CBE 5779
Transcript Abbreviation:
Experiment Design
Course Description:
Design and analysis of experiments with emphasis on applications in engineering.
Course Levels:
Undergraduate (1000-5000 level)
Graduate
Designation:
Elective
General Education Course
(N/A)
Cross-Listings
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
3.00
Total Completions Allowed
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: Jr or Sr standing in CBE.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 779.
Course Goals / Objectives:
Familiarize w/ statistical concepts and terminology in chemical systems and processes: data types & distributions, Central Limit Theorem, sampling, replication & randomization, constructing & testing statistical hypotheses, type I & type II errors
Become familiar with proper ways to report results using critical values, p-values, confidence intervals, and graphical techniques
Master the application and interpretation of the analysis of variance (ANOVA) of data from relatively simple experiments
Master fundamental principles of linear regression models, including models for both continuous and categorical (discrete) factors
Master basic methods of measuring the adequacy of a model, including: analysis of residuals, variance tests, R2 and adjusted R2 statistics, lack-of-fit test
Become familiar with retrospective power analysis and its relation to sample size
Master the basic principles of factorial designs with emphasis on 2k factorial designs, the use of blocking to handle nuisance variables, and fractional factorial designs
Become familiar with the response surface methodology for optimization experiments
Be exposed to advanced experimental designs strategies, including ?optimal? designs, split-plot designs, mixture designs, and method of augmenting existing designs
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Data types and different ways to classify variables; basic statistical concepts and terminology: sampling and sampling distributions; histograms | 2.0 | 0.0 | 0.0 | 0 |
| Different types of probability distributions; standardization and normalization; the Central Limit Theorem; extreme values in a distribution | 5.0 | 0.0 | 0.0 | 0 |
| Constructing and testing a statistical hypothesis: the null and alternative hypotheses, type I and type II errors; p-value; one-and two-sample means tests | 3.0 | 0.0 | 0.0 | 0 |
| Confidence intervals; matched pairs designs; checking the normality assumption; hypothesis tests about variances | 5.0 | 0.0 | 0.0 | 0 |
| Analysis of data from experiments with a single factor; means and effects models; ANOVA; comparing multiple pairs of means and the least significant difference (LSD) | 4.0 | 0.0 | 0.0 | 0 |
| Outliers; measures of model adequacy (residuals analysis, R2 and adjusted-R2, variance tests) | 2.0 | 0.0 | 0.0 | 0 |
| Retrospective and prospective power analysis; linear regression models, parameter estimation, hypothesis tests for model parameters; checking model adequacy | 4.0 | 0.0 | 0.0 | 0 |
| Blocking design strategies; factorial design; two-way ANOVA; factor coding; response curves and surfaces; models containing both continuous and discrete factors | 4.0 | 0.0 | 0.0 | 0 |
| Blocking in a factorial design; screening experiments: the 2k factorial design; replication and center points | 4.0 | 0.0 | 0.0 | 0 |
| Blocking and confounding in the 2k factorial design,complete and incomplete blocking strategies; two-level fractional factorial designs; augmenting a design | 4.0 | 0.0 | 0.0 | 0 |
| Response surface methodology (RSM) for process/product optimization; "optimal" designs, mixture (chemical composition) designs | 6.0 | 0.0 | 0.0 | 0 |
| Total | 43 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework (6) | 25% |
| Survey participation | 25% |
| Midterm Exam | 25% |
| Final Exam | 25% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Design and Analysis of Experiments, John Wiley & Sons (2004), 6th Edition | Montgomery, Douglas C |
ABET-CAC Criterion 3 Outcomes
(N/A)
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes
(N/A)
Embedded Literacies Info
(N/A)
Attachments
(N/A)
Additional Notes or Comments
(N/A)