Lecture Instructor: Jonathan “Nate” Wells (wellsj@reed.edu)
Nate’s Virtual Office: https://zoom.us/my/wellsj392
Nate’s Virtual Office Hours: Tues 10:30am - Noon, Thurs 3 - 4:30pm; or by appointment
Lab Instructor: Tom Allen (tomallen@reed.edu)
Tom’s Virtual Office: Link available in #announcements on Slack
Tom’s Virtual Office Hours: Mon 2:30pm - 4pm, Wed 1:30 - 3pm
TBA
Classroom: https://reed-edu.zoom.us/j/93938188045 (password available in #announcements on Slack)
Lab: Link and password available in #announcements on Slack
This course is an introduction to the basic ideas of statistical reasoning, data analysis, and probability theory, beginning with a survey of data visualization, processing, and summary techniques using the R programming language, continuing with an investigation of resampling and randomization methods for statistical inference and estimation, and concluding a comparison of simulation-based techniques to classical probability tools.
This course can be used towards your Group III, “Natural, Mathematical, and Psychological Science,” requirement. It accomplishes the following learning goals for the group:
This course does not satisfy the “primary data collection and analysis” requirement.
Daily readings will be assigned from the following sources:
Course Resources: The following web-based resources will be used for communicating class information:
Slack https://reedmath141s21.slack.com (announcements, discussions, direct messaging).
Course Website https://reed-statistics.github.io/math141s21/ (documents, a daily schedule, assignments, lecture notes, recorded lecture videos).
Gradescope https://www.gradescope.com/ (homework, lab, and daily reading submissions).
Our class will be conducted primarily online using Zoom. You will need the following during our scheduled class time: a computer with stable internet access, a webcam and microphone, a location where you can carry out a conversation at normal volume.
We will make very frequent use of the R programming language to create statistical models, run simulations, and implement stat learning algorithms. All homework will be completed using the RStudio IDE. R and RStudio are free to use, and can either be installed locally on your computer, or can be accessed using the Reed RStudio Server: https://rstudio.reed.edu/
If you would like to contact Nate or Tom, they can most easily be reached via Slack message weekdays between 9am and 4:30pm. While they try to answer messages as soon as possible, in some cases, they may not be able to respond until the following school day. If you’d prefer to talk live, send a message and we can schedule a time to chat on zoom.
By the end of the course, a student should be able to:
A typical week will involve the following:
Reading Assignment. Every class will have an assigned reading which you are required to review prior to the start of class.
Active Synchronous Lecture. Our 50-minute virtual meetings will include an interactive lecture by the instructor, with some time devoted to discussion either class-wide or in small groups. While lectures will be recorded and made available after class, you should plan to attend the lecture live whenever possible.
Code Review Lab. During lab time each week, students will present their code from the previous week’s lab assignment, discuss best practices and solutions, and survey material for the upcoming lab assignment. Students are expected to attend lab each week, and to have finished their assignment prior to the start of their lab section.
A prepared student will attend class for 50 minutes per day, three days each week, attend lab for 50 minutes one day each week, and spend about two to three hours per day of class on work outside the classroom (reading, doing homework, working on projects, discussing, studying, etc.). Together, this represents a 10 - 12 hour per week commitment.
Your grade in the class will be determined by your proficiency in each of the Course Outcomes, as demonstrated in the following assessments:
Statistical knowledge takes time to develop, and understanding deepens upon revisiting a concept a \(2^\text{nd}\), \(3^\text{rd}\), or \(n^\text{th}\) time. Studying basic terminology and elementary examples in the textbook before class means that lectures can be spent clarifying and expanding ideas, rather than introducing them. Daily reading assignments will be posted on a weekly basis on Slack. These assignments will list the specific section(s) to read for each day, along with a few basic questions to check comprehension. Answers are due bu by 7am the morning before each class day, and may either be hand-written and uploaded as a scan/photo to Gradescope, or typed and uploaded as a .pdf file to Gradescope. Up to three daily reading assignments may be missed without penalty.
