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.

Course Information

Title	Design and Analysis of Experiments
Theory Credits	3 (3 hrs/week)
Practical Credits	1 (2 hrs/week)

Course Outcomes

1. Acquaint with the role of statistics in different fields with special reference to agriculture.
2. Apply experimental designs to agricultural fields.
3. Apply randomization to blocks of various fields.
4. Familiarise with the three principles of design of experiments.
5. Deal with experimental data with different factors and levels.
6. Use appropriate experimental designs to analyse experimental data.

Theory — Five Units

Unit 1: Analysis of Variance (ANOVA)

Concept, definition and assumptions. ANOVA one-way classification — mathematical model, analysis with equal and unequal classifications. ANOVA two-way classification — mathematical model, analysis and problems.

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Unit 2: Completely Randomised Design (CRD)

Definition, terminology, three principles of design of experiments; CRD concept, advantages and disadvantages, applications, layout, statistical analysis. Critical Differences when hypothesis is significant.

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Unit 3: Randomised Block Design (RBD)

Concept, advantages and disadvantages, applications, layout, statistical analysis, problems and critical differences. RBD with one missing value and its analysis, problems.

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Unit 4: Latin Square Design (LSD)

Concept of LSD, its advantages and disadvantages, applications, layout, statistical analysis, problems and critical differences.

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Unit 5: Missing Values & Efficiency Comparisons

Estimation of one missing value in LSD and its analysis, problems. Efficiency of RBD relative to CRD. Efficiency of LSD over RBD and CRD and related problems.

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Practical — List of Experiments (8)

1. ANOVA — one-way classification with equal number of observations.
2. ANOVA — one-way classification with unequal number of observations.
3. ANOVA — two-way classification.
4. Analysis of CRD and critical differences.
5. Analysis of RBD and critical differences. Relative efficiency of CRD with RBD.
6. Estimation of single missing observation in RBD and its analysis.
7. Analysis of LSD and efficiency of LSD over CRD and RBD.
8. Estimation of single missing observation in LSD and its analysis.

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Text Books / References

1. S. C. Gupta & V. K. Kapoor — Fundamentals of Applied Statistics, Sultan Chand & Sons.
2. K. V. S. Sarma — Statistics Made Simple: Do it yourself on PC, PHI.
3. M. R. Saluja — Indian Official Statistics, ISI publications.

Suggested Co-Curricular Activities

1. Training of students by related industrial experts.
2. Assignments including technical assignments, if any.
3. Seminars, group discussions, quiz, debates etc. on related topics.
4. Preparation of audio and videos on tools of diagrammatic and graphical representations.
5. Collection of material / figures / photos of related topics.
6. Invited lectures and presentations of stalwarts on those topics.
7. Visits / field trips of firms, research organizations etc.

Exam Blueprint (recommended)

Unit	Topic	Approx. Weightage
1	ANOVA	20 %
2	CRD	20 %
3	RBD	20 %
4	LSD	20 %
5	Missing Values & Efficiency	20 %

Quick Reference

Design	Total df	Treatment df	Block(s) df	Error df
CRD	\(N-1\)	\(k-1\)	—	\(N-k\)
RBD	\(tb-1\)	\(t-1\)	\(b-1\)	\((t-1)(b-1)\)
LSD	\(t^2-1\)	\(t-1\)	\(t-1\) each for rows & cols	\((t-1)(t-2)\)

Item	Formula
CD (CRD, equal n)	\(t_{\alpha/2}\sqrt{2\text{MS}_E/n}\)
CD (RBD)	\(t_{\alpha/2}\sqrt{2\text{MS}_E/b}\)
CD (LSD)	\(t_{\alpha/2}\sqrt{2\text{MS}_E/t}\)
Missing value in RBD	\([tT' + bB' - G']/[(t-1)(b-1)]\)
Missing value in LSD	\([t(R' + C' + T') - 2G']/[(t-1)(t-2)]\)