SYS4021/6021:Linear Statistical Models| Fall 2021
Basic Course Information
What are the contributing factors to the severity of train accidents? How do you predict if an e-mail is spam? How can you translate goal-directed problems such as these into actionable decisions and meaningful recommendations that can have vast societal implications? How can you harness multi-dimensional, heterogeneous data to analyze the problem? In this course, we will explore Evidence Informed Systems Engineering (EISE) practices and how they can be applied to difficult, open-ended problems.
The primary tools for EISE come from linear statistical models and this course demonstrates the use of these models for problem understanding, prediction, and control. We will learn how to formulate hypotheses, build statistical models to test them, and make recommendations based on our findings. These steps can be laden with biases, for example in the data avai le to test these hypotheses, and in the metrics used to assess success. We will learn how to identify and prevent these biases to ensure equitable outcomes.
The specific modeling tools we will cover include principal components analysis, multivariate linear regression, logistic regression, time series analysis, and simulation and bootstrapping. In class, we will concentrate on the theory and practice of model construction, while weekly labs will assess your understanding of the theory and ability to apply it in practice. Projects will provide open-ended problem solving situations that illustrate the broad applicability of the methods in a setting similar to what you will encounter in the real world. We hope these projects illustrate the value of statistical modeling and that the course provides a foundation for future learning.
- Gain an appreciation of the ability of statistical modeling to inform engineering design
- Apply evidence-informed systems engineering approaches to solve real-world problems
- Formulate meaningful, testable hypotheses around those problems from associated data
- Identify appropriate statistical modeling technique(s) to test those hypotheses
- Assess the limitations of the information available to solve identified problems
- Uncover bias, errors, outliers, and influential observations in data and models
- Derive an actionable recommendation with statistical confidence using the evidential reasoning process
- Communicate the application of the evidence-informed systems engineering process to a problem through a technical summaries/reports directed to a client and / or practicing engineer
- Recognize the limitations of methods learned in class, but have the foundation to learn more advanced modeling tools when those covered in class are insufficient
SYS 3060, SYS 3034, and APMA 3012 or equivalent. It is recommended that students have a basic command of linear algebra, calculus, and statistics. We will use R for data analysis and R Studio for our programming sessions. Student are encourage to familiarize themselves with R programming, R for Data Science, and R Studio.
No required textbook, but students are encouraged to read chapters from:
- Linear Models with R by Julian Faraway.
- An Introduction with Statistical Learning by Gareth James and et. al.
- Applied Linear Statistical Models by Michael H. Kutner, Christopher J. Nachtsheim, John Neter.
- Using R for Introductory Statistics, simpleR by John Verzani.
Disclaimer: The professors reserve to right to make changes to the syllabus, including weekly lab, project, and exam due dates. These changes will be announced as early as possible.
|Wed, Aug 24th||Evidence-Informed Systems Engineering (EISE)|
|Mon, Aug 29th||Visualization|
|Wed, Aug 31st||Visualization||
Release Lab 1
Friday September 2nd, 9:00am (ET).
|Mon, Sep 5th||Visualization|
|Wed, Sep 7th||Extremes|
|Mon, Sep 12th||Principle Components Analysis (PCA)|
|Wed, Sep 14th||Principal Components Analysis (PCA)||
Release Lab 2
Friday September 16th, 9:00am (ET).
|Friday, Sep 16th, 11:59pm (ET)||Due Lab 1 : Visualization|
|Mon, Sep 19th||RDM Notebooks/PCA Exercise|
|Wed, Sep 21st||Multiple Linear Regression (MLR)|
|Mon, Sep 26th||Multiple Linear Regression (MLR)|
|Wed, Sep 28th||Multiple Linear Regression (MLR)||
Release Lab 3
Friday September 30th, 9:00am (ET).
|Friday, Sep 30th, 11:59pm (ET)||Due Lab 2 : Principle Component Analysis|
|Mon, Oct 3rd||No Class -- Fall Break|
|Wed, Oct 5th||Multiple Linear Regression (MLR)||
Release Project 1
Due Sunday October 30th, 11:59pm (ET).
