### Group Work
#In this class, I would like you to work in groups for a variety of reasons. A large part of this class is communicating analysis. At the beginning of the block, groups will be formed. You should expect to work with this group every day. When we work in groups in class we may decide on roles, specifically who is controlling the one screen will rotate). Group members will rotate roles between tasks to help make sure everybody is sharing work. You won’t be working in a group for everything; any quizzes, and exams may be individual. Advanced Regression
Spring 2025 Block 8
Instructor
Instructor Dr. Tyler George
Cornell College, West 311
Class Meetings
August 26th to September 18th
By Appointment
West 201
Office Hours
MWTh 3:05pm-4:05pm and by appt.
West 311
You Are A Priority
My goal for this block is to help you learn the material. I want to first and foremost recognize that you are an individual and thus are unique and may learn uniquely. Additionally, your health and well-being are priority one. Learning cannot happen effectively if you don’t meet your other personal needs. That all being said, I have structured the class in a way that I, from experience teaching and learning myself, think will be most beneficial for the majority of students. I promise you that I will do my best to create an inclusive and engaging learning environment. I ask that you keep an open line of communication between us for when you may need help and/or flexibility. You and your learning are why I am here.
Course Description
Following a second regression course, this class will begin with a review of multiple linear regression, but now using R. New topics will include probability distributions, likelihoods, differentiating binary vs binomial logistic regression, and poisson regression including its variants. The class of generalized linear models will then be presented which unifies all past modeling approaches. All methods are presented using realistic case studies. Conducting and communicating the modeling process including exploratory data analysis, model exploration and selection, and inferences are all emphasized. Prerequisite(s): STA 202 and DSC 223. Alternate years.
Learning Objectives
This course supports the Educational Priorities and Outcomes of Cornell College with emphases on knowledge, communication, and reasoning. Specifically, the learning objectives of this course are: - Learn statistical distributions, likelihood functions, and types of models including poisson regression, quasi-logistic regression, multilevel models, and polynomial regression. - Ability to communicate statistical ideas clearly and accurately (communication). - Understand the class of generalized linear models and the models contained (knowledge, reasoning). - Ability to ascertain which type of analysis is appropriate for a particular data set and question (reasoning).
Prerequisite
To be successful in this class, you should have completed STA 201, STA 202, and DSC 223.
Open Access Books – Free!
All of the materials for this class are free.
The main textbook is: Beyond Multiple Linear Regression by Paul Roback and Julie Legler – it is freely available online. Chapters 1-9.
The secondary text is: An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani – it is freely available online. Chapter 7.
Course Site and Moodle
Our course will run from a combination of Moodle and the course website at https://stats-tgeorge.github.io/STA363_AdvReg/.
Software – No need to install
We will use a combination of technologies in this course including R, and RStudio (server). I have set up an RStudio server on a machine we have on campus that we will all access with a web browser. You don’t need to install any software (in fact for a while I prefer you don’t). More on this in class. If you are an off campus student, please let me know right away, as you may need to checkout a laptop (free) from IT to work on homework from home.
You can access the RStudio Server at: <http://turing.cornellcollege.edu:8787/>.
If you have any technical problems you should contact IT as soon as possible. Submit a Work Order!
Evaluations and grades
Grade Category Descriptions
Homework:
Homework will be graded for correctness. I will generally evaluate a random subset of the assigned questions. The goal is the practice the application of the method and then be able to interpret the result.
Participation
I will measure this by your class (meeting) attendance, and your work on labs and class examples. With class work, please save your script files in the folder that I can view on the RStudio server. I will look at these files to see if you are following along.
Labs and Mini-Projects
This class includes a variety of related techniques generally only 1 of them is the correct technique for the particular research question. For this reason, we will do labs following a single method and mini “projects” at the end of each major new method or collection of methods. With labs, the data will be provided. With mini-projects, you will likely need to find, and clean data, and then apply the new technique.
