Stat 439 - Syllabus
Spring 2020
Contents
- Instructor contact information and office hours
- Meeting times and location
- Course description
- Prerequisites
- Course materials and technology
- Classroom community
- Assessment and Evaluation
- Course policies
Instructor Contact Information
email: stacey.hancock@montana.edu
Office: Wilson 2-195
Phone: 406-993-5350
Office Hours
- Tue/Thur 11am-1pm
- Thur 2-3pm
- Also available via the D2L Discussion forums and by appointment.
Meeting Times and Location
Stat 439 meets on Tuesdays and Thursdays, 9:25-10:40am in Roberts Hall 209.
Course Description
Stat 439 will provide an introduction to the principles and general methods for the analysis of categorical data. This type of data occurs extensively in both observational and experimental studies, as well as in industrial applications. While some theoretical statistical detail is given, the primary focus will be on methods of data analysis. Problems will be motivated from a scientific perspective.
By the end of this course, students should understand:
- categorical response data and how it differs from continuous response data
- statistical inference for proportions, including the use of likelihood, Wald procedures, score procedures and likelihood-ratio procedures
- contingency table analysis including 2x2 and higher-order tables, estimation and testing for odds ratios, chi-squared tests of independence, Fisher's Exact Test
- the basic theory and interpretation of generalized linear models, including logistic regression, multinomial logistic regression, and Poisson regression
- analysis of matched pairs categorical data.
- the basics of the analysis of correlated and clustered categorical response data including generalized estimating equations and generalized linear mixed models.
Prerequisites
Entrance to STAT 439 requires completion of STAT 412 or STAT 512 (Methods for Data Analysis II).
Course Materials and Technology
Textbook
The required textbook for the course is Alan Agresti's An Introduction to Categorical Data Analysis, 3rd edition, Wiley, 2018. Though the 3rd edition is preferred, you may also use the 2nd edition of this textbook (Wiley, 2007). (Note: Agresti has another textbook called Categorical Data Analysis. Make sure you are using his book that starts with "An Introduction...".)
Additional Textbook Related Links
R
We will use the statistical programming language R through the RStudio interface. See my Statistical Computing Resources page for download instructions and extra resources.
Learning Management Tools
We will use the following tools for learning management:
- Course Website: Find links to this syllabus, R resources, data sets, and the course calendar.
- D2L: Find announcements, instructor notes, exam review material, assignments, discussion forums, and gradebook.
Classroom Community
All members of the classroom community (instructor, students, visitors) are expected to treat each other with courtesy and respect. Our comments to others should be factual, constructive, and free from harassing statements. You are encouraged to disagree with others, but such disagreements need to be based upon facts and documentation (rather than prejudices and personalities). It is the instructor’s goal to promote an atmosphere of mutual respect in the classroom.
The success of all students in this course depends on all members of the classroom community agreeing to:
- Show up and be present
- Listen
- Contribute
- Build on other's ideas
- Speak clearly and loudly enough for all to hear
Please contact the instructor if you have suggestions for improving the classroom environment.
Assessment
Grades will be posted in D2L as they become available. Your grade in this course will be based on the following:
| Component | Percent of Course Grade |
|---|---|
| Homework and Data Analyses |
15% |
| Quizzes | 40% |
| Group project | 15% |
| Final Exam - Monday, May 4, 12:00-1:50pm | 30% |
| TOTAL |
100% |
Letter grades will be assigned as approximately (cutoffs may be slightly lowered at the end of the semester):
93-100 = A; 90-92 = A-; 88-90 = B+; 83-87 = B; 80-82 = B-; 78-80 = C+; 70-77 = C; 60-69 = D; below 60 = F
Grade Component Details
A group data analysis project will be completed throughout the course. Due dates and details will be posted in D2L.
Course Policies
If you cannot be in class, it is your responsibility to notify the instructor with as much advance warning as possible. While attendance is not directly tied to your grade, attending and being present in class is highly positively associated with your final course grade.
Late work in this course is not accepted. Portions of each component of the grade are dropped to allow for unexpected absences.
