design of experiments syllabus

STA 643 Modern Design of Experiments (S16)

Course goals & objectives:.

This course introduces students to concepts and techniques of Classical and Bayesian design - experimental units, randomization, treatments, blocking and restrictions to randomization, and utility of designs. We will cover optimal sample size determination for estimation and testing. Topics include simple A-B testing, factorial and fractional factorial designs, response surface methods, conjoint designs, sequential designs, bandit problems used in on-line advertising, design and modeling of complex computer experiments, and designs for multiple objectives. Computational algorithms for finding optimal designs will be covered in the context of various problems.

It is expected that students have either taken STA 601, are co-registed, or are familiar with some basics of Bayesian analysis or are very quick studies!

All students should be comfortable with linear algebra and mathematical statistics at the level of STA 532/611.

The course goals are as follows:

• Understand the different philosophical approaches to experimental design (Bayesian and frequentists)
• Build a solid foundation for the statistical theory for experimental design.
• Build appropriate statistical models for designed experiments, perform data analysis using appropriate software, and communicate results without use of statistical jargon.
• Construct appropriate experimental designs for given problems: sample size determination, choice of levels of variables, designs with restrictions on randomization, utility functions for measuring design objectives, use of simulation to characterize properties of designs.

Course topics will be drawn (but subject to change) from

• The Art of Experimentation
• Basics: Designs with One Source of Variation
• Factorial Experiments
• Restrictions on Randomization: Blocking
• Fractional Factorial Designs & Confounding
• Optimal Designs and Model Uncertainty
• Designs with Random Effects: Split Plots, Crossover Designs
• Conjoint Designs
• Response Surfaces & Optimization
• Design and Analysis of Computer Experiments
• Design for Nonlinear Models
• Sequential Designs

Students will choose an advanced topic and design an appropriate experiment for a final project.

Books and Resources

We will use Design & Analysis of Experiments byt Angela, Dean and Daniel Voss as a reference, with other materials provided to supplement as needed. This book is available through the Duke Library as an ebook

Homework	30%
Midterm	30%
Final Project	30%
Participation	10%

Grades may be curved at the end of the semester. Cumulative numerical averages of 90 - 100 are guaranteed at least an A-, 80 - 89 at least a B-, and 70 - 79 at least a C-, however the exact ranges for letter grades will be determined after the final exam. The more evidence there is that the class has mastered the material, the more generous the curve will be.

These will be assigned at each class or weekly on the course webpage.

The objective of the problem sets is to help you develop a more in-depth understanding of the material and help you prepare for exams and projects. Grading will be based on completeness as well as accuracy. In order to receive credit you must show all your work.

You are welcomed, and encouraged, to work with each other on the problems, but you must turn in your own work. If you copy someone else’s work, both parties will receive a 0 for the problem set grade as well as being reported to the Office of Student Conduct . Work submitted on Sakai will be checked for instances of plagiarism prior to being graded.

Submission instructions: You will submit your HW on Sakai by uploading a PDF. If the TAs cannot view your work, or read your handwriting, you will lose points accordingly.

All assignments will be time stamped and late work will be penalized based on this time stamp (see late work policy below).

Attendance & Participation:

You are expected to be present at class meeting and actively participate in the discussion. Your attendance and participation during class, as well as your activity on the discussion forum on Sakai will make up 10% of your grade in this class. While I might sometimes call on you during the class discussion, it is your responsibility to be an active participant without being called on.

Final Project

The objective of the Project is to give you independent applied research experience using real data and statistical methods. You will use all (relevant) techniques learned in this class or explore additional advanced material to design an experiment, explore its properties (either analytically or through simulation) and if possible carry it out.

Further details will be provided as due dates approach.

There will be one midterm in this class. See course info for dates and times of the exams. You are allowed to use one sheet of notes (``cheat sheet”) on the midterm exam. This sheet must be no larger than 8 1/2 x 11, and must be prepared by you . You may use both sides of the sheet and can write as small as you wish.

Email & Forum (Piazza):

I will regularly send announcements by email, please make sure to check your email daily.

