Syllabus
Unit 1
Basic Principles for designing statistical experiments: Randomisation, Replication and local control techniques; determination of experimental units and notion of experimental error. Analysis of variance with one–way and two–way classifications; Models and Methods of analysis.
Unit 2
Completely randomized and randomized block designs – Models and estimates of parameters and their standard error – Analysis of data arising from such designs, Analysis when one or two observations are missing.
Unit 3
Latin Square Design – Model – Estimation of parameters – Method of analysis – Missing Plot technique in LSD – Analysis of covariance – One-way classification only
Unit 4
Multiple Comparison tests: LSD , Student-Newman–Keuls test , Duncan’s Multiple range test, Tukey’s test – Transformations to stabilize the variance .
Unit 5
Factorial Experiments: 2 2 , 23 and 32 designs; estimation of main effects and interactions and their standard errors – Principles of confounding
Course Objectives and Outcomes
CO1: Compare the pairs of treatment means using different methods when null hypothesis in rejected in ANOVA.
CO2: Analyze the data using split plot, strip plot and general factorial experiments.
CO3: Construct fractional factorial experiments and apply confounding in real life problems.
CO4: Understand the analysis of BIBD, PBIBD, Quasi-Latin square, and cross over design and their applications in agriculture, business and industries