Course overview of AST301
(AST301) Design and Analysis of Experiments II
Instructor
Md Rasel Biswas
MS & BS in Applied Statistics (DU)
Lecturer at DU since July 2022
Email: rbiswas1@isrt.ac.bd
Course introduction
Course Title: Design and Analysis of Experiments II
Credit Hour: 4
This course deals with the concepts and techniques used in the factorial design. The course examines how to design factorial experiments, carry them out, and analyze the data. Experiments with random factors and nested and split designs are also discussed in this course.
Course objectives
Upon completing this course, students will be able to:
- Understand the basic ideas of factorial design.
- Design the experiments involving up to 2 and 3 factors with \(k\) levels.
- Analyze the data from such experiments.
- Interpret the results of his/her analysis.
- Design and analysis the experiments involving random factors.
Lecture plans
Lecture 1- 8: Introduction to factorial designs: basic definition and principles; advantage of factorials; two-factor factorial design; statistical analysis of fixed effects model, model adequacy checking, estimating model parameters, choice of sample size, assumption of no interaction in a two-factor model, one observation per cell; general factorial design; fitting response curve and surfaces; blocking in a factorial design.
Lecture 9- 16: \(2^k\) factorial design: introduction; \(2^2\) design; \(2^3\) design; general \(2^k\) design; a single replicate in \(2^k\) factorial design; blocking in a \(2^k\) factorial design; confounding in \(2^k\) factorial design; confounding in \(2^k\) factorial design in two blocks; confounding in \(2^k\) factorial design in four blocks; confounding in \(2^k\) factorial design in 2p blocks; partial confounding.
Lecture 17- 22: Two-level fractional factorial designs: one-half fraction of \(2^k\) design; one- quarter fraction of \(2^k\) design; general \(2^k\)-p fractional factorial design; resolution III designs; resolution IV and V designs; three-level and mixed-level factorial and fractional factorial designs: \(3^k\) factorial design, confounding in \(3^k\) factorial design, fractional replication of \(3^k\) factorial design, factorials with mixed levels
Lecture 23- 26: Response surface methods: introduction to response surface methodology; method of steepest ascent; analysis of second-order response surface; experimental designs for fitting response surfaces; mixture experiments; robust designs.
Lecture 27- 31: Experiments with random factors: random effects model; two-factor factorial with random factors; two-factor mixed model; sample size determination with random effects; rules for expected mean squares; approximate \(F\) tests; approximate confidence intervals on variance components; modified large-sample method; maximum likelihood estimation of variance components
Lecture 31- 36: Nested and split-plot designs: two-stage nested designs; statistical analysis, diagnostic checking, variance components; general m-staged nested design; designs with both nested and factorial factors; split-plot design; split-plot designs with more than two factors; split-split-plot design, strip-split-plot design.
Textbooks
Preequisite Courses
The prerequisite for this course is “(AST204) Design and Analysis of Experiments I.” Please review Chapters 1 to 4 of the textbook to get started.
Lecture time
Every Monday and Wednesday 8:00 AM – 9:20 AM
Assessment
- Attendance: 5%
- Incourse exams: 25%
- Final exam: 70%