Any non-personal questions related to the material covered in class, problem sets, labs, projects, etc. should be posted on Piazza forum . Before posting a new question please make sure to check if your question has already been answered. The TAs and myself will be answering questions on the forum daily and all students are expected to answer questions as well. Please use informative titles for your posts.

Note that it is more efficient to answer most statistical questions ``in person” so make use of OH.

Students with disabilities:

Students with disabilities who believe they may need accommodations in this class are encouraged to contact the Student Disability Access Office at (919) 668-1267 as soon as possible to better ensure that such accommodations can be made.

Academic integrity:

Duke University is a community dedicated to scholarship, leadership, and service and to the principles of honesty, fairness, respect, and accountability. Citizens of this community commit to reflect upon and uphold these principles in all academic and non-academic endeavors, and to protect and promote a culture of integrity. Cheating on exams and quizzes, plagiarism on homework assignments and projects, lying about an illness or absence and other forms of academic dishonesty are a breach of trust with classmates and faculty, violate the Duke Community Standard , and will not be tolerated. Such incidences will result in a 0 grade for all parties involved as well as being reported to the Office of Student Conduct . Additionally, there may be penalties to your final class grade. Please review the Duke’s Academic Dishonesty policies .

• next day: lose 30% of points
• later than next day: lose all points

Regrade requests must be made within 3 days of when the assignment is returned, and must be submitted in writing. These will be honored if points were tallied incorrectly, or if you feel your answer is correct but it was marked wrong. No regrade will be made to alter the number of points deducted for a mistake. There will be no grade changes after the final exam.

• Use of disallowed materials (textbook, class notes, web references, any form of communication with classmates or other persons, etc.) during exams will not be tolerated. This will result in a 0 on the exam for all students involved, possible failure of the course, and will be reported to the Office of Student Conduct . If you have any questions about whether something is or is not allowed, ask me beforehand.

Design of Experiments

Credit hours:, learning objective:, description:.

A thorough and practical course in design and analysis of experiments for experimental workers and applied statisticians. SAS statistical software is used for analysis. Taken by graduate students from many fields. F2018 STAT514 Syllabus

Topics Covered:

Prerequisites:, applied / theory:, web address:, web content:, computer requirements:, other requirements:, proed minimum requirements:.

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Keyboard Shortcuts

Stat 503: design of experiments.

•   Overview
•   Materials
•   Assessment Plan
•   Prerequisites
•   Online Notes

The course will cover most of the material in the text, Chapters 1-15. The students will be required to use statistical computer software to complete many homework assignments and the project.

Course Topics

This graduate level course covers the following topics:

• Understanding basic design principles
• Working in simple comparative experimental contexts
• Working with single factors or one-way ANOVA in completely randomized experimental design contexts
• Implementing randomized blocks, Latin square designs and extensions of these
• Understanding factorial design contexts
• Working with two level, 2 k , designs
• Implementing confounding and blocking in 2 k  designs
• Working with 2-level fractional factorial designs
• Working with 3-level and mixed-level factorials and fractional factorial designs
• Simple linear regression models
• Understanding and implementing response surface methodologies
• Understanding robust parameter designs
• Working with random and mixed effects models
• Understanding and implementing nested and split-plot and strip-plot designs
• Using repeated measures designs, unbalanced AOV and ANCOVA
•   Designing Experiments

Course Author(s)

Dr. James L Rosenberger is the primary author of these course materials and has taught this course for many semesters in residence and online.

  This course uses Honorlock for proctored exams. For more information view O.3 What is a proctored exam? in the student orientation.

For most assignments the Minitab GLM or SAS Proc GLM and Proc Mixed commands will satisfy the computing requirements. Minitab Design Of Experiments (DOE) commands are also utilized extensively.

Students should already feel comfortable using SAS at a basic level, be a quick learner of software packages, or able to figure out how to do the required analyses in another package of their choice. Students who have no experience with programming or are anxious about being able to manipulate software code are strongly encouraged to take the one-credit courses in SAS in order to establish this foundation before taking courses that rely on this software.

SAS will be supported and sample programs will be supplied but you will be required to do some programing on your own. Due to different software applications, software versions and platforms there may be issues with running code. Students must be proactive in seeking advice and help from appropriate sources including documentation resources, other students, the teaching assistant, instructor or helpdesk.

