Syllabus MATH 351-01 Introduction to Probability and Statistics II
Section 01: MWF 11 - 11.50am
Holt 291
Contact Information
Edward A. Roualdes (call me Edward)
Email: eroualdes@csuchico.edu
Office hours:
- For the first five weeks of the semester (1/21/2025 - 2/21/2025), my office hours will be in the Meriam Library Innovation Lab:
- Mondays 1pm - 2.30pm
- Tuesdays 1pm - 2.30pm
Thursdays 11am - 11.50amFridays 2pm - 3.45pm
- If none of those times work for you, please email me so that we can find a time that works for us both: eroualdes@csuchico.edu
- After the first five week of the semester, when my two online and asynchronous classes end, I'll reduce my office hours. I don't know how yet: maybe I'll take a poll; maybe I'll just keep the times for which most students showed within the first five weeks.
Course Description
Basic concepts of probability theory, random variables and their distributions, limit theorems, sampling theory, topics in statistical inference, regression, and correlation.
Student Learning Objectives / Goals
- Introduce the language of statistics
- Build understanding of random variables and their distributions
- Learn how probability connects to statistics, and statistics connects to data
- Introduce properties of statistics and sampling distributions
Resources
We will primarily use various chapters from Probability and Statistics - The Science of Uncertainty, Second Edition. This book is freely available as a PDF online, at the link above, hosted by the second author, Jeffrey S. Rosenthal.
Python Numpy Tutorial (with Jupyter and Colab)
We will also draw material from Wikipedia.
If you would like to buy a book, please consider any one of the following:
- A First Course in Probability
- An Introduction to Probability Theory and its Applications
- Weighing the Odds
Additional Requirements
- Access to a laptop will be essential to master the material of this course. If you don’t have immediate and consistent access to a laptop, please speak to me as soon as possible.
- We will learn to code in Python using Jupyter notebooks, both Python and Jupyter notebooks are free, open-source software.
Content Delivery
Lectures are in person at the times listed above. No recordings will be available. As Gil Scott-Heron says, the revolution will not be televised; this class will be live. Or maybe you prefer it's all the way live.
All course materials will be posted to my website: roualdes.us/math351.
Course Communication
The absolute best place to ask a question is during lecture. I understand, though, that not all students feel comfortable asking questions publicly.
If you prefer more private and in person communication, come to office hours.
If you prefer written and identifiable communication, email me at eroualdes@csuchico.edu. If your questions become too complex for email, as judged by me, I reserve the right to ask you to come visit my office to receive your answers in person.
If for any reason I need to address everyone in the course, I will use Canvas announcements.
Course Grading
Your final grade for this course will be given according to the +/- grading systems, based on the following percentages and scale: 90 to 100, A; 80 to <90, B; 70 to <80, C; 60 to <70, D; <60, F.
| Component | Percentage |
|---|---|
| Homework | 100% |
Grades will be posted to a shared (between me and each of you, individually and exclusively) Google Sheets file. Access is only granted to your@csuchico.edu account.
So long as you understand the Academic Integrity Policy below, I encourage you to use large language models, like ChatGPT, to help you through Homework and/or general learning. However, I refuse to grade AI slop. If I judge the content of a homework to be AI slop, I reserve the right to give you a failing grade on that assignment. You can meet me in office hours to try to defend your case.
Homework
All homework must be created using Jupyter notebooks, which are
then to be shared with me in a manner we mutually agree upon in
class via git and private shared GitHub repositories
that I will create (we decided in class on 2025-01-22).
class.
Homework will be part in class and part at home. I intend for the majority of homework to be completed in class, but I can not guarantee you won't have to finish some homework up outside of class. This is part of the reason that access to a laptop is essential to this course.
You can re-submit a homework that was previously submit on time after it was graded for up to 50% of your missed points back. Think of this as an attempt to correct some of your less-than-correct solutions. As an example, if you earned 80% on a Homework, you can re-submit this Homework with updated answers for a maximum of 10% added to your original score. Thus, you could obtain 90% on a homework for which you originally earned 80%.
You can not re-submit a submit-late Homework.
Tests
There will be zero tests.
Make-up Policy
Homework can be submit late for a maximum of 50% credit. You can submit a homework as late up until the last day of the regular semester, May 09 at 11:59pm.
You can not re-submit a submit-late Homework.
No homework will be accepted after May 09 at 11:59pm.
Diversity Policy
Respect: Students in this class are encouraged to speak up and participate during class meetings. Because the class will represent a diversity of individual beliefs, backgrounds, and experiences, every member of this class must show respect for every other member of this class (this includes me).
Academic Integrity Policy
Students are permitted and encouraged to collaborate on all assignments. However, each student must turn in their own work. Further, it is the expressed expectation of this instructor that all students demonstrate integrity and individual responsibility in all actions related to this course. Unethical behavior of any kind is unacceptable and will be prosecuted vigorously. Any sign of cheating in any way on any course assignment will be addressed directly, according to University standards. If you do not understand what plagiarism is, or what cheating entails, you must seek information regarding this matter from the current University Catalog and from me. The consequences of plagiarism begin with a failing grade on the work, and possibly a failing grade in the course, depending upon University action. More information is found on the Student Rights And Responsibilities campus webpage.
The use of artificial intelligence tools are in general not discouraged. At times, ChatGPT or some other large language model may help you through some otherwise challenging coding or writing problems. But submitting Homework exclusively based on such software is considered unacceptable and dishonest. In the end, there's nothing I can do to stop this behavior, other than warn you that future employers can quickly tell the difference between people who know the tools and ideas we'll develop in this course and those who don't. Please do use AI as a tool to help you be more efficient. Please don't become a statistician/data scientist/programmer who can't work without AI.
Disability Support
If you have any disability related needs, please contact Disability Support Service (Colusa Hall 898-5959 or campus information 898-INFO for directions) on campus to obtain the appropriate documentation. Afterwards, email me to identify your needs within the first two weeks of class so that any necessary arrangements can be made.
Confidentiality and Mandatory Reporting
As an instructor, one of my responsibilities is to help create a safe learning environment on our campus. I am required to share information regarding sexual misconduct with the University. Students may speak to someone confidentially by contacting the Counseling and Wellness Center (898-6345) or Safe Place (898-3030). Information on campus reporting obligations and other Title IX related resources are available at www.csuchico.edu/title-ix.
Course Outline
- Introduction to Python
- Recap MATH 350
- random variables
- distribution and density functions
- means and standard deviations
- probability
- percentiles
- Expectations of the Sample Mean
- mean, variance, bias
- mean squared error
- Central Limit Theorem (for the sample mean)
- Normal distribution theory
- mathematics
- simulations
- Inference
- point estiamtes
- likelihood
- re CLT
- Confidence Intervals
- traditional methods
- bootstrap CIs
- Linear Models
- single mean model
- multiple mean model
- linear regression
- bootstrap
- K-fold cross validation