This paper is designed to develop skills and knowledge for collecting and analyzing data using sample surveys, designing experiments, and understanding the role of official statistics in society and how they are used by policymakers, researchers, and the public.
Course |
Learning outcomes (at course level |
Learning and teaching strategies |
Assessment Strategies |
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Paper Code |
Paper Title |
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CSTT 401 |
Sample Surveys and Design of Experiments
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CO 31: Understand the basic concepts and principles of survey sampling, including population, sampling frame, sampling units, and sampling techniques.
CO 32: Select the appropriate sampling techniques such as stratified and systematic sampling for conducting the real-life sample surveys and for a given research problem.
CO 33: Students will be able to conduct and interpret variance analysis for one-way and two-way classified data.
CO 34: Students will be able to design, analyze, and evaluate experiments using various design principles and techniques.
CO 35: students will be able to critically evaluate the Indian Official Statistical System and its agencies, functions and publications. |
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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Sample Surveys: Basic concepts of sample survey: concept of sampling, need for sampling, complete enumeration v/s sampling, principles of sampling theory, principal steps in a sample surveys, sampling and non-sampling errors.
Simple random sampling (srswr and srswor): definition and procedures of selecting a sample, properties of simple random sample, estimation of mean and sampling variance of sample mean, comparison of srswr and srswor.
Stratified random sampling: introduction, estimation of population mean and its variance, types of allocation: equal, proportional and optimum, comparison of stratified sampling under proportional and neyman allocation with SRSWOR.
Systematic sampling: introduction to linear systematic sampling, estimation of sample mean and its variance (N=nk), comparison of systematic sampling with srswor.
Anova: Definition, Assumptions, Effects of violation of assumptions. Linear model: Fixed, random and mixed effect model. One-way and two-way classified data with one observation per cell only, Variance of the estimates for both one-way and two-way classified data and critical difference.
Design of experiments: Meaning of experiment, experimental unit, treatment, field, block, experimental error, precision, uniformity trials. Fundamental principles of design of experiments- replication, randomization and local control. Completely randomized, Randomized block design. efficiency of RBD over CRD.
Indian Official Statistics: Present Official Statistical System in India relating to census of population. Methods of collection of official statistics, major publications. MOSPI : CSO, NSSO (divisions, their role and functions).
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