Montgomery, D. C. (2020).  Design and Analysis of Experiments , 10th Edition, John Wiley & Sons. ISBN-13: 978-1119722106

Last updated: FA23

Assessment Plan

• 10 Homework assignments graded. 40% (10% penalty for late assignments)
• Experiment design and analysis project.10% (due last week of class)
• Two preliminary examinations. 15% each.
• Comprehensive final examination (proctored). 20%

PLEASE NOTE: This course may require you to take exams using certain proctoring software that uses your computer’s webcam or other technology to monitor and/or record your activity during exams. The proctoring software may be listening to you, monitoring your computer screen, viewing you and your surroundings, recording and storing any and all activity (including visual and audio recordings) during the proctoring process. By enrolling in this course, you consent to the use of the proctoring software selected by your instructor, including but not limited to any audio and/or visual monitoring which may be recorded.  Please contact your instructor with any questions . ( Read more... )

Prerequisites

STAT 501 (or STAT 462 ) and STAT 502

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Design of Experiments

Arizona State University via Coursera Specialization Help

Experimental Design Basics

12 hours 56 minutes

Random Models, Nested and Split-plot Designs

9 hours 18 minutes

Factorial and Fractional Factorial Designs

11 hours 51 minutes

Response Surfaces, Mixtures, and Model Building

13 hours 8 minutes

Douglas C. Montgomery

• united states

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Design and Analysis of Experiments

Download the Course Schedule

Explore innovative strategies for constructing and executing experiments—including factorial and fractional factorial designs—that can be applied across the physical, chemical, biological, medical, social, psychological, economic, engineering, and industrial sciences. Over the course of five days, you’ll enhance your ability to conduct cost-effective, efficient experiments, and analyze the data that they yield in order to derive maximal value for your organization.

Course Overview

THIS COURSE MAY BE TAKEN INDIVIDUALLY OR As part of THE  PROFESSIONAL CERTIFICATE PROGRAM IN BIOTECHNOLOGY & LIFE SCIENCES .

This program is planned for those interested in the design, conduct, and analysis of experiments in the physical, chemical, biological, medical, social, psychological, economic, engineering, or industrial sciences. The course will examine how to design experiments, carry them out, and analyze the data they yield. Various designs are discussed and their respective differences, advantages, and disadvantages are noted. In particular, factorial and fractional factorial designs are discussed in greater detail. These are designs in which two or more factors are varied simultaneously; the experimenter wishes to study not only the effect of each factor, but also how the effect of one factor changes as the levels of other factors change. The latter is generally referred to as an interaction effect among factors.

The fractional factorial design has been chosen for extra-detailed study in view of its considerable record of success over the last 30 years. It has been found to allow cost reduction, increase efficiency of experimentation, and often reveal the essential nature of a process. In addition, it is readily understood by those who are conducting the experiments, as well as those to whom the results are reported.

The program will be elementary in terms of mathematics. The course includes a review of the modest probability and statistics background necessary for conducting and analyzing scientific experimentation. With this background, we first discuss the logic of hypothesis testing and, in particular, the statistical techniques generally referred to as Analysis of Variance. A variety of software packages are illustrated, including Excel, SPSS, JMP, and other more specialized packages.

Throughout the program we emphasize applications, using real examples from the areas mentioned above, including such relatively new areas as experimentation in the social and economic sciences.

We discuss Taguchi methods and compare and contrast them with more traditional techniques. These methods, originating in Japan, have engendered significant interest in the United States.

All participants receive a copy of the text, Experimental Design: with applications in management, engineering and the sciences , Duxbury Press, 2002, co-authored by Paul D. Berger and Robert E. Maurer, in addition to extensive PowerPoint notes.

Participant Takeaways

• Describe how to design experiments, carry them out, and analyze the data they yield.
• Understand the process of designing an experiment including factorial and fractional factorial designs.
• Examine how a factorial design allows cost reduction, increases efficiency of experimentation, and reveals the essential nature of a process; and discuss its advantages to those who conduct the experiments as well as those to whom the results are reported.
• Investigate the logic of hypothesis testing, including analysis of variance and the detailed analysis of experimental data.
• Formulate understanding of the subject using real examples, including experimentation in the social and economic sciences.
• Introduce Taguchi methods, and compare and contrast them with more traditional techniques.
• Learn the technique of regression analysis, and how it compares and contrasts with other techniques studied in the course.
• Understand the role of response surface methodology and its basic underpinnings.
• Gain an understanding of how the analysis of experimental design data is carried out using the most common software packages.
• Be able to apply what you have learned immediately upon return to your company.

