Course

Unit 1

Simple random sampling, stratified random sampling, systematic random sampling – estimation of the population mean, total and proportion, properties of estimators, various methods of allocation of a sample, comparison of the precisions of estimators under proportional allocation, optimum allocation – Comparison of systematic sampling – Simple random sampling and stratified random sampling for a population with a linear trend.

Unit 2

Cluster sampling bootstrap sampling jack knife sampling bias and variance of estimates – Acceptance sampling for attributes, single sampling, double sampling, measuring performance of the sampling plans- OC, AOQ, ASN, ATI curves.

Unit 3

Planning of experiments, Basic principles of experimental design, uniformity trails, analysis of variance, one-way, two-way and three-way classification models.

Unit 4

Completely randomized design (CRD), randomized block design (RBD) Latin square design (LSD) and Graeco-Latin square designs.

Unit 5

Factorial experiments, 2n and 3n factorial experiments, analysis of 22 , 23 and 32 factorial experiments, Yates procedure, confounding in factorial experiments, fractional factorial design.

Text Books / References

Text Books:

1. Cochran, W.C. Sampling Techniques, Third Edition, Wiley Eastern, (1977).
2. Murthy, M.N., Sampling Theory, Tata McGraw Hill, New Delhi, (1967).
3. Ravichandran, J. Probability and Statistics for engineers, First Reprint Edition, Wiley India, 2012.

References:

1. Philip J. Ross, Taguchi’s Techniques for quality Engineering, MaGraw-Hill, 1989.
2. Schilling E. G. (1982) Acceptance Sampling in Quality Control, Marcel Decker.

This course introduces fundamental and advanced concepts of sampling techniques, including simple random, stratified, systematic, cluster, and resampling methods for efficient estimation. It covers properties of estimators, allocation methods, and acceptance sampling plans with performance measures. The course also focuses on principles of experimental design and analysis of variance for different classification models. Additionally, it provides practical knowledge of standard and factorial experimental designs used in statistical analysis.

Course Outcomes: After successful completion of the course, students will be able to

• CO1. Apply simple random, stratified, and systematic sampling techniques to estimate population mean, total, and proportion, and analyze properties of estimators.
• CO2. Evaluate allocation methods and compare the precision of estimators under proportional and optimum allocation, including populations with linear trends.
• CO3. Apply cluster, bootstrap, and jackknife sampling methods, and assess bias, variance, and performance measures of acceptance sampling plans (OC, AOQ, ASN, ATI).CO4. Demonstrate principles of experimental design and perform analysis of variance (ANOVA) for one-way, two-way, and three-way classification models.
• CO5. Apply standard designs (CRD, RBD, LSD, Graeco-Latin) and factorial experiments, including Yates procedure, confounding, and fractional factorial design for data analysis.

CO-PO Mapping: