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Analysis of Variance and Design of Experiments
This paper aims at teaching the students about Analysis of Variance and Design of Experiment.
Students will be able to:
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Course |
Learning outcomes (at course level |
Learning and teaching strategies |
Assessment Strategies
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Paper Code |
Paper Title |
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STT-602 |
Analysis of Variance and Design of Experiments
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CO 78: Identify the behaviour of the experimental unit.
CO 79: Develop an experimentation strategy that maximizes learning using a minimum of resources.
CO 80: Apply the basic principles while designing a statistical experiment.
CO 81: Use appropriate experimental design to analyze the experimental data.
CO 82: Take decisions on the output of the design and also identify the outliers.
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Approach in teaching: Interactive Lectures, Group Discussion, Classroom Assignment Problem Solving Sessions
Learning activities for the students: Assignments Seminar Presentation Subject based Activities |
Classroom Quiz Assignments Class Test Individual Presentation |
Linear Model and its different types (only introduction). Concept of ANOVA (i).One-way classified data.(ii)Two-way classification with one observation per cell. Fixed effect models of (i) and (ii) and the assumptions involved. Effects of violation of assumptions made in ANOVA.
Estimation of treatment effects and treatment differences. Expectation of sum of squares, variance of the estimates for both one-way and two-way classified data and critical difference.
Need for design of experiments, Meaning of experiment, experimental unit, treatment, field, block, experimental error, precision, uniformity trials, choice of size and shape of plots and blocks. Fundamental principles of design of experiments- replication, randomization and local control, Efficiency of design.
Basic designs( with one observation per cell and fixed effects model)- Completely Randomized Design, Randomized Block Design - Analysis of these designs, standard error of treatment differences, efficiency of RBD over CRD, their advantages , disadvantages and usages. Missing Plot Techniques, Estimation of single and two missing values in RBD
Design of Experiments – III: Latin Square Design – Its analysis, least square estimates, expectation of sum of squares, efficiency of LSD over CRD and RBD. Estimators of single missing value in LSD. Factorial experiments- 22 and 23 experiments, main effects, interaction effects and their analysis.
- Goon, A.M. Gupta, M.K. and Dasgupta, B. (2001): Fundamentals of Statistics (Volume II), The World Press Pvt Ltd, Kolkata, VII Edition,
- Gupta, S.C. &. Kapoor, V.K. (2000): Fundamentals of Applied Statistics,Sultan Chand & Sons, New Delhi tenth edition.
- Das, M.N. and Giri, N.C. (2002): Design and Analysis of Experiments, New Age International Publisher, Second Edition.
SUGGESTED READINGS:
- Joshi, D.D. (2003): Linear Estimation and Design of Experiments, New Age International Publisher.
- Montgomery D.C.(1952): Design and Analysis of Experiments, Sixth Edition, Wiley Eastern Ltd. Limited
- Cochran, W.G. and Cox, G.M.(1997): Experimental Design, Asia Publishing House, third edition.
e-RESOURCES:
JOURNALS:
- Sankhya The Indian Journal of Statistics, Indian Statistical Institute
- Aligarh Journal of Statistics, Department of Statistics and Operations Research, Aligarh Muslim University
- Afrika Statistika, Saint-Louis Senega University
- International Journal of Statistics and Reliability Engineering, Indian Association for Reliability and Statistic
- Journal of the Indian Society for Probability and Statistics, Indian Society for Probability and Statistics
- Journal of the Indian Statistical Association, Indian Statistical Association
- Statistica, Department of Statistical Sciences Paolo Fortunato, University of Bologna
- Statistics and Applications, Society of Statistics, Computer and Applications
- Stochastic Modeling and Applications, MUK Publications and Distributions
