UNM Stat 579.002: Response Surface Methodology (RSM)

Fall 2014 Syllabus is below table.

Fall 2014 schedule; Time: TR 1400-1515; Location: SMLC 120; STAT 579 002 = CRN 49592
Did you receive a registration error for Fall 2014? Send me an email with the following answers: 1. What registration error did you get (copy/paste is best)? 2. What is your UNM ID? 3. What is your Math/Stat background (that is, do you have the pre-reqs)?
Before first day set up R. Step 0: Set up R and Rstudio (1) Download R for windows or mac, (2) install Rstudio, and (3) install a package we’ll use with the following R command: install.packages("rsm"). R style matters. There is a lot of online help on R, such as at UCLA. Usually try searching for “R [mytopic]” and you’ll get lots of results.
News: 9/23 updated HW03 due date Tentative Timetable
Wk-Date Ch Topic Slides Code Data Pts HW sol FB Data HW Due Read MMA
01-08/19 01 Introduction Ch 01 R 25 HW01 sol 08/28 Ch 1 Background (from JB)
01-08/21 02 Building Empirical Models Ch 02 R Tab 2.8 120 HW02 sol 2.6 2.25 09/09 Ch 2
03-09/04 03 Two-Level Factorial Designs Ch 03 R Ex 3.1 Ex 3.2 125 HW03 sol 3.1 3.6 3.11 3.26 9/23 Ch 3 Sec 1-7
05-09/16 04 Two-Level Fractional Factorial Designs Ch 04 R Ex 4.5a Ex 4.5b 130 HW04 sol 4.4 4.7 4.11 4.20 10/02 Ch 4 Sec 1-6
06-09/25 05 Process Improvement with Steepest Ascent Ch 05 R Ex 5.1 50 HW05 sol 5.5 10/16 Ch 5 Sec 1-3, 5
07-10/02 06 The Analysis of Second-Order Response Surfaces Ch 06 R Ex 6.2 Ex 6.3 Ex 6.6 Ex 6.8 80 HW06 sol 6.9 6.14 10/28 Ch 6 Sec 1-4, 6
08-10/09 Fall Break
09-10/14 Cancelled(ACASA)
10-10/21 07 Experimental Designs for Fitting Response Surfaces — I Ch 07 R Ex 7.6 80 HW07 sol 11/06 Ch 7 Sec 1-3.2, 4-4.3, 4.5-4.7
11-10/30 08 Experimental Designs for Fitting Response Surfaces — II Ch 08 R 50 HW08 sol 11/13 Ch 8 Sec 2
13-11/11 10 Robust Parameter Design and Process Robustness Studies Ch 10 R Ex 10.5 50 HW10 sol 10.4 11/25 Ch 10 Sec 1-4
14-11/20 11 Experiments with Mixtures Ch 11 R Ex 11.1 Ex 11.2 none yet Ch 11 Sec 1-3
15-11/27 Thanksgiving break
16-12/02 Project Presentations Helicopter Drop! Contest Results R 50 of Project
16-12/04 Cancelled (travel)
17-12/09 Finals week Project reports due 12/09, 7:30-9:30am (date/time is guess) 100 of Project
CRAN Task View: Design of Experiments (DoE) & Analysis of Experimental Data Pre-design experiment guide sheet: pdf doc Notes from Fall 2014 using R: RSM_notes_F14.pdf includes all chapters in one document. Creative Commons License Lecture notes for Response Surface Methodology (RSM) Stat 579 University of New Mexico is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at


Description: Empirical model building and process optimization using experimental design and statistical modeling. The first half of the course covers building empirical first- and second-order empirical models, (fractional) factorial designs, and process improvement with steepest ascent.  The second half covers advanced topics including robust parameter design and mixture experiments.  A final project requires small teams of students to identify a process of their choosing, improve that process, and document their work in a short final report. Prerequisite: Stat 540, Stat 545, Math 314 Semesters offered: Special Lecture: Stat 579.002; TR 1400-1515; Location: TBA Office hours: Tue 11:00-12:00, Thu 15:30-16:30, and by appointment in SMLC 312 email: “Erik B. Erhardt” <>, please include “RSM” in subject line Textbook: “Response Surface Methodology: Process and Product Optimization Using Designed Experiments” (Wiley Series in Probability and Statistics); Authors: Raymond H. Myers, Douglas C. Montgomery, Christine M. Anderson-Cook; Publisher: Wiley; 3 edition (January 14, 2009); ISBN-10: 0470174463; ISBN-13: 978-0470174463; Amazon: Laptops running R: I encourage you to bring a laptop to class each day so you can try the R programming exercises in class. If you don’t have one, no problem, teamwork is encouraged — sit next to someone friendly who likes to share.

Student learning outcomes

At the end of the course, you will be able to: General outcomes:

1. Organize knowledge in graphs, tables, and code to support concise, comprehensible, and scientifically defensible written interpretations to produce knowledge.

2. Distinguish a testable scientific hypothesis or data-supported interpretation from an opinion.

3. Understand from a data story the goals of the study and apply the correct statistical procedure.

4. Explain the scientific aspects of a problem to nonscientists in a fashion that enhances understanding and decision making.

Topical outcomes:

5. Understand fundamental concepts of matching experimental designs with analysis models.

6. Recognize types of experimental designs and analysis models.

7. Perform and interpret a proper response surface analysis.

8. Define parameters of interest and hypotheses in words and notation.

9. Summarize data visually, numerically, and descriptively and interpret the observed characteristics. Calculate and interpret numerical summaries such as the estimated stationary point and predicted response and confidence bounds for each, and create visual summaries such as contour and surface plots.

10. Use statistical software, such as R, to read and manage data, create informative plots, report numerical summaries, apply statistical models, by recommended programming practice including abstraction and documentation.

11. Design experiments to infer causal treatment effects.

12. Identify and explain the statistical methods, assumptions, and limitations used in reported studies in scientific literature or popular media.

13. Evaluate and criticize published studies, the work of peers, and your own work and assess what was done well, what could be done better, and examine whether their conclusions are supported using statistical principles.

14. Make evidence-based decisions by constructing and deciding between testable hypotheses using appropriate data and methods.

15. Discover relationships and make predictions through model development and selection.

Meeting the learning outcomes

You will acquire new information in this class, but the emphasis is comprehending, integrating, and applying information. Rote factual memorization is the lowest form of learning. Effective learning takes place by explaining, integrating, applying, and analyzing facts, hypotheses, and theories. Learning in this class occurs by:
  1. Doing – completion of exercises that require analysis of data to answer questions and test hypotheses, or researching answers to reading assignments.
  2. Discussion – interaction with classmates to assemble and synthesize information you’d utilizing the collective skills and knowledge base of the group.
  3. Listening, acting, and reflecting – activities during class time provide insights into information not available in readings and includes review difficult material to aid comprehension. Note taking permits later reflection on lecture content. Listening to the professor lecture is the least effective learning tool for both students, however, and you should plan on coming to every class prepared to participate in active and reflective learning opportunities.
Learning without thought is labor lost. What I hear, I forget. What I see, I remember. What I do, I understand. – Confucius


Grading breakdown

Course grade is wholly homework and project-based.  There are assignments associated with each chapter and one final project. Semi-weekly homework: 75% Final Project: 25% (Report, presentation, and contest.) Rubrics guide assessment (and self-assessment) of homework, code, projects, exams, and presentations.


Homework is due 1 week (or 2 classes, whichever is shorter) after we complete each chapter. Extra points may be given for exceptional work based on the rubrics for homework and code.  For example, if you earn a 5+5+5 on the rubric, then your grade will be increased by 10% of what you earned for doing exceptional work. Header for homework assignments for each part:
First Last RSM Stat 579.2 HW ## MM/DD/YYYY
All R (or SAS) code for the assignment should be included with the part of the problem it addresses (for code and output use a fixed-width font, such as Courier). Do NOT use your R code and output as your answer to the problem, but include them to show me how you arrived at your answer. Your prose solution (in a non-fixed-width font) should be provided in addition to R output. Please hand in a physical version of your homework and projects, I will write comments on it and give it back to you. An electronic version will be accepted under exception circumstances (almost never) — better to have a classmate print and bring it to class in your absence. Late assignments will be penalized 20% if handed in by 5pm the following day, and will not be accepted after that.  Please slide your late assignments under my office door (SMLC 312) after writing the date/time in the upper-left hand corner of when you’re turning it in. Homework is designed to encourage you to review the material we’ve learned, synthesize new information from the R help pages or the web, and apply (and learn!) your new skills. Expect to spend 4-5 hours a week (outside of class!) to do well, and maybe double that to do outstandingly well.

Model answers

My solutions posted after each HW is due will provide model answers to have a sense of the quality and content I’m looking for.

Collaboration and citation

For homeworks (and obviously team projects) I encourage you to work together. Please discuss the data, code, and problems with one another, but do your own exploration and write up. We expect everyone to hand in substantially different homeworks, and we will enforce this under the honor code. The small benefit you might get from plagiarism is not worth the severe penalty. As in life, please use any resources available to you. Projects and some homeworks will explicitly encourage you to use resources on the internet, but showing extra initiative will always be appreciated. You may find R programming tough at first, so feel free discuss your problems with other classmates or meet with or email questions to me. I encourage you to use the ideas of others, but make them your own, giving credit. For projects have a formal bibliography, for homework cite casually, and for code copy the URL in a comment (which is doubly helpful for finding the resource again).

Team projects

Final project description – The final project requires small teams of students to improve the flight time of a paper helicopter.  The project consists of planning, designing, conducting, and analyzing an experiment, using appropriate design-of-experiment principles, and then using an optimization strategy to improve the process to a stable optimum.  Outcomes include a written project report with a final class presentation, as well as a competition between teams and the professor. Working in teams is how science gets done. Each member of the team is responsible for every part of the project. I know team projects can be frustrating, requiring maturity, mutual consideration, and professionalism throughout, but I hope to teach some skills that should make it less painful. Each project will receive a single grade, but individual grades will be weighted by effort as judged by the entire team if there are issues regarding unequal contribution.

Competition: 2011 Final project contest results and movie of drop, and class photo. John’s last words: “is that significant enough for you?”.


No exams. Note that the final project may take substantial time to develop and run experiments. We will complete our final project presentations and contest before or on the scheduled final date and time.

Disability statement

If you have a documented disability that will impact your work in this class, please contact me to discuss your needs. You’ll also need to register with the Accessibility Resource Center in 2021 Mesa Vista Hall (building 56) across the courtyard east from the SUB.
Random stuff: UNM has license for free online access to the definitive books for the Lattice and ggplot2 graphing platforms. Note you must be on campus or logged in through the UNM proxy to access these. R is currently available in these UNM Locations: DSH 141 and 143, Econ 1004, SMLC pods, and SUB IT-LoboLab Pod and IT-LoboLab Classroom. R style matters. There is a lot of online help on R, such as at UCLA, and Google’s Intro to R video series. Usually try searching for “R [mytopic]” and you’ll get lots of results.  ggplot2 plotting cookbook. R reference card by Jonathan Baron. Translate between MATLAB and R. Allen and Erhardt Figure checklist.  Choosing the right chart.  Nature Methods points of view on visualization. Raster vs vector graphics. Statistics pre-req refresher from Khan Academy. Coursera has a free 4-week course on computing for data analysis with R. Muddy points in perspective. R+LaTeX+knitr for reproducible research.  See my SC1 lecture notes (Ch01), and Mohammad Arbabshirani’s notes (pdf, rnw). Marginal improvements: Tim Harford: Pop-Up Ideas: Hotpants 0:00-4:20


SAS Examples, HW data, and Programs from 2011 (from 2014 onward we’ll be using R)

In Fall 2011, SAS was available in DSH labs 141 & 143, or you could purchase a 1 year license for $100 for SAS 9.2 for Windows. (macro *.mac files are *.sas files, run macro first, then use %[macro_name] to execute the macro) Table 2.8 Example 3.1 Example 3.2 Example 4.5 Example 6.2 Example 6.3 Example 6.6 Example 6.8 (2nd ed), Box-Cox transform Example 7.6 Constructing CCDs Design Evaluation Example 8.10 Design Augmentation Example 10.5a Example 11.1 Example 11.2 Generating Mixture Designs We will be using SAS’s Macros for the Design and Analysis of Experiments: ADX. Homework datasets: 2.6, SAS2.6,, 3.11, 3.264.4, 4.7,

Acumen in Statistics