Course Syllabus
Contact Information
My interest has from an early age been in things mathematical - Physics and Math. In later life, I turned to Ancient Greek and statistics and earned a Ph.D. in Bayesian statistics at the University of Auckland New Zealand. These days I teach and develop classical and Bayesian statistics courses as well as innovate using the R workspace.
Course Details
For a list of course activities, scroll to the bottom of this page.
Course Format
Multi-modal: This course will be a combination of online synchronous meetings and asynchronous instruction and work. However, synchronous meetings are optional to attend in real-time; you may choose to watch the recordings after the Live Sessions instead of attending if needed.
Course Prerequisites
It is strongly recommended that you have completed MATH 4753G since many statistical concepts will be built on distributional theory and R procedures developed in MATH 4753G. Otherwise, special permission can be awarded through the director.
Course Materials
If you have previously taken MATH 5743 or 4753, you should already have the textbook for this course.
- Statistics for Engineering and the Sciences 6th Edition by Mendenhall and Sincich - Chapters 11-17
Program Learning Outcomes (PLO)
- Statistical Knowledge: Explain and apply statistical concepts, theory, practice and technology to solve problems and inform decision-making.
- Modeling & Computation: Perform modeling and computations, and develop and implement techniques to gain insight into a given problem or question.
- Data Management: Design, implement and operate data management systems for real-world projects.
- Data Analytics: Collect, clean/process, manipulate, interpret and use data for decision-making and other applications.
- Communication & Consultation: Design products/studies and communicate findings clearly in written, verbal and visual form to a variety of audiences, from specialists in the field to non-specialists.
- Ethics: Demonstrate an awareness of ethical issues that arise in data science and articulate ethical considerations for conducting data analysis.
Course Learning Outcomes (CLO) Alignment
| CLO | Description | PLO |
|---|---|---|
| A | Create regression models. | 2 |
| B | Apply regression models to real-world data. | 2 |
| C | Compare different approaches to experimental design. | 1 |
| D | Create point and interval estimates for model parameters. | 1 |
| E | Distinguish between regression models. | 1 |
| F | Conduct multiple linear regression analysis, categorical data analysis, ANOVA, and nonparametric tests. | 4 |
| G | Apply linear algebra theory to multiple linear regression. | 1 |
Grades
Breakdown
| Activity | Description | Percentage |
|---|---|---|
| Labs |
Most weeks, students will submit an R package showing application of the concepts from the module. |
20% |
| Quizzes |
Each quiz will have 5 items at 2 pts each: multiple choice, multi-select, matching, ordering. Each quiz will have no time limit, and students will have only one attempt. Open book and open notes allowed. |
10% |
| Project |
There will be one major project in the course, which will demonstrate students' ability to connect concepts and apply them to new situations. |
20% |
| Midterm |
Students will have a comprehensive exam for all topics covered to this point in the course. It will include short-answer and long-answer questions. There will be a 1-hour time limit, and the exam will have 10 parts of 10 points each. |
20% |
| Final |
Students will have a comprehensive exam for all topics covered in the course. It will be short-answer questions and long-answer questions. There will be a 1-hour time limit, and the exam will have 10 parts of 10 points each. |
30% |
| Total |
Scale
| Percentage | Letter Grade |
|---|---|
| 90–100% | A |
| 80–89% | B |
| 70–79% | C |
| 60–69% | D |
| Below 60% | F |
Course Components
Module Overviews
For each module, you will have an introduction page that will provide an overview of the topic and prime you for what you are about to learn. It will also offer specific learning objectives and a to-do list to help you plan your week.
Quizzes
The quizzes will help to ensure that you have the foundational knowledge and/or component skills required for the course and will help inform you for the labs, where you will be asked to apply or extend your learning. This auto-graded quiz holds you accountable for the resources and will help to check your understanding of key concepts. These are intensive quizzes. Although they are open-book, they are very challenging.
Labs
Most weeks, you will submit a lab at the end of the week showing application of the concepts presented. These will typically involve the creation and submission of R packages. This will allow you and the instructor to assess your progress on the material and give you any necessary feedback that might help you to improve your performance on the project, quizzes, exams, and future labs. These will be graded based on their level of completeness. Perfection is not required, but you should show an appropriate level of effort to complete every part of the lab. Be sure to check the feedback comments even if you receive full credit, as you still may need to improve your process before attempting the project or exams.
Project
You will submit one major project during the course. You will combine and apply knowledge to solve a real-world problem; through this, you will show your mastery over multiple concepts and applications presented cumulatively.
Resources
Each week, you will be given resources (textbook readings, pre-recorded videos, etc.) to help you in the application of the concepts. These resources will help you gain the foundational knowledge needed to do well on the weekly assessments and successfully participate in the Live Sessions.
Live Sessions
Every other week, you will attend or watch a recorded Live Session. The instructor will introduce the week’s project and offer good and bad examples of approaches to similar problems. Students will discuss problems as a group and do group activities to scaffold each other’s knowledge of the topic at hand.
Midterm & Final
Twice during the semester, you will have comprehensive exams that will allow the instructor to assess your mastery of the course content. You will have one hour to complete each exam.
Course Policies
Communication
You may contact me through the help board on Canvas or by email if you have urgent and/or personal needs. My email address is wayne.s.stewart@ou.edu
The course help boards will be checked each day and you should get a response in less than 24 hours, Monday-Friday.
Feedback
Grading should be accomplished in less than a week and you should get feedback and an explanation for the grade you obtained.
Late Policy
Late work will not be accepted except for exceptional circumstances - family bereavement, sickness, etc. All work will be given a zero if not submitted on time.
Cheating
I expect that you may ask for help from peers and/or tutors, especially in the early stages of an assignment. However, the final submission of your work on each assignment should be your own and represent your own understanding. This helps both me and you to be able to clearly see where you are in the material, and it allows me to better help you in clarifying your understanding.
University Academic Policies and Student Support
Access the University Academic Policies Document.
Course Summary:
| Date | Details | Due |
|---|---|---|