Who Should Attend

This course is appropriate for anyone interested in designing, conducting, and analyzing experiments in the biological, chemical, economic, engineering, industrial, medical, physical, psychological, or social sciences. Applicants need only have interest in experimentation. No previous training in probability and statistics is required, but any experience in these areas will be useful.

Program Outline

Class runs 9:00 am - 5:00 pm every day.

• Introduction to Experimental Design
• Hypothesis Testing
• ANOVA I, Assumptions, Software
• Multiple Comparison Testing
• ANOVA II, Interaction Effects
• Latin Squares and Graeco-Latin Squares
• 2K Designs (continued)
• Confounding/Blocking Designs
• Confounding/Blocking Designs (continued)
• 2k-p Fractional-Factorial Designs
• 2k-p Fractional-Factorial Designs (continued)
• Taguchi Designs
• Taguchi Designs (continued)
• Orthogonality and Orthogonal contrasts
• 3K Factorial Designs
• Regression Analysis I
• Regression Analysis II
• Regression Analysis III & Introduction to Response Surface Modeling
• Response Surface Modeling (continued), Literature Review, Course Summary

AMONG THE SUBJECTS TO BE DISCUSSED ARE:

• The logic of complete two-level factorial designs
• Detailed discussion of interaction among studied factors
• Large versus small experiments
• Simultaneous study of several factors versus study of one factor at a time
• Fractional experimental designs; construction and examples
• The application of hypothesis testing to analyzing experiments
• The important role of orthogonality in modern experimental design
• Single degree-of-freedom analysis; pinpointing sources of variability
• The trade-off between interaction and replication
• Response surface experimentation
• Yates' forward algorithm
• The reliability of estimates in factorial designs
• The usage of software in design and analysis of experiments
• Latin and Graeco-Latin squares as fractional designs; examples
• Designs with all studied factors at three levels
• The role of fractional designs in response surface experimentation
• Taguchi designs
• Incomplete study of many factors versus intensive study of a few factors
• Multivariate linear regression models
• The book and journal literature on experimental design

Testimonials

The type of content you will learn in this course, whether it's a foundational understanding of the subject, the hottest trends and developments in the field, or suggested practical applications for industry.

How the course is taught, from traditional classroom lectures and riveting discussions to group projects to engaging and interactive simulations and exercises with your peers.

What level of expertise and familiarity the material in this course assumes you have. The greater the amount of introductory material taught in the course, the less you will need to be familiar with when you attend.

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Design and Analysis of Experiments - 625.662

Statistically designed experiments are plans for the efficient allocation of resources to maximize the amount of empirical information supporting objective decisions. Although other statistical approaches, including visualization and regression, can lead to uncovering relationships among variables, experimental design is unique in supporting the claim that the nature of the relationships can be regarded as cause and effect. Inference is achieved using a general linear model based on data collection adhering to a broad framework, wherein one or more independent variables (treatments) are intentionally and simultaneously manipulated, experimental units are randomly assigned to a level of treatment, and a response is observed. This approach in experimental research appears in virtually every field of study where the strong case for establishing cause and effect relationships is required, including, for example, randomized control trials in the health sciences or process optimization in engineering. In this course we will consider building block concepts including crossed and nested factors, fixed and random effects, aliasing and confounding, and then apply these building blocks to common experimental designs (e.g., completely randomized, randomized block, Latin squares, factorial, fractional factorial, hierarchical/nested, response surface, and repeated measure designs.) Analysis techniques will include fixed effect, random effect, and mixed effects analysis of variance. Power and sample size calculation methods will be covered and design optimality will be discussed. Applications will come from the physical sciences, engineering and the health sciences. The software packages R and JMP will be used for analysis.