In the case of a personal emergency, contact your instructor via email or phone on or before the due date or quiz date.
While working with your fellow students on homework can aid in the learning process, it must be done responsibly. To ensure you are adhering to our academic integrity policies,
- Attempt all problems on your own and write down your solutions before collaborating with others.
- When collaborating with others, do not look at another student's homework or code. Instead, use a white board or talk through the problem verbally.
- Take responsibility of your own learning. If someone tells you how to do a problem, you will forget. If you work through the problem on your own when you get stuck, you will remember.
- If you do collaborate with another student, note on the homework who you collaborated and on which problems.
- Cite all additional sources (including Google searches, Stack Overflow, etc.)
Copying another student’s solutions is not collaboration -- it is cheating. The first instance of cheating on a homework will result in a zero grade for that assignment for all parties involved. The second instance will also result in a zero grade, and will be reported to your student record.
Any instance of cheating on a quiz or exam will result in a zero grade for the quiz/exam and will be reported to your student record. Egregious instances of academic dishonesty may be subject to more severe sanctions.
If you find that a calculation error was made in grading an assignment, quiz, or exam, notify your instructor as soon as possible so I can correct it!
If you feel you deserved a better grade than that which was awarded, first carefully review the graded assignment/quiz/exam and feedback. Then, if needed, ask your instructor to go over how the question was graded so you understand what you missed.
If you find you are falling behind on the material, use our resources to get additional help immediately! Waiting until after the final exam, and then asking your instructor to reconsider your grade at is unfair to other students and will not be considered.
Behavioral Expectations: Montana State University expects all students to conduct themselves as honest, responsible and law-abiding members of the academic community and to respect the rights of other students, members of the faculty and staff and the public to use, enjoy and participate in the University programs and facilities. For additional information reference see MSU's Conduct Guidelines and Grievance Procedures for Students and Student Rights & Responsibilities.
Collaboration: Discussing assignments with others (in your group for example) is a good way to learn. Giving others answers is not doing them a favor, because then they aren't learning the material. Copying from others is cheating, and will not be tolerated. University policy states that, unless otherwise specified, students may not collaborate on graded material. Any exceptions to this policy will be stated explicitly for individual assignments. If you have any questions about the limits of collaboration, you are expected to ask your instructor for clarification.
Plagiarism: Paraphrasing or quoting another’s work without citing the source is a form of academic misconduct. Even inadvertent or unintentional misuse or appropriation of another’s work (such as relying heavily on source material that is not expressly acknowledged) is considered plagiarism. If you have any questions about using and citing sources, you are expected to ask for clarification.
Academic Misconduct: Section 420 of the Student Conduct Code describes academic misconduct as including but not limited to plagiarism, cheating, multiple submissions, or facilitating other’s misconduct. Possible sanctions for academic misconduct range from an oral reprimand to expulsion from the university.
Section 430 of the Student Code allows the instructor to impose the following sanctions for academic misconduct: oral reprimand; written reprimand; an assignment to repeat the work or an alternate assignment; a lower or failing grade on the particular assignment or test; or a lower grade or failing grade in the course.
See the Stat 216 Academic Integrity Policy as described above.
Academic Expectations: Section 310.00 in the MSU Conduct Guidelines states that students
- be prompt and regular in attending classes;
- be well prepared for classes;
- submit required assignments in a timely manner;
- take exams when scheduled;
- act in a respectful manner toward other students and the instructor and in a way that does not detract from the learning experience; and
- make and keep appointments when necessary to meet with the instructor.
In addition to the above items, students are expected to meet any additional course and behavioral standards as defined by the instructor.
Monday February 3 is the last day to withdraw without a "W" grade.
Tuesday April 14 is the last day to withdraw with a "W" grade.
University policy is explicit that the adviser and instructor must approve requests to withdraw from a course with a grade of “W”. Students who stop attending and stop doing the work are not automatically dropped. Taking a “W” does not hurt your GPA but it is a sign that you are not making progress toward your degree, and could affect your financial aid or student loans.