DCA 102 Chap 10 - Sampling & Estimation

# DCA 102 Chap 10 - Sampling & Estimation - BUSINESS...

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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 sub-set 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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DCA 102 Chap 10 - Sampling & Estimation - BUSINESS...

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