
π
Standard Error (SE)
Standard error measures the sampling error associated with a sampling distribution. The
larger the size of the sample taken, the smaller the standard error.
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 Spring '11
 Rahendra
 Statistics, Normal Distribution, MONA SEOW

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