| name: Henry Scharf (you can call me Henry) | office: GMCS 518 or virtual |
| email: hscharf@sdsu.edu | office hours: T/Th 10:00-10:55am or by appt. |
| (please let me know you’re coming; either in person or virtual) |
| course number: STAT 410 | semester: Fall 2022 |
| meeting times: T/Th 2:00–3:15pm | room: GMCS 421 (back up room) |
| mode of delivery: lecture/lab | platform: Canvas, Gradescope |
| prerequisites: Statistics 350B and CS150. | |
If for some reason we cannot meet in person as a class, we will adopt a synchronous, virtual schedule. Classes will happen at the same scheduled time, but we’ll utilize this Zoom room (link will also be available through Canvas on our homepage and calendar).
For millennia, the Kumeyaay people have been a part of this land. This land has nourished, healed, protected and embraced them for many generations in a relationship of balance and harmony. As members of the San Diego State University community, we acknowledge this legacy. We promote this balance and harmony. We find inspiration from this land, the land of the Kumeyaay.
For essential information about student academic success, please see the SDSU Student Academic Success Handbook.
Facility with the R software environment has become a crucial skill for managing, manipulating, and analyzing data. In this course, you will learn the basic constructs, syntax, and workflow of R programming for summarizing and visualizing data and for performing and reporting results from statistical analyses. R programming will be introduced through a review and more advanced development of statistical inference and regression modeling.
In-class Quizzes (15%): A very short quiz will be given during the first 5-10 minutes of each class meeting. The lowest two scores will be dropped. All quizzes will contribute equally.
Homework (35%): Homework assignments will be given every 2-3 weeks in the form of an Rlab (see schedule). Each assignment will begin in class (virtually), and students will have 8 days to submit their solutions online.
Exams (25%): There will be two in-class exams.
Final Project (25%): Groups will be made up of 3-4 students chosen by me. Each group will identify one or more real data sets and perform an analysis using statistical methods from this course (and beyond, if desired).
I try to grade assignments as quickly as I can because I think it is most useful for students to receive feedback as soon as possible. Grades and feedback will be posted on Canvas. If you have a question about grades or notice an inaccuracy, please let me know right away.
Letter Grade: Students earning final grades in the following ranges will receive the corresponding letter grade or higher (square brackets are inclusive, round parentheses are not).
| Percent | Letter | Grade Point |
|---|---|---|
| [90, 100] | A | 4.0 |
| [80, 90) | B | 3.0 |
| [70, 80) | C | 2.0 |
| [60, 70) | D | 1.0 |
| [0, 60) | F | 0 |
Late Policy: Brief extensions will be granted for assignments when a reasonable request if made at least 48 hours before the due date. If no arrangements have been made in advance, a late penalty of 25% of the total assignment grade per day will be assessed.
Academic Honesty: The University adheres to a strict policy prohibiting cheating and plagiarism including:
The California State University system requires instructors to report all instances of academic misconduct to the Center for Student Rights and Responsibilities. Academic dishonesty will result in disciplinary review by the University and may lead to probation, suspension, or expulsion.
A Handbook of Statistical Analyses Using R [HSAUR3] by Hothorn and Everitt.
Available through SDSU bookstore or online. This text is available through Immediate Access
R for Data Science by Hadley Wickham and Garrett Grolemund. Especially for those who live in, or are curious about, the tidyverse.
A free online text intended for beginning R users written by the Chief Scientist and Director of Learning at RStudio.
Applied Linear Statistical Models by Kutner, Nachtsheim, Neter, and Li.
A definitive text on linear statistical models, including regression and generalized linear models. This is a great secondary reference for topics in this class, and also a good text to have for general use.
Introduction to Statistical Learning by James, Witten, Hastie, and Tibshirani
A terrific text on modern statistical methods that ranges from regression to deep learning.
Computational Statistics by Geof Givens and Jennifer Hoeting
A great computational statistics reference for those interested in going above and beyond the content of this course.
The textbooks for this course are provided in a digital format by the first day of classes and are free through the add/drop date. Your SDSU student account will then be charged a special reduced price for use of the materials for the remainder of the semester unless you opt-out of the content by 11:59 PM on the add/drop date. Please note that both books above are immediate access. You must opt-out of any of the books you do not want. Please visit www.shopaztecs.com/immediateaccess for additional information about Immediate Access pricing, digital subscription duration, print add-ons, opting out and other frequently asked questions. If you have access questions, please refer to the RedShelf Solve link.
Available here, subject to change.
Motivation to participate in this class needs to come primarily from within. Some of the assessment structures provide minimal external nudging to keep students going (e.g., quizzes), but for the most part your success will be a product of your own internal desire to actually learn this stuff. For my part as the instructor, this means I will try to keep topics as immediately relevant for you, the students, as possible. I will try to be responsive to your requests throughout the semester. If you find something boring/useless, I’ll try to take it out. If you want me to go into more depth on a particular topic, I’ll try to make time to do that.
For your part as a student, this means you will have to manage your own time carefully and do whatever you must to make assignments/projects relevant for you. When there is an opportunity, find data sets you care about and want to analyze. Focus on methods you want to be able to take with you throughout your career. A fully engaged student will probably find that she is frequently searching online for more information about something we discussed in class. He may find himself listening to unassigned podcasts and reading blogs written by experts. From time to time, they may bump up against a problem to which the collective response of humanity is “We don’t know how to do that…yet.”
I am committed to ensuring each student’s access to all course materials, time and attention from me as the instructor both in and out of class, and fair opportunities to demonstrate mastery of the course content. Please contact me if you require any special assistance or accommodations. To avoid any delay in the receipt of your accommodations, you should contact Student Ability Success Center as soon as possible. Please note that accommodations are not retroactive, and that I cannot provide accommodations based upon disability until I have received an accommodation letter from Student Ability Success Center.
I encourage students to reach out by email anytime they need help or have a question. I endeavor to respond to all emails within 24 hours during the work week. Generally I will not be able to respond during the weekend. For questions that require a longer response than a few sentences, please visit me during office hours or schedule a meeting with me. For questions that can be easily answered through a straightforward search online, you may receive a terse reply inviting you to find the answer on your own. (For example: STUDENT: When are your office hours again? ME: You can figure that out without my help. I believe in you!)
Religious Observances: In accordance with the University Policy File, please notify me about planned absences for religious observances by the end of the second week of classes.
Medical-related Absences: Please contact me if you need to miss class, etc. due to an illness, injury or emergency. For the purposes of addressing university policy, documentation may be requested.
SDSU Economic Crisis Response Team: If you or a friend are experiencing food or housing insecurity, or any unforeseen financial crisis, visit sdsu.edu/ecrt, email ecrt@sdsu.edu, or walk-in to Well-being & Health Promotion on the 3rd floor of Calpulli Center.
The Art of R Programming by Norman Matloff
Fundamentals of Data Visualization by Claus O. Wilke
There are many other useful references online. If you find a particularly good one you think others would appreciate, please let us know!