EM 3020: Design and Analysis of Experiments

Basic Details:

Course Code: EM 3020
Credits: 3 (Notional hours: 150)
Pre-requisites: EM2020
Compulsory/Optional: Optional

Aim :

To develop the skills necessary to efficiently and effectively design and analyze
experiments

Intended Learning Outcomes:

On successful completion of the course, the students should be able to;

ILO1: Identify the most suitable design for an experiment.
ILO2: Apply statistical methods to design experiments and to analyze the data using statistical software.
ILO3: Develop successful experiments that can lead to reduced development time, enhance process performance, and improve product quality.

Couse Content:

Fully randomized design, Randomized complete block design, Latin square design:

Introduction, design of the experiment, statistical analysis of the fixed effects model,
estimation of the model parameters, pairwise comparisons of the means, residual analysis.

Two factor and Three factor factorial designs:

advantage of factorials, design of the
experiment, testing for interactions, statistical analysis of the fixed effects model with and
without interactions, estimation of the model parameters.

2k factorial designs:

estimating factor effects, formulation of the model, statistical
testing using ANOVA, residual analysis, blocking and confounding, interpretation of
results.

Fractional factorial designs:

One-half fraction and one- quarter fraction of the 2k factorial
design.

Taguchi method:

background and overview of Taguchi method, loss function, insight to
orthogonal arrays, design of experiment, robust design using Taguchi method.

Response surface method:

Introduction, designs for fitting the first- order model and the
second order model.

Time Allocation (Hours):

Lectures

Tutorials

Assignments

Independent Learning & Assessment

Recommended Texts:

● D.C. Montgomery, Design and Analysis of Experiments, 7th edition, (2008), John Wiley and Sons, Inc.
● D.C. Montgomery and G.C. Runger, Applied Statistics and Probability for Engineers, 6th edition, (2013), John Wiley and Sons, Inc.
● Madhav S. Phadke, Quality Engineering using Robust Design,1st edition, (1989), Prentice Hall.

Assessment:

In - course: