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Statistics Unit 1

Unit 1

The purpose Statistical Inference is to obtain information about a population from information information about a population from information contained in a sample
A popultiaon is the set of all the elements of interest
A sample is a subset of the population
The target population is the population we want to make inferences about.
The sampled population is the population from which the sample is actually selected
Two types of errors can occur in conducting a survey Sampling error AND Non-sampling error
Sampling Error This is defined as the magnitude of the difference between the point estimate, computed from the sample, and the population parameter. It occurs because not every element in the population is surveyed. Not in census
Non-sampling Error both a census and a sample survey. Measurement error Errors due to non-response and so on
Sampling Methods ( 2 of them) Non-probabilistic sampling AND Probabilistic sampling
Two essential considerations for conducting a sample survey are: : (1) degree of representativeness, and (2) sampling costs.
Representativeness A sample is considered representative of a population to the extent that its |composition, in all important aspects/joint frequency distribution of all variables of interest| is identical with that of the population
Non-probabilistic Sampling Methods no probability statistically valid statements cannot be made include convenience and judgement sampling cost is lower
Probabilistic Sampling Methods The probability of obtaining each possible sample can be computed.
Probabilistic Sampling Methods includes 4 different samplings simple random, stratified random, cluster, and systematic sampling
Simple Random Sampling A simple random sample of size n from a finite p po ulation of size N is a sample selected such that every possible sample of size n has the same probability of being selected.
Stratified Random Sampling In stratified random sampling, the population is divided to H strata Then, for each stratum h, a simple random sample of size nh is selected. Example:
Cluster Sampling Cluster sampling requires that the population be divided into N groups of elements called clusters. We then select a simple random sample of n clusters
________ sampling tends to provide good results when the elements within the clusters are heterogeneous. Cluster
A primary application of ________ _________ involves area sampling, where the clusters are local authority areas, postcode areas, cities or other well-defined geographic entities cluster sampling
Systematic Sampling Systematic sampling is often used as an alternative to simple random sampling, which can be time consuming if a large population is involved consuming if a large population is involved.
Created by: NikaKirillova



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