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Source document. This page reproduces the syllabus this course was written to, as published — its semesters, credits and paper numbers are that document’s, not this site’s. The course itself is studied on its own, in any order.

This page reproduces the prescribed outline for the theory paper and for Section A of its practical, so that the teaching pages can be checked against it line by line. It is the syllabus, not a summary of it. Nothing about marks, duration or examination pattern appears on this site.

Course Objectives

  1. Analysis of the experimental data using full factorials, with partial and total confounding.
  2. Analysis of the experimental data using one way and two classifications.
  3. To estimate the parameters of population and estimating variances.

Course Outcomes

  1. Able to carry out the analysis for the data by identifying appropriate Complete and Incomplete block designs and factorial and fractional factorial designs.
  2. Able to apply the analysis of covariance for the data set.
  3. Able to carry out the analysis for the data using response surface methodology.

Stated Pre-requisite

ASSUMED BEFORE THIS PAPER

Concept of analysis of Variance and ANOVA for one-way and two-way classifications with one observation per cell, expectation of various sums of squares, Statistical analysis, Analysis of Completely randomized, Randomized Block and Latin Square Designs including estimation of missing observations and efficiencies.

Covered by Design and Analysis of Experiments, Units 1 to 5.

Unit I

AS PRESCRIBED

Analysis of variance for m-observations, nij-observations per cell. Multiple Comparison tests: Fishers Least Significance Difference (LSD) and Duncan's Multiple Range (DMR) tests. Analysis of Covariance for One-way and Two-way classifications.

→ Unit 1 notes

Unit II

AS PRESCRIBED

Factorial experiments: Estimation of Main effects, interaction effects and analysis of 2k factorial experiments in general and with particular reference to k = 2, 3 and 4 and 32 factorial experiment.

→ Unit 2 notes

Unit III

AS PRESCRIBED

Total and Partial Confounding in case of 23, 24 and 32 factorial designs. Concept of balanced partial confounding. Fractional replications of factorial designs: One half replication of 23 and 24 factorial designs, one-quarter replications of 25 and 26 factorial designs. Resolution of a design. Split–Plot design. Balanced incomplete block design (BIBD) – parametric relations, intra-block analysis, recovery of inter-block information. Construction of BIBD's through MOLS.

→ Unit 3 notes

Unit IV

AS PRESCRIBED

Partially balanced incomplete block design with two associate classes PBIBD (2) – Parametric relations, intra block analysis. Simple lattice design and Youden-square design. Concept of Response surface methodology (RSM), Response surface designs. Design for fitting first-order and second-order models. Variance of estimated response. Second order rotatable designs (SORD), Central composite designs (CCD), Rotatability of CCD.

→ Unit 4 notes

Practical Paper STS-206, Section A — List of Practicals

The practical paper is Designs and Analysis of Experiments and Sampling Theory (Conventional). Section A belongs to this paper; Section B belongs to Sampling Theory (STS-204) and is written with that paper.

  1. Analysis of Variance for two-way classification m-observations per cell.
  2. Analysis of Variance for two-way classification nij-observations per cell.
  3. Analysis of Covariance for one-way classification.
  4. Analysis of Covariance for two-way classification.
  5. Analysis of Variance for 23, 24 factorial experiments.
  6. Analysis of Variance for 32 factorial experiments.
  7. Identification of Confounded terms in 23, 24 and 32 factorial experiments.
  8. Construction of design with a specified effect is confounded.
  9. Analysis of Variance for Total confounding of 23, 24 designs.
  10. Analysis of Variance for Partial confounding of 23, 24 designs.
  11. Analysis of Variance for one-half fraction of 24 designs.
  12. Analysis of Variance for one-quarter fraction of 25 designs.
  13. Analysis of variance for Split-Plot design.
  14. Analysis of Balanced Incomplete Block Design.
  15. Analysis of Youden Square Design.
  16. Analysis of Partially Balanced Incomplete Block Design.

→ Practical notes, Section A

References

  1. Montgomery, D. C. (2019): Design and Analysis of Experiments, John Wiley.
  2. Das, M. N. and Giri, N. C. (1979): Design and Analysis of Experiments.
  3. Kempthorne, O. (2008): The Design and Analysis of Experiments, 2nd edition.
  4. Cochran, W. G. and Cox, G. M. (1957): Experimental Designs, 2nd edition.

Author spellings above follow the published books: the printed list gives “Montogomery” for Montgomery and “Kempthrone” for Kempthorne.

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