Sampling Distributions

Sampling Distributions - Chapter 6 Sampling Distributions...

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Unformatted text preview: Chapter 6 : Sampling Distributions Populations and Samples The Sampling Distribution of the mean ( σ known) The Sampling Distribution of the mean ( σ unknown) The Sampling Distribution of the Variance 6.1 Populations and Samples Population: A set or collection of all the objects, actual or conceptual and mainly the set of numbers, measurements or observations which are under investigation. Finite Population : All students in BITS Pilani Goa Campus Infinite Population : Total water in the sea or all the sand particle in sea shore. Populations are often described by the distributions of their values, and it is common practice to refer to a population in terms of this distribution Finite populations are described by the actual distribution of its values and infinite populations are described by corresponding probability distribution or probability density. For example, we may refer to a number of flips of a coin as a sample from a “binomial population” or to certain measurements as a sample from a “normal population”. Populations and Samples (cont’d) “Population f ( x )” means a population is described by a frequency distribution, a probability distribution or a density f ( x ). If a population is infinite it is impossible to observe all its values, and even if it is finite it may be impractical or uneconomical to observe it in its entirety. Thus it is necessary to use a sample . Sample: A part of population collected for investigation which needed to be representative of population and to be large enough to contain all information about population Random Sample (finite population): A set of observations X 1 , X 2 , …, X n constitutes a random sample of size n from a finite population of size N , if its values are chosen so that each subset of n of the N elements of the population has the same probability of being selected. Populations and Samples (cont’d) Random Sample (infinite Population): A set of observations X 1 , X 2 , …, X n constitutes a random sample of size n from the infinite population ƒ( x ) if: 1. Each X i is a random variable whose distribution is given by ƒ( x ) 2. These n random variables are independent. We consider two types of random sample: those drawn with replacement and those drawn without replacement . Populations and Samples (cont’d) The purpose of most statistical investigations is to generalize from information contained in random samples about the population from which the samples were obtained. In particular, we are usually concerned with the problem of making inferences about the parameters of populations, such as the mean μ or the standard deviation σ . In making such inferences, we use statistics such as and s , namely quantities calculated on the basis of sample observations....
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This note was uploaded on 02/23/2011 for the course MATH 112 taught by Professor Ritadubey during the Spring '11 term at Amity University.

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Sampling Distributions - Chapter 6 Sampling Distributions...

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