The ability to immediately interrogate your beliefs and understanding through dialogue sets a live class apart from more passive means of education. Moreover, the online nature of our course means it is easier to become disconnected. For these reasons, you are expected to attend class regularly and to actively participate by asking questions, responding to class polls, and engaging in class discussion. If you are unable to attend class, you may make-up the absence by watching the recorded lecture video and completing a short accompanying assignment before the start of the next class meeting. Up to three classes may be missed without penalty.
Additionally, you are expected to make at least one significant contribution to our Slack workspace each week. Examples of significant contributions can be found on Slack.
Each week during lab sections, a lab assignment will be posted on the lab page of our course website, to be completed and submitted to Gradescope before the start of your lab section the following week. Solutions to each problem must be typed in an .rmd file, exported as a .pdf, and then uploaded as a .pdf to Gradescope. Up to twice throughout the term, you may request a three day extension on your lab assignment. Except in extraordinary circumstances, requests must be made prior to an assignment’s due date.
Throughout the term, you will work in groups of 3-4 on a project that answers a significant research question using real-world data, by implementing the fundamental techniques developed in our class, as well as some more advanced methods from supplementary sources. The project will culminate in a 10-minute video presentation and a 2-3 page technical report.
Two take-home midterm exams will be given during the term. The midterms will be made available on a Friday and should be completed during a single 2 hour session before the following Monday. Tentatively, the first is scheduled for Friday, February 26 (Week 5) and the second for Friday, April 2 (Week 10).
A cumulative final exam will be given during Finals Week. The exam will consist of both a take-home written component, as well as an oral component.
If illness or other circumstances prevent you from participating in class for 3 or more class days, please let me know as soon as possible so we can make appropriate arrangements for missed work.
Reed College is dedicated to creating inclusive learning environments. Please notify me as soon as possible if there are aspects of the instruction or design of this course that result in disability-related barriers to your participation. You are also encouraged to contact Disability & Accessibility Resources at dar@reed.edu, and to peruse the services offered on their website at https://www.reed.edu/disability-resources/.
Students are allowed and encouraged to collaborate on most in-class and homework assignments. However, any work that you turn in for grading must be your own. You are welcome to use internet resources to supplement content we cover in this course, with the exception of solutions to homework problems. Copying solutions from the internet is an Honor Principle violation. Exams will explicitly mention what resources may be consulted. All written work that references material outside of the textbook or lecture should be accompanied by an appropriate citation.
It is very important to stay on-top of material, since the course will move at a fairly brisk pace. If you find yourself falling behind, here are some suggestions for getting back on track:
We expect all members of Math 141 to make participation a harassment-free experience for everyone, regardless of age, body size, visible or invisible disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
We expect everyone to act and interact in ways that contribute to an open, welcoming diverse, inclusive, and healthy community of learners. Examples of unacceptable behavior include: using sexualized language or imagery, making insulting or derogatory comments, harassing someone publicly or privately, or other unprofessional conduct.
Instead you can contribute to a positive learning environment by demonstrating empathy and kindness, being respectful of differing viewpoints and experiences, and giving and gracefully accepting constructive feedback.
This Code of Conduct is adapted from the Contributor Covenant, version 2.0.
This is the schedule as of Day 1. A more up-to-date schedule can be found here.
| Week | Sections Covered | Week | Sections Covered |
|---|---|---|---|
| 1 | Structure of Data | 9 | Confidence Intervals |
| 2 | Grammar of Graphics | 10 | Hypothesis Testing (Exam 2) |
| 3 | Data Wrangling | 11 | Inference for Multiple Parameters |
| 4 | Principles of Data Collection | 12 | Inference for Regression |
| 5 | Linear Regression (Exam 1) | Spring Break | |
| 6 | Multiple Linear Regression | 13 | Logistic Regression |
| 7 | Probability | 14 | Reading Week |
| 8 | The Sampling Distribution | 15 | Final Exam |