Release Lab 4: Midterm Review Lab
Friday October 7th, 9:00am (ET).
|Mon, Oct 10th||Multiple Linear Regression (MLR)|
|Wed, Oct 12th||Project 1 Group Time|
|Sunday, Oct 16th, 11:59pm (ET)||Due Lab 3 : Multiple Linear Regression Lab|
|Mon, Oct 17th||Midterm Review|
|Wed Oct 19th - Fri Oct, 21st, 22nd||Midterm Exam (virtually)|
|Friday, Oct 21st, 11:59pm (ET)||Due Lab 4 : Practice Midterm Review Lab|
|Mon, Oct 24th||Generalized Linear Models (GLM)|
|Wed, Oct 26th||Project 1 Group Time||
Release Lab 5
Friday October 28th, 9:00am (ET).
|Sunday, Oct 30th, 11:59pm (ET)||Due Project 1|
|Mon, Oct 31st||Generalized Linear Models (GLM)|
|Wed, Nov 2nd||Generalized Linear Models (GLM)|
|Mon, Nov 7th||Generalized Linear Models (GLM) - contest|
|Wed, Nov 9th||Time Series Analysis|
|Friday, Nov 11th, 11:59pm (ET)||Due Lab 5: Generalized Linear Models|
|Mon, Nov 14th||CLASS CANCELED|
|Wed, Nov 16th||Time Series Analysis||
Release Grad Project
Due Friday December 16th, 11:59pm (ET).
Release Lab 6: Time Series
Friday November 18th, 9:00am (ET).
|Mon, Nov 21st||Time Series Analysis|
|Wed, Nov 23rd||No Class -- Thanksgiving|
|Mon, Nov 28th||Time Series Analysis||Release Project 2|
|Wed, Nov 30th||Project 2 Group Time||
Release Lab 7: Final Review Lab (Bonus)
Friday December 2nd, 9:00am (ET).
|Mon, Dec 5th||Course Recap and Final Exam Review|
|Monday, Dec 5th, 11:59pm (ET)||Due Lab 6: Time Series|
|Sunday, Dec 11th, 11:59pm (ET)||Due Project 2|
|December, Dec 16th, 11:59pm (ET)||Due Lab 7 : Final Exam Review (Bonus)|
|Thur Dec 8th - Fri Dec 16th||Final Exam (virtually)|
|December, Dec 16th, 11:59pm (ET)||Due Grad Final Project|
Student Evaluation and Assessment
- Weekly labs: 20% (lowest dropped)
- Hands-On Activities: 10%
- Projects: 30%
- Midterm Exam: 20%
- Final Exam (4021) /Final Project (6021): 20%
- Class Participation: +% (extra) -- includes synchronous participation + Piazza + office hours participation.
On Friday morning at 9 am ET, bi-weekly laboratory assignments based upon course and laboratory notes will be posted via the Tests & Quizzes feature on the course Collab site.
These assignments provide exercises in R programming that supplement the material covered in class and provide the foundation for the projects.
Each lab assignment requires students to program in R and analyze a supplied data set.
The assignments are designed to assess your knowledge on statistical modeling techniques and their mechanics. These assignments must be done individually, include an honor pledge, and be completed by Friday night at 11:59 pm ET, typically two weeks later. While there is no time limit for these assignments, they are designed not to take more than 50 minutes. Laboratory sessions are excellent practice for exams and real-world analysis under time constraints. At the end of the semester, the lowest grade on the lab assignments will be dropped. Additionally, if you complete the Bonus Lab, it will replace your next lowest lab.
Hands-on activities will be used throughout the course to allow you to practice the methods covered in class, recognize opportunities to apply them in your own work, and discover their shortcomings. Students can still participate in the activities if participating online and will submit their activity on Collab under "Assignments" for pass/fail credit.
The class will have two group projects on real-world topics and data sets. These exercises provide a real-world context for what we learn and are open-ended problem solving experiences that illustrate the concepts of evidence-informed systems engineering. Hence, they provide the opportunity to demonstrate understanding of class material using real data to solve a goal-directed problem. Projects are designed to teach students how to perform a detailed analysis as well as how to proficiently communicate results as in a technical document or client report.
Exams are based entirely on classroom notes and discussions, readings, projects, and laboratory assignments. Each exam will contain a closed book section with short answer questions and an open book section requiring analytical problem solving. The undergraduate final examination (for SYS 4021 students only) is cumulative. Example questions for both exams will be provided before the exams.
