STAT 326 Labs and Projects

Site Information

Welcome! This site contains code and exercises for the labs in STAT 326 (Mathematical Statistics), information for the mini projects in the course, and information about the final portfolio. While we will also do plenty of handwritten work to help understand concepts, the primary purpose of this site is to hold all of the computing/coding work in one place.

The syllabus for the entire course is also given here.

Instructor Information

  • Professor: Matt Higham
  • Office: Bewkes 123
  • Email: mhigham@stlawu.edu
  • Office Hours: 15 minute slots bookable at https://calendly.com/mhigham/prof-higham-office-hours. The link to book is also on our Canvas home page.
    • Note that you must book a time for office hours at least 12 hours in advance to guarantee that I am present and available at that time.

Course Materials

Course Information

Welcome to STAT 326. The purpose of this course is to strengthen statistical reasoning skills, applying the foundational tools learned in MATH/STAT 325 to more statistical contexts.

General Course Outcomes

  1. Given a probability model, derive a point estimator for parameter(s) in that model using data.

  2. Describe properties of what might make an estimator “good.”

  3. Given a probability model, derive an interval estimator for parameter(s) in that model using data.

  4. Explain the difference between a “frequentist approach” to statistics and a “Bayesian approach” to statistics. Apply a Bayesian approach to analysis to binomial data and to count data.

  5. Derive and conduct hypothesis tests about parameters in a probability model using classical statistics tests and simulation-based permutation tests.

  6. Use statistical simulation to answer questions about how statistical methods work and how violations of assumptions of statistical methods impact results.

Use of R and RStudio

We will use RStudio throughout the semester as a tool to help us understand concepts in this course.

  • R and R Studio are both free to use.
  • You should already have both R and R Studio installed on your personal computer from MATH/STAT 325.
  • The purpose of this class is not to learn R in detail. The concepts for the course receive a strong priority.

Assessment

There are 1000 total points that can be earned in this course from components described below. An up-to-date list of all relevant due dates and exam dates will be found on Canvas.


Assessments

There will be 5 100-point assessments throughout the semester, given on Wednesdays. More details about what you might expect on these assessments will be provided closer to the date of the first assessment.

Additionally, 100 points will be given to the average of your highest 4 assessments. Therefore, the total number of points from assessments will be 600 points.

Mini-Projects

On many of the Wednesdays on “off-weeks” (weeks where you do not have an assessment), you will complete five different mini-projects. The topics of these mini-projects vary widely from project to project, but, by the end of the semester, you can expect to have completed statistical simulation studies, a meaningful story, a Bayesian data analysis, and an analysis on the benefits and drawbacks of using p-values in statistics.

Each mini-project will be worth 30 points, for a total of 150 points.


Homework Assignments

Weekly Homeworks are worth 10 points each and are due most weeks on Mondays. Homeworks will be graded for both correctness and completion, though you will be given “checkpoints” on each homework problem so that you can check your work as you go along.

One homework will be dropped from your grade so the total number of points from Homework Assignments will be 90 points.

You may work with other students on the homeworks, but please make sure to read the Rules for Collaboration section before doing so. If you need additional help outside of collaboration with classmate or office hours, the Peterson Quantitative Research Center (PQRC) in Valentine Hall is a great resource!


Recap Tasks

In addition to homework assignments, you will also be assigned “recap tasks” to complete by the start of class every Monday. The purpose of these recap tasks is to give you additional practice on concepts discussed in class. Each recap task is worth 5 points. There will be 13 recap tasks throughout the semester, and one recap task will be dropped from your grade. Therefore, there are 60 points to be earned from these recap tasks.

You may not use AI on recap tasks, unless otherwise stated.


Final Portfolio

A final portfolio will be assigned near the end of the semester. A component of your final portfolio will be answering some questions about it in person so you should make sure that you are around for our final exam the morning of May 6. More details will be provided later, but the total number of points for this final portfolio is 100 points.

Point Allocation

The 1000 points possible for the class will be allocated in the following way:

  • Assessments: 600 points: 100 points for each of 5 in-class assessments, plus 100 additional points for the average of your top 4 assessments.
  • Mini-Projects: 150 points: 30 points for each of 5 mini-projects, none of which will be dropped from your grade.
  • Homework Assignments: 90 points: 10 Homeworks for 10 points each for a total of 90 points (with one of the 10-point homeworks dropped).
  • Recap Tasks: 60 points: 13 recap tasks, each worth 5 points, with one recap task dropped from your grade, for a total of 60 points.
  • Portfolio: 100 points: 1 final portfolio. Details of what will go in your final portfolio will be described later.


Grading Scale

Grade 4.0 3.75 3.5 3.25 3.0 2.75 2.5 2.25 2.0 1.75 1.5 1.25 1.0 0.0
Points 950-1000 920-949 890-919 860-889 830-859 800-829 770-809 750-769 720-749 700-719 670-699 640-669 600-639 0-599

Collaboration, Diversity, Accessibility, and Academic Integrity

Rules for Collaboration

You are allowed to collaborate with your classmates (or your classmates from the other section of this course) for homework assignments and handouts with the following rules.