Course Prerequisite(s)

Multivariate calculus, linear algebra, and one semester of graduate probability and statistics (e.g., EN.625.603 Statistical Methods and Data Analysis). Some computer-based homework assignments will be given.

Course Offerings

There are no sections currently offered, however you can view a sample syllabus from a prior section of this course.

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Design of Experiments

Module overview.

When planning experiments, it is essential that the data collected are as relevant and informative as possible. The statistical principles for the design of experiments include the choice of optimal or good treatments sets and appropriate replication of them, randomization to ensure unbiasedness and the use of blocking and other methods for reduction of variance

Linked modules

Pre-requisites: MATH6174 or STAT6123 or (MATH2011 and MATH2010)

Aims and Objectives

Learning outcomes.

Having successfully completed this module you will be able to:

• understand the potential practical problems in its implementation
• construct optimal or good designs for a range of practical experiments
• appreciate the advantages and disadvantages of a design for a particular experiment
• describe how the analysis of the data from the experiment should be carried out

Emphasis throughout will be on the statistical principles underlying the methods and how they can be applied to and adapted for practical experiments. The following methods will be discussed and practised.

1) Basic ideas: objectives leading to choice of treatments; randomization to ensure validity of analysis; blocking to separate sources of variation in order to ensure efficiency of analysis, ANOVA methodology.

2) Choice of treatments: replication for unstructured treatments; optimal design for quantitative treatments; the factorial treatment structure and its advantages; incomplete factorial structures,

including regular fractional factorials.

3) Randomization: practical constraints on randomization.

4) Blocking: incomplete block designs for unstructured treatments, including balanced incomplete block designs; confounding for factorial designs.

Learning and Teaching

Teaching and learning methods.

Lectures, problem classes and self-directed computer work

Study time

Type	Hours
Problem Classes	12
Independent Study	102
Lecture	36
Total study time	150

General Resources

Website on Blackboard.

Mead, R, Gilmour, SG, and Mead, A (2012). Statistical Principles for the Design of Experiments . Cambridge.

Box, G.E.P., Hunter, J.S. and Hunter, W.G. (2005). Statistics for Experimenters . New York: Wiley.

Montgomery, D.C. (2009). Design and Analysis of Experiments . New York: Wiley.

Atkinson, A.C., Donev, A.N. and Tobias, R.D. (2007). Optimum Experimental Designs, with SAS . Oxford: Oxford Science Publication.

Wu, C.F.J. and Hamada, M. (2009). Experiments - Planning, Analysis and Parameter . New York: Wiley.

John, J.A. and Williams, E.R. (1995). Cyclic and computer generated designs . London: Chapman and Hall.

Dean, A.M. and Voss, D.T. (1999). Design and Analysis of Experiments . New York: Springer-Verlag.

Max D. Morris (2011). Design of Experiments: An Introduction Based on Linear Models . CRC Press.

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

Exercises and Quizzes

• Assessment Type: Formative
• Final Assessment:
• Group Work: No

This is how we’ll formally assess what you have learned in this module.

Breakdown

Method	Percentage contribution
Written assessment	50%
Coursework	50%

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown

Method	Percentage contribution
Written assessment	100%

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown

Method	Percentage contribution
Coursework	50%
Written assessment	50%

Repeat Information

Repeat type: Internal & External

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Introduction to Design of Experiments

This course will teach you the application of DOE rather than statistical theory, and teaches full and fractional factorial designs, Plackett-Burman, Box-Behnken, Box-Wilson and Taguchi designs.

This course will teach you how to use experiments to gain maximum knowledge at minimum cost. For processes of any kind that have measurable inputs and outputs, Design of Experiments (DOE) methods guide you in the optimum selection of inputs for experiments, and in the analysis of results. Full factorial as well as fractional factorial designs are covered.

• Introductory, Intermediate
• Expert Instructor
• Tuiton-Back Guarantee
• 100% Online

Learning Outcomes

At the conclusion of this course you will be familiar with the foundations of experimental design.  You will learn about interactions, coding and randomization, how to choose appropriate designs, and how to conduct experiments and analyze your results.

