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Statistics Unit 1
Unit 1
| Question | Answer |
|---|---|
| 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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