Exams
You will have two exams this block, tbd. Each will have two components. Component 1 will be on these dates and you will get a choice of oral or written format. Component 2 will be a take-home, open-book, open-note, exam. You will have 12 hours or more to complete this component.
| Assignment | Points |
|---|---|
| Homework | 200 |
| Participation (Attend Meetings) | 100 |
| Labs & Mini Projects | 300 |
| Exams, two 200pts exams | 400 |
| Total | 1000 |
| Grade | Range | Grade | Range |
|---|---|---|---|
| A | 93-100% | C | 73-76% |
| A- | 90–92% | C- | 70-72% |
| B+ | 87–89% | D+ | 67-69% |
| B | 83-86% | D | 63-66% |
| B- | 80-82% | D- | 60-62% |
| C+ | 77-79% | F | <60% |
AI Policy
The beta release of Dall-E-Mini in July 2022 and ChatGPT in November 2022 are among many tools using artificial intelligence. There is a good possibility that using tools like these are going to become an important skill for careers in the not distant future (https://www.theguardian.com/commentisfree/2023/jan/07/chatgpt-bot-excel-ai-chatbot-tech).
In the meantime though, it’s going to take a while for society to figure out when using these tools is/isn’t acceptable.
Work created by AI tools may not be considered original work and, instead, considered automated plagiarism. It is derived from previously created texts from other sources that the models were trained on, yet doesn’t cite sources. AI models have built-in biases (ie, they are trained on limited underlying sources; they reproduce, rather than challenge, errors in the sources) AI tools have limitations (ie, they lack critical thinking to evaluate and reflect on criteria; they lack abductive reasoning to make judgments with incomplete information at hand; they make up or use inaccurate information and may “hallucinate” sources that do not exist)
In this course, all informal writing should be written without the use of AI. The purpose of informal writing is to help you think through your ideas, connect with your lived experiences, and to figure out your thoughts and opinions. Using AI here subverts that process.
A final note: Other courses may have different AI policies, and it is important to be aware of the policy in each class.
DISABILITIES AND ACCOMODATIONS POLICY
Cornell College makes reasonable accommodations for persons with disabilities. Students should notify the Office of Academic Support and Advising and their course instructor of any disability related accommodations within the first three days of the term for which the accommodations are required, due to the fast pace of the block format. For more information on the documentation required to establish the need for accommodations and the process of requesting the accommodations.
ACADEMIC HONESTY POLICY
Cornell College expects all members of the Cornell community to act with academic integrity. An important aspect of academic integrity is respecting the work of others. A student is expected to explicitly acknowledge ideas, claims, observations, or data of others, unless generally known. When a piece of work is submitted for credit, a student is asserting that the submission is her or his work unless there is a citation of a specific source. If there is no appropriate acknowledgment of sources, whether intended or not, this may constitute a violation of the College’s requirement for honesty in academic work and may be treated as a case of academic dishonesty. The procedures regarding how the College deals with cases of academic dishonesty appear in The Catalog, under the heading “Academic Honesty.”
Illness Policy
If you are experiencing COVID-19 symptoms, do not attend class. Perform a home test or contact Director of Student Health Services Lynn O’Brien at student_health@cornellcollege.edu immediately to arrange a COVID-19 test at the Health Center. If you need to isolate due to COVID-19, or if you become unable to attend class for any other health reason, contact me as soon as possible to determine if you are able to continue in the class. A Withdrawal for Health Reasons may be required.
Mandatory Reporter Reminder
It is my goal that you feel supported and able to share information related to your life experiences during classroom discussions, in your written work, and in any one-on-one meetings with me. You should also know that all Cornell College faculty and staff are mandatory reporters. This means that I will keep information you share with me private to the greatest extent possible. However, I am required to share information regarding sexual assault, abuse, criminal behavior, or about a student who may be a danger to themselves or to others. If you wish to speak to someone confidentially who is not a mandatory reporter, you can schedule an appointment with one of the counselors in the Ebersole Health and Wellbeing Center or contact the College Chaplain, Rev. Melea White, at mwhite@cornelllcollege.edu.