• Explain the key concepts of DOE, and why it is used
• Calculate treatment effects
• Produce plots from the results of experiments
• Specify fractional and full factorial designs
• Specify specialized designs, e.g. Taguchi, Box-Wilson, others
• Use Excel-based software to design experiments and analyze data

Who Should Take This Course

All six-sigma practitioners, scientists, engineers, and technicians who are interested in performing experiments that maximize process knowledge with a minimum amount of resources. Managers who are responsible for delivering products “on time” and “on budget” will also benefit from this course by learning what their employees should be doing. This course will stress the application of DOE rather than statistical theory. While design of experiments has been very successfully applied in research and development, that is not the only application. The techniques presented also apply to manufacturing, quality control, and even marketing.

Our Instructors

Dr. jim rutledge, course syllabus.

Foundations of DOE

• What is experimental design
• Why use DOE
• Measure of quality (Cp Cpk, dpm)
• Interactions
• Confounding/aliasing
• Randomization

Simple Designs and Their Analysis

• DOE 12-step checklist example
• Calculating effects
• Interaction plots
• Marginal means plot of effects
• Pareto chart of effects
• Prediction equations
• Using Excel based DOE KISS software

Design Types

• Fractional factorial designs
• Design resolution
• Aliasing pattern
• Plackett-Burman designs
• Box-Behnken designs
• Box-Wilson (central composite) designs
• Taguchi designs

Practice Conducting and Analyzing Experimental Data

• Multiple regression
• Normal probability plot
• Importance of analyzing interactions
• Taguchi’s signal to noise ratios
• Variance reduction analysis
• Practice planning, executing, and analyzing an experiment

Class Dates

Prerequisites.

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I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.

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Frequently asked questions, what is your satisfaction guarantee and how does it work.

We offer a “Student Satisfaction Guarantee​” that includes a tuition-back guarantee, so go ahead and take our courses risk free. That’s our commitment to student satisfaction. Students may cancel, transfer, or withdraw from a course under certain conditions. If you’re not satisfied with a course, you may withdraw from the course and receive a tuition refund.

Please see our  knowledge center for more information.

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• Students are entitled to a full refund if a course they are registered for is canceled.
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Who are the instructors at Statistics.com?

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• Authors of well-regarded texts in their area;
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• Senior faculty; and
• Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at Statistics.com.

Please see our  knowledge center  for more information.

Visit our knowledge base and learn more.

Register For This Course

Related courses, additional information, time requirements.

Homework in this course consists of short answer questions to test concepts, guided data analysis problems using software, and guided data modeling problems using software.

In addition to assigned readings, this course also has discussion tasks, and an end of course data modeling project.

Course Text

Understanding Industrial Designed Experiments by Schmidt et al is available as an e-book, or hard cover from Amazon .

The course makes use of Quantum XL, an add-in to Microsoft Excel.  A 30-day trial version of the add-in can be downloaded from  www.sigmazone.com .  The add-in should function with Excel 2002 and above, note however, the course notes are written with examples from Excel 2010.

Note:  Do not start your trial prematurely – you’ll need it throughout the 4-week course.

Supplemental Information

Literacy, accessibility, and dyslexia.

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• NOC:Design and Analysis of Experiments (Video) 
• Co-ordinated by : IIT Kharagpur
• Available from : 2017-12-21
• Intro Video
• Lecture 30: Introduction to Factorial Experiments
• Lecture 31 : Statistical Analysis of Factorial Experiments
• Lecture 32 : Estimation of parameters and model adequacy test for factorial experiement
• Lecture 33 : Full_Factorial_Single_Replicate
• Lecture 34 : General_Full_factorial_design
• Lecture 35 : Blocking_Factorial_design
• Lecture 36 : Two_level_Factorial_Experiment
• Lecture 37 : Statistical analysis of 2^k factorial design
• Lecture 38 : 2_k_Factorial_Design_Single_Replicate
• Lecture 39 : 2_k_Factorial_Design_Centre_Points
• Lecture 40 : 2_k_Factorial_Design_Optimality_Issues
• Lecture 41: 2_k_ Factorial Design - Issues with Coded Design Variables "
• Lecture 42: Blocking and Confounding in 2_k_Factorial Design
• Lecture 43: Blocking and Confounding in 2_k_Factorial Design (Contd.)
• Lecture 44: Blocking and Confounding in 2_k_Factorial Design (Contd.)
• Lecture 45 : Fractional factorial design: Introduction
• Lecture 46 : Fractional factorial design: Contd.
• Lecture 47 : Fractional factorial design: One quarter fraction of the 2k design
• Lecture 48 : "Alias Structure in Fractional factorial design: Regression Approach "
• Lecture 49 : "General 2^(k-p) Fractional Factorial Design "
• Lecture 50 : "Fractional factorial design: Fold-over Design "
• Lecture 51 : Plackett-Burman Designs
• Lecture 52:Response Surface Methodology (RSM) - First Order Model
• Lecture 53:Response Surface Methodology (RSM) - First Order Model (Contd.)
• Lecture 54: Experimental Design for Fitting Response Surfaces
• Lecture 55: Response Surface Methodology (RSM): Fitting Second Order Model
• Lecture 56: Analysis of Second Order Response Surface
• Lecture 57 : ANOVA using MINITAB
• Lecture 58 : Factorial Design using MINITAB
• Lecture 59 : Fractional Factorial Design using MINITAB
• Lecture 60 : Response Surface Methodology using MINITAB
• Live Session 03-03-2021
• Watch on YouTube
• Assignments
• Download Videos
• Transcripts

