ST 516 Fall 2019 Syllabus

Course Syllabus ST 516-001 Experimental Statistics for Engineers II

Fall 2019, 3 Credit Hours

Course Description:
This course focuses on practical application of model building and experimental design for engineering problems.  In model building, we extend concepts of inference and simple regression learned in ST 515 to multiple regression, including interaction effects and methods of variable selection such as cross validation. We also consider regularization techniques in ridge regression and lasso.  Next, we explore classification methods such as logistic regression and decision trees; we also apply tree-based algorithms to regression, and extend to machine learning techniques random forests and boosting. Finally, we provide an overview of neural networks.  An introduction to design of experiments is provided building on ST 515 concepts of ANOVA, beginning with single factor experiments and progressing to full factorial designs with fixed and random effects.  An optimal design approach is then applied to the study of screening and response surface designs, both with and without blocking, as well as split-plot and mixture designs.

Course Structure
The course meets for lectures twice per week. Students are required to complete in-class activities, homework, and three mid-term exams; final exam is optional.

Course Policies
Students are responsible for checking their unity email and the course website regularly for announcements and materials.

Teaching Team
Dr. Dan Harris - Instructor
Email: [email protected]
Office Location: 5118 SAS Hall
Office Hours: W 2-4 pm, F 9-11 am and by appointment

Rahul Ghosal - Teaching Assistant
Email: [email protected]
Office Location: 1101 SAS Hall
Office Hours: Tu/Th 4-5 pm

Note: You are welcome to attend any office hours listed above.

Course Meetings                    Tu/Th 8:30 – 9:45 pm, 1102 SAS Hall
Requisites and Restrictions   Prerequisite: ST 515, Graduate standing in the College of Engineering
Transportation                        This course will not require students to provide their own transportation. Non-scheduled class time for field trips or out-of-class activities is NOT required for this class.
Safety & Risk Assumptions   None

Course Materials

  • Optional Text: James Gareth, et. al, Introduction to Statistical Learning: with Applications in R, 1st Edition (8th printing), 2017, ISBN: 9781461471370 (free download for NCSU students https://catalog.lib.ncsu.edu/catalog/NCSU2798710)
  • Optional Text: Douglas Montgomery, Design and Analysis of Experiments, 9th Edition, 2017, ISBN: 9781119113478
  • Additional Resource: Jay L. Devore, Probability & Statistics for Engineering and the Sciences, 9th Edition, 2016, ISBN: 9781305251809
  • Additional Resource: Peter Goos and Bradley Jones, Optimal Design of Experiments: a Case Study Approach, 12h Edition, 2011, ISBN: 9780470744611
  • R and RStudio – open source software downloaded free of charge from r-project.org and www.rstudio.com
  • JMP – software available free of charge through NC State software portal

 

Grading
Homework — 100 points
There will be 6 homework assignments worth 40 points each. They will be posted to the course website and will be due in class one to two weeks after posting as noted. One lowest homework grade will be dropped.

Midterm Project: Model Building — 200 points
Due: 10/8/2019 Teams of 4-5 students will be assigned a data set with specific instructions on objective of modeling outcomes.  Final submission will be a 5-10 page report following a prescribed format.

Final Project: Design of Experiments — 200 points     
Due: 12/12/2019. Teams of 4-5 students will be assigned a specific research question to be answered using an experimental design. Students will be expected to identify an appropriate experimental procedure, then generate simulated data to demonstrate the efficacy of the design. Final submission will be a 5-10 page report following a prescribed format.

Total Possible — 600 points

Letter Grades
This Course uses Standard NCSU Letter Grading. Percentage cutoffs are firm and no rounding occurs.

Requirements for Credit-Only (S/U) Grading
In order to receive a grade of S, students are required to take all exams and quizzes, complete all assignments, and earn a grade of C- or better. Conversion from letter grading to credit only (S/U) grading is subject to university deadlines. Refer to the Registration and Records calendar for deadlines related to grading. For more details refer to http://policies.ncsu.edu/regulation/reg-02-20-15.

Requirements for Auditors (AU)
Information about and requirements for auditing a course can be found at http://policies.ncsu.edu/regulation/reg-02-20-04.

Policies on Incomplete Grades
If an extended deadline is not authorized by the instructor or department, an unfinished incomplete grade will automatically change to an F after either (a) the end of the next regular semester in which the student is enrolled (not including summer sessions), or (b) the end of 12 months if the student is not enrolled, whichever is shorter. Incompletes that change to F will count as an attempted course on transcripts. The burden of fulfilling an incomplete grade is the responsibility of the student. The university policy on incomplete grades is located at http://policies.ncsu.edu/regulation/reg-02-50-3.