Final Project (for SYS 6021 students only):
The final project will be a detailed data analysis of a topic and dataset of your choosing. We encourage you to do something related to your research and are happy to work with you in selecting a data source and defining a project. You can also choose to extend any one of the projects. For instance, you could choose to do an extended analysis of train accidents. You must submit your topic description and data sources for your final project at the specified date on Collab. In your final project, you must show competence in a subset of topics discussed in the class. Specifically you must organize your work according to the principles of Evidence Informed Systems Engineering and use two methods from the following topics: visualization, principal components, multiple linear regression, generalized linear models, time series analysis, bootstrapping, and advanced topics.
Submission and Late Submission Policy:
On the day a project is due you must submit an electronic copy in pdf (NOT doc or docx, etc.) along with source code on the Collab site and pledge your submission.
No late assignments will be accepted in this class, unless the student has procured special accommodations for warranted circumstances.
We acknowledge the ongoing pandemic and want to be mindful of any special circumstances associated with it. We will be accommodating also due to exceptional circumstances but this is a large class so please make sure this is truly warranted and contact us as soon as possible. In many cases you will do better to submit an incomplete assignment rather than a late one.
Recording of Lectures:
We will be recording every lecture in order to accommodate students who will be learning remotely. All students will have access to the recordings
after class, but only online students should attend remotely unless you receive permission because you are sick or cannot attend for some other
reason. Because lectures include fellow students, you and they may be personally identifiable on the recordings.
We might set aside some time at the end for questions that will not be recorded -- this will be announced when it takes place.
These recordings may only be used for the purpose of individual or group study with other students enrolled in this class during this semester.
You may not distribute them in whole or in part through any other platform or to any persons outside of this class, nor may you make your own recordings of this class unless written permission has been obtained from the Instructor and all participants in the class have been informed that recording will occur. If you want additional details on this, please see Provost Policy 008 and follow-up guidelines. If you notice that we have failed to activate the recording feature, please remind us!
We try to create a safe environment, not only for our students, but also for our faculty and our staff. To that end, please stay home or in your dorm room if you are ill with or are symptomatic for any communicable disease. I would rather you stay home and work something out with me for making up work or taking an exam than for an illness to spread through the class. If you believe you are sick, please contact Student Health for appropriate treatment or testing.
It is the University's long-standing policy and practice to reasonably accommodate students so that they do not experience an adverse academic consequence when sincerely held religious beliefs or observances conflict with academic requirements.
Students who wish to request academic accommodation for a religious observance should submit their request to us by private message on Piazza as far in advance as possible. Students who have questions or concerns about academic accommodations for religious observance or religious beliefs may contact the University’s Office for Equal Opportunity and Civil Rights (EOCR) at UVAEOCR@virginia.edu or 434-924-3200.
It is our goal to create a learning experience that is as accessible as possible. If you anticipate any issues related to the format, materials, or requirements of this course, please meet with us outside of class so we can explore potential options. Students with disabilities may also wish to work with the Student Disability Access Center (SDAC) to discuss a range of options to removing barriers in this course, including official accommodations. We are fortunate to have an SDAC advisor, Courtney MacMasters, physically located in Engineering. You may email her at sdac.studenthealth.virginia.edu. If you have already been approved for accommodations through SDAC, please send us your accommodation letter and meet with us so we can develop an implementation plan together.
Academic Integrity Statement:
"The School of Engineering and Applied Science relies upon and cherishes its community of trust.
We firmly endorse, uphold, and embrace the University’s Honor principle that students will not lie,
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destroy an exemplary reputation that has taken years to build. Acting in a manner consistent with the principles
of honor will benefit every member of the community both while enrolled in the Engineering School and in the future.
Students are expected to be familiar with the university honor code,
including the section on academic fraud."
In summary, if assignments are individual then no two students should submit the same source code -- any overlap in source code of sufficient similarity will be potentially flagged as failure to abide to the Honor Code. You can discuss, you can share resources, you can talk about the assignment but not share code as this would potentially incur on an honor code violation. Regardless of circumstances we will assume that any source code, text, or images submitted alongside reports or projects are of the authorship of the individual students unless otherwise explicitly stated through appropriate means. Any missing information regarding sources will be regarded potentially as a failure to abide by the academic integrity statement even if that was not the intent. Please be careful clearly stating what is your original work and what is not in all assignments.
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Engaging in your career development is an important part of your student experience. For example, presenting at a research conference, attending an interview for a job or internship, or participating in an extern/shadowing experience are not only necessary steps on your path but are also invaluable lessons in and of themselves. I wish to encourage and support you in activities related to your career development. To that end, please notify me by email as far in advance as possible to arrange for appropriate accommodations.
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