Module Name	Download
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W12A1
W1A1
W2A1
W3A1
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Sl.No	Chapter Name	MP4 Download
1	Lecture 1
2	Lecture 2
3	Lecture 3
4	Lecture 4
5	Lecture 5
6	Lecture 6
7	Lecture 7
8	Lecture 8
9	Lecture 9
10	Lecture 10
11	Lecture 11
12	Lecture 12
13	Lecture 13
14	Lecture 14
15	Lecture 15
16	Lecture 16
17	Lecture 17
18	Lecture 18
19	Lecture 19
20	Lecture 20
21	Lecture 21
22	Lecture 22
23	Lecture 23
24	Lecture 24
25	Lecture 25
26	Lecture 26
27	Lecture 27
28	Lecture 28
29	Lecture 29
30	Lecture 30: Introduction to Factorial Experiments
31	Lecture 31 : Statistical Analysis of Factorial Experiments
32	Lecture 32 : Estimation of parameters and model adequacy test for factorial experiement
33	Lecture 33 : Full_Factorial_Single_Replicate
34	Lecture 34 : General_Full_factorial_design
35	Lecture 35 : Blocking_Factorial_design
36	Lecture 36 : Two_level_Factorial_Experiment
37	Lecture 37 : Statistical analysis of 2^k factorial design
38	Lecture 38 : 2_k_Factorial_Design_Single_Replicate
39	Lecture 39 : 2_k_Factorial_Design_Centre_Points
40	Lecture 40 : 2_k_Factorial_Design_Optimality_Issues
41	Lecture 41: 2_k_ Factorial Design - Issues with Coded Design Variables "
42	Lecture 42: Blocking and Confounding in 2_k_Factorial Design
43	Lecture 43: Blocking and Confounding in 2_k_Factorial Design (Contd.)
44	Lecture 44: Blocking and Confounding in 2_k_Factorial Design (Contd.)
45	Lecture 45 : Fractional factorial design: Introduction
46	Lecture 46 : Fractional factorial design: Contd.
47	Lecture 47 : Fractional factorial design: One quarter fraction of the 2k design
48	Lecture 48 : "Alias Structure in Fractional factorial design: Regression Approach "
49	Lecture 49 : "General 2^(k-p) Fractional Factorial Design "
50	Lecture 50 : "Fractional factorial design: Fold-over Design "
51	Lecture 51 : Plackett-Burman Designs
52	Lecture 52:Response Surface Methodology (RSM) - First Order Model
53	Lecture 53:Response Surface Methodology (RSM) - First Order Model (Contd.)
54	Lecture 54: Experimental Design for Fitting Response Surfaces
55	Lecture 55: Response Surface Methodology (RSM): Fitting Second Order Model
56	Lecture 56: Analysis of Second Order Response Surface
57	Lecture 57 : ANOVA using MINITAB
58	Lecture 58 : Factorial Design using MINITAB
59	Lecture 59 : Fractional Factorial Design using MINITAB
60	Lecture 60 : Response Surface Methodology using MINITAB