Late Assignments
All due dates are firm and no late work will be accepted, no exception. Computer failures, lost files, sickness or other difficulties are not valid excuses for submitting an assignment late.

Attendance Policy
For complete attendance and excused absence policies, please see http://policies.ncsu.edu/regulation/reg-02-20-03.

Makeup Work Policy
There is no make up for homework assignments.

Students who are unable to attend an exam for a legitimate unavoidable reason may take a make-up exam only if they provide suitable documentation. According to university policy, a student must notify the instructor in advance if s/he will miss an exam. If it is not possible to notify the instructor in advance, the instructor must be given notice as soon as possible after the exam.

Suitable documentation of an absence: Examples include a physician’s note in case of illness or letter from the University or a student's advisor. Students who have a personal emergency (extreme family illness or death, etc.) should contact the Division of Academic & Student Affairs (515-2446; http://dasa.ncsu.edu/) to obtain documentation.

Academic Integrity and Honesty
Students are required to comply with the university policy on academic integrity found in the Code of Student Conduct found at http://policies.ncsu.edu/policy/pol-11-35-01.

See http://policies.ncsu.edu/policy/pol-11-35-01 for a detailed explanation of academic honesty.

Copying someone else's work and presenting it as your own is plagiarism. Plagiarism, cheating, and other forms of academic dishonesty will not be tolerated. To create a fair and equitable environment, the instructor aggressively enforces the universities policies on academic misconduct. Although working together on written assignments to overcome obstacles is encouraged, each student must compose and write their own analysis and reports. All cases of academic misconduct will be handled as set out in university policies.

Honor Pledge
Your signature on any test or assignment indicates "I have neither given nor received unauthorized aid on this test or assignment."

Electronically-Hosted Course Components
Students may be required to disclose personally identifiable information to other students in the course, via electronic tools like email or web-postings, where relevant to the course. Examples include online discussions of class topics, and posting of student coursework. All students are expected to respect the privacy of each other by not sharing or using such information outside the course.

Accommodations for Disabilities
Reasonable accommodations will be made for students with verifiable disabilities. In order to take advantage of available accommodations, student must register with the Disability Services Office (http://www.ncsu.edu/dso), 919-515-7653. For more information on NC State's policy on working with students with disabilities, please see the Academic Accommodations for Students with Disabilities Regulation at http://policies.ncsu.edu/regulation/reg-02-20-01.

Non-Discrimination Policy
NC State University provides equality of opportunity in education and employment for all students and employees. Accordingly, NC State affirms its commitment to maintain a work environment for all employees and an academic environment for all students that is free from all forms of discrimination. Discrimination based on race, color, religion, creed, sex, national origin, age, disability, veteran status, or sexual orientation is a violation of state and federal law and/or NC State University policy and will not be tolerated. Harassment of any person (either in the form of quid pro quo or creation of a hostile environment) based on race, color, religion, creed, sex, national origin, age, disability, veteran status, or sexual orientation also is a violation of state and federal law and/or NC State University policy and will not be tolerated. Retaliation against any person who complains about discrimination is also prohibited. NC State's policies and regulations covering discrimination, harassment, and retaliation may be accessed at http://policies.ncsu.edu/policy/pol-04-25-05 or http://www.ncsu.edu/equal_op/. Any person who feels that he or she has been the subject of prohibited discrimination, harassment, or retaliation should contact the Office for Equal Opportunity (OEO) at 919- 515-3148.

Tentative Course Schedule (subject to change)

Week 1: Introduction to statistical learning, R

Week 2: Review simple linear regression, introduce multiple regression

Week 3: Interaction effects, diagnostics, response transformations

Week 4: Variable selection, cross validation

Week 5: Ridge, lasso, principal components

Week 6: Logistic regression, decision trees

Week 7: Bagging, random forests, boosting

Week 8: Overview of neural networks, Midterm project due in class 10/8

Week 9: Intro to design of experiments/JMP, single factor experiments

Week 10: Factorial designs, fixed and random effects

Week 11: Screening designs

Week 12: Response surface designs

Week 13: Blocking in screening and response surface designs

Week 14: Designing experiments with covariates

Week 15: Split-plot designs

Week 16: Mixture experiments

Week 17: Final project due 12/12 by 4:00pm