The objective of advanced sampling is to develop efficient methods for selecting samples from a population, while the objective of design of experiments is to plan and conduct experiments to extract the maximum amount of information with the minimum amount of resources.
Students will be able to:
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Learning outcomes (at course level |
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Paper Code |
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DSTT 701(C) |
Advance Sampling and Design of Experiments
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CO 92: Students will learn Cluster Sampling and Two-Stage Sampling, and how to use Ratio, Difference, Regression and Product methods for efficient estimation of population parameters with the help of Auxiliary Information
CO 93: Students will learn Double Sampling for ratio estimation, PPSWR/PPSWOR sampling, and efficient estimation methods like Cumulative Total and Lahiri's method for population parameter estimation
CO 94: Students will learn how to minimize non-sampling errors and estimate the variance of the sample mean in survey research, as well as techniques for addressing non-response error, such as Hanson and Horvitz, Politz and Simmon, and Warness
CO 95: Students will be able to design and analyze factorial experiments using Latin Square Design, 22 23 and 2n factorial designs, and understand confounding effects and their impact on experimental outcomes, and construct confounded factorial experiments using 2 to the power n.
CO 96: Students will be able to apply experimental design and analysis techniques, including the Analysis of Missing Plot Technique, Analysis of Covariance, and Balanced Incomplete Block Design for intra-block analysis, to make sound decisions based on statistical data. |
Approach in teaching:
Interactive Lectures, Group Discussion, Classroom Assignment Problem Solving Sessions
Learning activities for the students:
Assignments Seminar Presentation Subject based Activities
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Classroom Quiz Assignments Class Test Individual Presentation
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Cluster sampling and two stage sampling with equal and unequal number of second stage units, Use of Auxiliary Information: Ratio, difference, regression and product methods of estimation and properties.
Double sampling and its uses in ratio estimation. Population proportion to size with replacement and without replacement (PPSWR or PPSWOR) sampling, cumulative total and Lahiri’s method.
Non Sampling errors: observational error, mathematical model, the sample mean, its variance and estimation of the variance. Non-Response error: Hanson and Horvitz, Politz & Simmon Warness technique.
Latin Square Design. Factorial Experiment: 22 23 and 2n .Confounding: Total and partial confounding. Construction of confounded factorial experiments belonging to 2n
Analysis of missing plot technique. Analysis of covariance. Balanced incomplete block design (intra - block analysis).
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