Sl.No	Chapter Name	English
1	Lecture 1	PDF unavailable
2	Lecture 2	PDF unavailable
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25	Lecture 25
26	Lecture 26	PDF unavailable
27	Lecture 27	PDF unavailable
28	Lecture 28	PDF unavailable
29	Lecture 29	PDF unavailable
30	Lecture 30: Introduction to Factorial Experiments	PDF unavailable
31	Lecture 31 : Statistical Analysis of Factorial Experiments	PDF unavailable
32	Lecture 32 : Estimation of parameters and model adequacy test for factorial experiement	PDF unavailable
33	Lecture 33 : Full_Factorial_Single_Replicate	PDF unavailable
34	Lecture 34 : General_Full_factorial_design	PDF unavailable
35	Lecture 35 : Blocking_Factorial_design	PDF unavailable
36	Lecture 36 : Two_level_Factorial_Experiment	PDF unavailable
37	Lecture 37 : Statistical analysis of 2^k factorial design	PDF unavailable
38	Lecture 38 : 2_k_Factorial_Design_Single_Replicate	PDF unavailable
39	Lecture 39 : 2_k_Factorial_Design_Centre_Points	PDF unavailable
40	Lecture 40 : 2_k_Factorial_Design_Optimality_Issues	PDF unavailable
41	Lecture 41: 2_k_ Factorial Design - Issues with Coded Design Variables "	PDF unavailable
42	Lecture 42: Blocking and Confounding in 2_k_Factorial Design	PDF unavailable
43	Lecture 43: Blocking and Confounding in 2_k_Factorial Design (Contd.)	PDF unavailable
44	Lecture 44: Blocking and Confounding in 2_k_Factorial Design (Contd.)	PDF unavailable
45	Lecture 45 : Fractional factorial design: Introduction	PDF unavailable
46	Lecture 46 : Fractional factorial design: Contd.	PDF unavailable
47	Lecture 47 : Fractional factorial design: One quarter fraction of the 2k design	PDF unavailable
48	Lecture 48 : "Alias Structure in Fractional factorial design: Regression Approach "	PDF unavailable
49	Lecture 49 : "General 2^(k-p) Fractional Factorial Design "	PDF unavailable
50	Lecture 50 : "Fractional factorial design: Fold-over Design "	PDF unavailable
51	Lecture 51 : Plackett-Burman Designs	PDF unavailable
52	Lecture 52:Response Surface Methodology (RSM) - First Order Model	PDF unavailable
53	Lecture 53:Response Surface Methodology (RSM) - First Order Model (Contd.)	PDF unavailable
54	Lecture 54: Experimental Design for Fitting Response Surfaces	PDF unavailable
55	Lecture 55: Response Surface Methodology (RSM): Fitting Second Order Model	PDF unavailable
56	Lecture 56: Analysis of Second Order Response Surface	PDF unavailable
57	Lecture 57 : ANOVA using MINITAB	PDF unavailable
58	Lecture 58 : Factorial Design using MINITAB	PDF unavailable
59	Lecture 59 : Fractional Factorial Design using MINITAB	PDF unavailable
60	Lecture 60 : Response Surface Methodology using MINITAB	PDF unavailable

Sl.No	Language	Book link
1	English	Not Available
2	Bengali	Not Available
3	Gujarati	Not Available
4	Hindi	Not Available
5	Kannada	Not Available
6	Malayalam	Not Available
7	Marathi	Not Available
8	Tamil	Not Available
9	Telugu	Not Available

Design and Analysis of Experiments

• All Engineering
• Management Students
• Manufacturing companies like GM, Tata Motors, Tata Steel
• Process industries such as ONGC
• General Electric
• R&D organizations

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Note: This exam date is subjected to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout, books and references, instructor bio.

Prof. Jhareswar Maiti

Course certificate.

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SWAYAM SUPPORT

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Course Status :	Completed
Course Type :	Elective
Duration :	12 weeks
Category :
Credit Points :	3
Postgraduate
Start Date :	18 Jan 2021
End Date :	09 Apr 2021
Enrollment Ends :	01 Feb 2021
Exam Date :	24 Apr 2021 IST