Applied Regression Analysis
STAT 757 – Section 1001
Instructor: Colin Grudzien
Communication
Announcements and course updates will be posted in Webcampus. Students are expected to keep up-to-date on the course by reading these announcements for important information on assignments, midterms, etc.
Health and safety updates
The University of Nevada, Reno publishes it’s information for students including important policy updates and forms in this page. Students are expected to keep up-to-date with official communications from the university regarding health and safety, and university policies.
Help & Questions
There will be a classroom discussion forum where you can seek help from the instructor and peers about the assignments. Some assignments will require that you post and discuss results in this forum.
The best way to contact the instructor is via email or message within Webcampus. Messages received before 16:00 during business days will usually get a response the same day. Emails received after 16:00 or during the weekend are not guaranteed a same-day response. Longer questions will not be answered by email and will be directed to appointments or office hours.
The instructor will be available from 4:00 - 5:00 for public office hours and questions, shared with students in STAT 445/645. If you cannot attend office hours or if you need to discuss something privately you can set an appointment with the instructor directly.
Netiquette
When communicating online, you should always:
Treat your instructor and peers with respect, even in email or in any other online communication.
Use clear and concise language. Be respective of readers’ time and attention.
Remember that all college level communication should have correct spelling and grammar.
Avoid slang terms and texting abbreviations.
Use standard fonts that are optimized for online reading (e.g., sans serif) along with a consistent and readable size (12 or 14 pt.)
Avoid using the caps lock feature AS IT CAN BE INTERPRETED AS YELLING.
Limit and possibly avoid the use of emoticons. Not everyone knows how to interpret them.
Be cautious when using humor or sarcasm as tone is sometimes lost in an email or discussion post and your message might be taken literally or offensively.
Be careful sharing personal information online (both yours and other’s).
Always give proper credit when referencing or quoting another source. (Corollary: Don’t copy and paste another student’s post and claim it as original as that is essentially plagiarism.)
Don’t repeat someone else’s post without adding something of your own to it. (See corollary above regarding reuse of someone else’s post.)
Always be respectful of others’ opinions even when they differ from your own. When you disagree with someone, you should express your differing opinion in a respectful, non-critical way. (Corollary: Do not make personal or insulting remarks.)
Course description
Content
This course introduces students to fundamental techniques of applied regression within a frequentist perspective. The main goal is to empower learners to confidently perform and communicate a regression analysis of real data to address research questions in a reproducible framework. Students will learn to perform data analysis, construct regression models, quantify model uncertainty and qualify their analysis by evaluating model assumptions with diagnostics. Mathematical theory is introduced as needed throughout the course, but the main content emphasis is on the selection, application and evaluation of basic statistical regression models.
Structure for online learning
This class will follow a flipped design in which students are expected to watch one or more short lectures before the class session. This lecture will cover content that is relevant to the day’s activity in class. Class sessions will be held on Zoom and students are expected to attend the Zoom meeting to work on the day’s activities with their cohort. The day’s activities will require posting a discussion of the results according to the assignment rubric in the forum for completion credit. Quizzes and Midterms are taken online, open-book, open-note in WebCampus, but students are expected to work alone on these exams.
Catalog description
Techniques and applications of linear regression analysis: inference and model diagnostics.
Prerequisites
Students should be familiar with basic statistical notions such as hypothesis testing and confidence intervals, as well as with calculus and basic matrix operations. Advanced knowledge in the above subjects is not expected, and there will be some review of these topics. Basic scripting skills in R are required for the course, but can be learned through the Homework 1 in the first five weeks of the class. Students who are already confident in R scripting or have already completed the introduction to R modules in DataCamp can be excused from Homework 1 with instructor approval on a case-by-case basis.
Required software and technology
By signing up for this class you acknowledge the responsibility to have access to:
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A home desktop or home laptop computer that you can use for online quizzes and that you can install statistical software on and download data to.
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A reliable internet connection to consistently attend class activities and to access online class content or examination.
In the first two weeks of class students are required to obtain a free educational license of DataCamp, with instructions sent in an announcement by the instructor. You do not have to pay for DataCamp so please await instructions on how to sign up free through STAT 757.
You will also need to install the following software for class assignments:
Required book
There is no required textbook for this class. Students will be expected to follow the video lectures and the class activities to learn the content of this course, but additional recommended resources are the following:
Electronic resources
Students are responsible for checking their email accounts and Web Campus for announcements. Students are assumed to be aware of all information posted to these sources prior to each meeting. Announcements, homework and grades will be posted in Web Campus.
This page includes an archive of lecture notes and activities from the class, along with the current schedule for the semester.
Student learning outcomes
Upon completion of this course, students will be able to:
demonstrate understanding of the concepts that underly modern methods of linear regression, and critically assess the assumption associated with different statistical models;
interpret and discuss the results of regression analyses in a broader scientific context and using the terminology of the applied problem; and
perform essential regression analysis using a professional statistical package, write technical report, and present the results to a professional audience.
Assignments and grading
Video lectures
Each week there will be at least two short video lectures to watch before the class activities on Monday and Wednesday normal class sessions.
Activities and discussion posts
In a normal class session, students will work through a class activity that uses the content from the video lectures that week. The activity will require a written summary and analysis at the end of the day according to the activity rubric. There will be at least one discussion post required per activity, with the discussion prompt, the grading rubric and assignment due dates posted in Canvas.