  • you must state the name(s) of who you collaborated with at the top of each assessment.
  • all work must be your own. Even if you work with someone else, you must write your answers on your own. Therefore, I expect your answers to free response questions to be at least slightly different from the person(s) you collaborated with.
  • you must not copy answers directly from the Internet or directly from the homework solutions file.
  • this isn’t a rule, but keep in mind that collaboration is not permitted on any of the exams. Therefore, when working with someone, make sure that you are both really learning so that you both can have success on the exam assessments.

AI Policy

Throughout the semester, we will use generative AI (specifically, ChatGPT) to aid us in performing basic calculations and in evaluating integrals. For homework assignments and mini-projects, the policy on AI usage will be stated at the top of the assignment. For recap tasks, you may not use AI to assist in coming up with your answer. Make sure that you follow this policy. While AI can be a tool to enhance your learning, becoming overly reliant on it prevents the growth of logical thinking skills.

Diversity Statement

Diversity encompasses differences in age, colour, ethnicity, national origin, gender, physical or mental ability, religion, socioeconomic background, veteran status, sexual orientation, and marginalized groups. The interaction of different human characteristics brings about a positive learning environment. Diversity is both respected and valued in this classroom.

Accessibility Statement

Your experience in this class is important to me. It is the policy and practice of St. Lawrence University to create inclusive and accessible learning environments consistent with federal and state law. If you have established accommodations with the Student Accessibility Services Office in the past, please activate your accommodations so we can discuss how they will be implemented in this course.

If you have not yet established services through the Student Accessibility Services Office but have a temporary health condition or permanent disability that requires accommodations (conditions include but not limited to; mental health, attention-related, learning, vision, hearing, physical or health impacts), please contact the Student Accessibility Services Office directly to set up a meeting. The Student Accessibility Services Office will work with you on the interactive process that establishes reasonable accommodations.

Color Vision Deficiency: The Student Accessibility Services office can loan glasses for students who are color vision deficient. Please contact the office to make an appointment.

For more specific information about setting up an appointment with Student Accessibility Services please see the options listed below:

Telephone: 315.229.5537

Email: studentaccessibility@stlawu.edu

Website: https://www.stlawu.edu/offices/student-accessibility-services

Academic Integrity

Academic dishonesty will not be tolerated. Any specific policies for this course are supplementary to the

Honor Code. According to the St. Lawrence University Academic Honor Policy,

  1. It is assumed that all work is done by the student unless the instructor/mentor/employer gives specific permission for collaboration.
  2. Cheating on examinations and tests consists of knowingly giving or using or attempting to use unauthorized assistance during examinations or tests.
  3. Dishonesty in work outside of examinations and tests consists of handing in or presenting as original work which is not original, where originality is required.

Claims of ignorance and academic or personal pressure are unacceptable as excuses for academic dishonesty. Students must learn what constitutes one's own work and how the work of others must be acknowledged.

For more information, refer to www.stlawu.edu/acadaffairs/academic_honor_policy.pdf.

To avoid academic dishonesty, it is important that you follow all directions and collaboration rules and ask for clarification if you have any questions about what is acceptable for a particular assignment or exam. If I suspect academic dishonesty, a score of zero will be given for the entire assignment in which the academic dishonesty occurred for all individuals involved and Academic Honor Council will be notified. If a pattern of academic dishonesty is found to have occurred, a grade of 0.0 for the entire course can be given.

It is important to work in a way that maximizes your learning. Be aware that students who rely too much on others for the homework and projects tend to do poorly on the quizzes and exams.

Please note that in addition the above, any assignments in which your score is reduced due to academic dishonesty will not be dropped according to the homework policy e.g., if you receive a zero on a homework because of academic dishonesty, it will not be dropped from your grade.

PQRC

The Peterson Quantitative Resource Center (PQRC) offers free, no appointment necessary peer tutoring across a range of courses with quantitative content. The PQRC student staff of mentors is trained to assist students to develop and to improve their quantitative skills and understanding. More information about the PQRC’s current hours and modes of operation can be found at the PQRC webpage: https://www.stlawu.edu/offices/pqrc

Tentative Schedule

The following gives a tentative schedule and tentative weeks for exams. Note that these may change.

Week Date Due Topic
0 1/15 NA Sampling Distributions and Simulation
1 1/20 NA Sampling Distributions, Simulation, and Order Statistics
2 1/27 Mini-Project 1 (Simulation) MLEs and MOM Estimators
3 2/3 Assessment 1 Properties of Estimators
4 2/10* NA Properties of Estimators
5 2/17 Mini-Project 2 (Meaningful Story) Pivot Method for Confidence Intervals
6 2/24 Assessment 2 STAT 113 Confidence Intervals
7 3/3 Mini-Project 3 (Confidence Intervals) Bootstrapping
8 3/10 Assessment 3 Bayesian Inference
NA 3/17 Spring Break NA
9 3/24 NA Bayesian Inference
10 3/31 Mini-Project 4 (Bayesian) Likelihood Ratio Tests
11 4/7 Assessment 4 Likelihood Ratio Tests
12 4/14 Mini-Project 5 (p-values) STAT 113 Hypothesis Tests
13 4/21 Assessment 5 Type I Errors, Type II Errors, Power
14 4/28 Portfolio Checkpoint Power