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 available 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 and R Studio.
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 25th||Evidence-Informed Systems Engineering (EISE)|
|Mon, Aug 30th||Visualization|
|Wed, Sep 1st||Visualization|
|Mon, Sep 6th||Visualization/Extremes|
|Wed, Sep 8th||Principle Components Analysis (PCA)|
|Friday, Sep 10th, 11:59pm (ET)||Lab 1 : Visualization|
|Mon, Sep 13th||Principle Components Analysis (PCA)|
|Wed, Sep 15th||PCA and R markdown notebooks|
|Friday, Sep 17th, 11:59pm (ET)||Lab 2 : Principle Component Analysis|
|Mon, Sep 20th||Multiple Linear Regression (MLR)|
|Wed, Sep 22nd||Multiple Linear Regression (MLR)|
|Friday, Sep 24th, 11:59pm (ET)||Lab 3 : MLR Lab 1|
|Mon, Sep 27th||Multiple Linear Regression (MLR)||
Release Project 1
Due Friday October 8th, 11:59pm (ET).
|Wed, Sep 29th||Multiple Linear Regression (MLR)|
|Friday, Oct 1st, 11:59pm (ET)||Lab 4 : MLR Lab 2|
|Mon, Oct 4th||Project 1 Group time|
|Wed, Oct 6th||Midterm Review|
|Friday, Oct 8th, 11:59pm (ET)||Lab 5 : Midterm Review Lab|
|Mon, Oct 11th||No Class -- Fall Break|
|Wed, Oct 13th||Midterm Exam|
|Mon, Oct 18th||Generalized Linear Models (GLM)||
Release Graduate Final Project
Due Friday December 17th, 11:59pm (ET).
|Wed, Oct 20th||Generalized Linear Models (GLM)|
|Friday, Oct 22nd, 11:59pm (ET)||Lab 6 : GLM Lab 1|
|Mon, Oct 25th||Generalized Linear Models (GLM)|
|Wed, Oct 27th||GLM Contest|
|Friday, Oct 29th, 11:59pm (ET)||Lab 7 : GLM Lab 2|
|Mon, Nov 1st||Time Series Analysis|
|Wed, Nov 3rd||Time Series Analysis|
|Friday, Nov 5th, 11:59pm (ET)||Lab 8 : Time Series Lab 1|
|Mon, Nov 8th||Time Series Analysis|
|Wed, Nov 10th||Time Series Analysis||
Release Project 2
Due Monday December 6th, 11:59pm (ET).
|Friday, Nov 12th, 11:59pm (ET)||Lab 9 : Time Series Lab 2|
|Mon, Nov 15th||Project 2 Group Time|
|Wed, Nov 17th||Bootstrapping and Simulation|
|Mon, Nov 22th||Bootstrapping and Simulation|
|Wed, Nov 24th||No Class -- Thanksgiving|
|Mon, Nov 29th||Bootstrapping and Simulation|
|Wed, Dec 1st||Bootstrapping and Simulation|
|Friday, Dec 3rd, 11:59pm (ET)||Lab 10 : Bootstrapping and Simulation|
|Mon, Dec 6th||Course Recap and Final Exam Review|
|Friday, Dec 10th, 11:59pm (ET)||Bonus Lab 11 : Final Exam Review|
|Friday, Dec 17th, 2-5pm (ET)||Final Exam|
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 Sunday night, 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 midnight. 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 (for SYS 4021 students only):
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 --
however there might be small discussions pre and post-lecture which might not be recorded -- if these take place they are not considered essential and they will
be communicated through other means (e.g. email or UVA Collab). 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!
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,
cheat, or steal, nor shall they tolerate those who do. We recognize that even one honor infraction can
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.
Discrimination and power-based violence:
The University of Virginia is dedicated to providing a safe and equitable learning environment for all students. To that end, it is vital that you know two values that the University and us hold as critically important:
- Power-based personal violence will not be tolerated.
- Everyone has a responsibility to do their part to maintain a safe community on Grounds.
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 in writing directly to any or all of the instructors by Piazza private message 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.
"The University of Virginia strives to provide accessibility to all students. If you require an accommodation to fully access this course, please contact the Student Disability Access Center (SDAC) at (434) 243-5180 or email@example.com. If you are unsure if you require an accommodation, or to learn more about their services, you may contact the SDAC at the number above or by visiting their website at https://www.studenthealth.virginia.edu/student-disability-access-center/about-sdac." If you need any specific accommodations in the format of the lectures, videos, etc, please communicate it to the instructor as soon as possible.
You have many resources available to you when you experience academic or personal stresses. In addition to your professor, the School of Engineering and Applied Science has three staff member located in Thornton Hall who you can contact to help manage academic or personal challenges. Please do not wait until the end of the semester to ask for help! Lisa Lampe, Director of Undergraduate Education (academic), Blake Calhoun, Director of Undergraduate Success (academic), Alex Hall, Assistant Dean of Students (non-academic issues). In addition to having an Assistant Dean of Students embedded in Engineering, we are also fortunate to have two CAPS counsellors embedded in our School. You may schedule time with Elizabeth Ramirez-Weaver or Katie Fowler through Student Health. When scheduling, be sure to specify that you are an Engineering student.