Quizzes
There will be quizzes most weeks in WebCampus to evaluate learning of the concepts from the week. This will usually take the form of a multiple choice exam in WebCampus which is open-book, open-note, including electronic resources. Students are expected to work on the quiz alone, without help or information from any other individual – communication with any other individual about the quiz is a breach of academic integrity.
Homework
In the first five weeks of class, students will complete chapters of DataCamp’s Introduction to R. Students who are already confident in R scripting or have already completed the introduction to R modules in DataCamp can be excused from Homework 1 with instructor approval on a case-by-case basis.
Midterms
There are two midterms in this class, each taking the following format:
- Midterm 1: in-class. This midterm covers the introductory part of the course — this is tentatively scheduled for 09/25, 1:00 - 2:15 PM, with required identity verification and online proctoring. The exam will be open-book, open-note, but no electronic resources will be allowed on this exam. Communication between a student and anyone except the proctor during exam time will be considered cheating.
- Midterm 2: take-home project. This will be assigned on 10/12 and will be due on Friday 11/06, 11:59 PM. This project will be open book, open notes, open computer (including internet resources). This will take the form of an initial investigation and proposal for the final modeling project. Students may work with others on their project, but each student must submit their own work and individual report. See the Midterm 2 Assignment for details.
Final exam policy
- There will be a final modeling project and report. This project will be open book, open notes, open computer (including internet resources). Students may work with others on their project, but each student must submit their own work and individual report. The final project must be submitted on WebCampus by 12:00 PM on Monday, December 14. Details on the assignment are in the final project description.
Final grades
Final grades will be calculated according to the following:
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Category
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Percent of final grade
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Class activities
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\(20\%\)
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Quiz and homework average
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\(20\%\)
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|
Midterm 1
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\(20\%\)
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|
Midterm 2
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\(20\%\)
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|
Final exam project
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\(20\%\)
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The final letter grade will be assigned according to the weighted score as in the following table:
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Weighted score \(x\)
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Final letter grade
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\(90\% \leq x \leq 100\%\)
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A
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\(80\% \leq x < 90\%\)
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B
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\(70\% \leq x < 80\%\)
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C
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\(60\% \leq x < 70\%\)
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D
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\(0 \% \leq x < 60\%\)
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F
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Late policy and exceptions
There are no makeups for quizzes, exams or participation except for university recognized activities or exceptional circumstances, as per university policy. To accommodate unforeseen circumstances, the lowest quiz or homework score and the lowest two participation scores will be dropped from the final grade calculation. For undergraduates, a midterm score can be replaced with the final project score only once. If a student feels confident in R scripting at the start of the course and can demonstrate previous work, all DataCamp assignments can be excused and replaced with the score they receieve on the Midterm 1 Project. This is approved on a case-by-case basis by the instructor.
If a student needs to miss class due to participation in official university activities or a religious observance, they must make arrangements with the instructor at least one week prior to the date in question – the absence will not be given consideration without this advanced notice. In cases of absences due to extended illness, family emergency, bereavement, or other compelling reason, students should notify the instructor as soon as possible and within one week of the start of the absence. The instructor has the right to request formal, written documentation in such cases as they deem appropriate. Please see the full policy statement on absences.
Diversity statement
The University of Nevada, Reno is committed to providing a safe learning and work environment for all. Students are expected to treat each other and the instructor with respect. No form of harassment, discrimination or bullying will be tolerated. If you believe you have experienced discrimination, sexual harassment, sexual assault, domestic/dating violence, or stalking, whether on or off campus, or need information related to immigration concerns, please contact the University’s Equal Opportunity & Title IX Office at (775) 784-1547. Resources and interim measures are available to assist you. For more information, please visit the Title IX website for UNR
Disability services
Any student with a disability needing academic adjustments or accommodations is requested to speak with the Disability Resource Center (Pennington Student Achievement Center, Suite 230) as soon as possible to arrange for appropriate accommodations. More information can be found at the DRC website.
Academic conduct
No laptops, cell phones, mp3 players, or other electronics are to be used for personal reasons in class. If you are being disruptive during class you will be asked to leave. Disruptions in this context include inadequate participation. Please see our official Student Code of Conduct.
Academic success services
A common habit among successful students is to seek help outside of the classroom. Your student fees cover use of the Math Center (784-4433), Tutoring Center (784-6801), and University Writing Center (784-6030). These centers support your classroom learning; it is your responsibility to take advantage of their services.
Statement on Audio and Video Recording
Zoom meetings will generally be recorded and you will be asked for consent to be recorded to participate in these meetings. Surreptitious or covert video-taping of class or unauthorized audio recording of class is prohibited by law and by Board of Regents policy. This class may be videotaped or audio recorded only with the written permission of the instructor. In order to accommodate students with disabilities, some students may be given permission to record class lectures and discussions. Therefore, students should understand that their comments during class may be recorded.
Academic dishonesty
Cheating, plagiarism, or otherwise obtaining grades under false pretenses constitutes academic dishonesty according to the code of this university. Academic dishonesty will not be tolerated and penalties can include canceling a students enrollment without a grade or giving an F for the assignment or for the entire course. For more details, see the University of Nevada, Reno general catalog. The University Academic Standards Policy defines academic dishonesty, and mandates specific sanctions for violations. See the University Academic Standards policy: UAM 6,502.