Syllabus

The primary text for this course is Douglas C. Montgomery, Design and Analysis of Experiments, seventh edition, Wiley India, 2009 [IITJ Library]. The topics to be covered are as follows:

1. Introduction to Design of Experiments: Fundamental Principles of Experimentation
2. Statistical Preliminaries: F-distribution, Theory of random sampling
3. Analysis of Variance (ANOVA)
   • One-Way Analysis of Variance
   • Analysis of the fixed effects model
   • Model adequacy checking
4. Randomized Blocks, Latin Squares, and Related Designs
   • Statistical analysis of the RCBD
   • Model adequacy checking
   • Estimating model parameters and general regression significance test
   • The Latin square design
   • Statistical analysis of balanced incomplete block designs (BIBD)
   • Least squares estimation of the parameters
   • Recovery of interblock information in the BIBD
5. Introduction to Factorial Designs
   • Basic definitions and principles
   • The advantage of factorials
   • Two-factor factorial design
   • The \(2^k\) factorial design
6. Response Surface Methodology
   • Method of steepest ascent
   • Analysis of second-order response surfaces

Textbooks

• Douglas C. Montgomery, Design and Analysis of Experiments, seventh edition, Wiley India, New Delhi, 2009. [IITJ Library]
• Helge Toutenburg and Shalabh, Statistical Analysis of Designed Experiments, third edition, Springer, New York, 2009. [IITJ Library]
• Ranjit K. Roy, Design of Experiments Using the Taguchi Approach, John Wiley & Sons, New Jersey, 2001. [IITJ Library]

Reference Books

• George Casella, Statistical Design, Springer, Berlin, Heidelberg, 2008. [IITJ Library]
• Max D. Morris, Design of Experiments: An Introduction Based on Linear Models, Chapman and Hall/CRC, New York, 2010.

Online Course Material

• J. Maiti, Design and Analysis of Experiments, NPTEL Course Material, Department of Industrial and Systems Engineering, IIT Kharagpur, 2021. [NPTEL]

Lectures

Classes are held in Slot D in lecture hall PH101, Department of Physics [Google Maps]:

• Monday, 11:00–11:50 AM
• Wednesday, 11:00–11:50 AM
• Friday, 10:00–10:50 AM

There is no separate tutorial slot. Please note timetable adjustments as per the Academic Calendar. Doubt clarifications will be scheduled at the mutual convenience of the instructor, teaching assistants, and students.

Lecture updates will be posted on Google Classroom.

Extra classes

• Friday, 20 March 2026 · 3:00–4:50 PM · LHB106, Lecture Hall Complex - I [Google Maps]
• Saturday, 11 April 2026 · 4:00–4:50 PM · LHB304, Lecture Hall Complex - I [Google Maps]

Teaching Assistants

• Darshana, PhD student, Department of Mathematics
• Shivam, PhD student, Department of Mathematics

Evaluation

• Major exam: 40 points
• Minor exam: 25 points
• Quizzes (x2): 10 points each
• Group project: 10 points
• Attendance: 5 points

Attendance

Attendance in lectures is compulsory. If you miss lectures for valid reasons (e.g., medical), please inform the instructor promptly. Students who do not meet the 80% attendance requirement will be awarded an F grade. [Google Sheets]

Assessments

• Quiz 1 · 28 Jan 2026 · 11:00–11:50 AM · PH101 statistics
• Minor exam · 21 Feb 2026 · 8:00–10:00 AM · LHB105, Lecture Hall Complex - I statistics
• Quiz 2 · 6 Apr 2026 · 7:30–8:30 PM · CC Lab 1, S. R. Ranganathan Learning Hub [Google Maps] statistics
• Group project presentations · 20 Apr 2026 · 10:00–10:50 AM · PH101 statistics
• Major exam · 27 Apr 2026 ·9:00 AM–12:00 noon · CSL101 and CSL102, Department of Computer Science and Engineering [Google Maps] statistics

Course materials

• First Course Handout
• Sample problem statements — suggested contexts for the group project
• Self assessment — a formative checklist for exam preparation (ungraded)