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Basic concepts: populations, probability samples, sample design, bias, sampling and nonsampling errors. Simple probability sampling: statistics, estimates, weights, choosing the sample size, confidence limits, use of R, systematic sampling. Stratified sampling: theory, defining strata, sample allocation, precision, advantages over
Sampling techniques, simple random sampling, stratified sampling, cluster sampling, systematic sampling, ratio sampling, estimation of standard error, questionnaire design, regression estimation. The students will carry out a survey that best suits their needs and interests.
Course Description (from the Undergraduate Calendar 2019-2020) This course focuses on the design and analysis of survey samples for finite populations. Topics covered include: non-probability and probability sampling, simple random sampling, stratified sampling, cluster sampling, systematic sampling,
This course is concerned with the design of sample surveys and the statistical analysis of data collected from such surveys. Topics covered are: experiments and surveys, steps in planning a survey; randomisation approach to sampling and estimation, sampling distribution of estimator, expected values, variances, generalisation of probability ...
This course focuses on the design and analysis of survey samples for finite populations. Topics covered include: non -probability and probability sampling, simple random sampling, stratified
The aim of this course is to cover sampling design and analysis methods that would be useful for research and management in many field. A well designed sampling procedure ensures that we can summarize and analyze data with a minimum of assumptions and complications.
Welcome to the course notes for STAT 506: Sampling Theory and Methods. These notes are designed and developed by Penn State’s Department of Statistics and offered as open educational resources.
