Instructor Information

name: Henry Scharf (you can call me Henry) office: GMCS 518 or virtual
email: office hours: T/Th 2:00–3:00pm or by appt.
(please let me know you’re coming)

Course Information

course number: STAT 410 location: GMCS 421 backup zoom room
semester: Spring 2023 meeting times: T/Th 11:00–12:15pm
mode of delivery: lecture/lab platform: Canvas, Gradescope, Discord (invite)
prerequisites: Statistics 350B

Land Acknowledgement

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.

Course Objectives

In a world in which the price of calculation continues to decrease rapidly, but the price of theorem proving continues to hold steady or increase, elementary economics indicates that we ought to spend a larger and larger fraction of our time on calculation. –-John Tukey, Sunset Salvo (1986)

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.

Student Learning Outcomes

  • Import, munge, summarize, and visualize data in the R statistical software environment.
  • Interpret the mathematical underpinnings of regression models, including model assumptions and inferences.
  • Apply regression models to data in the R statistical software environment.
  • Use RStudio and R Markdown to perform statistical analyses and then interpret and report statistical findings.
  • Approach scientific problems and communicate statistical results from a data scientist perspective.
  • Appreciate the breadth of machine learning approaches in data science applications.
  • Thoughtfully discuss potential challenges to equity, inclusion, and diversity in the practical deployment of machine learning tools used in public policy.


I try to grade assignments as quickly as I can because I think it is most useful for you 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.

Course Materials


  • An Introduction to R [I2R] by Alex Douglas, Deon Roos, Francesca Mancini, Ana Couto & David Lusseau.

    Available free online. The book also has a companion online course.

  • An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani

    A terrific text on modern statistical methods that ranges from regression to deep learning. Available free online.

  • 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.

Immediate Access 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 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.

Course Schedule

Available here, subject to change.

Student Motivation

Motivation to participate in this class needs to come primarily from within. Some of the assessment structures provide minimal external nudging intended to help keep you 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 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 and I will be happy to make a plan with you.

Student Ability Success Center (SASC)

To avoid any delay in the receipt of accommodations arranged through the SASC, you should contact the center as soon as possible. Please note that such accommodations are not retroactive, and that I cannot provide accommodations based upon disability until I have received an accommodation letter from SASC.

Communication with Instructor

I encourage you to reach out by email anytime you 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, email , or walk-in to Well-being & Health Promotion on the 3rd floor of Calpulli Center.

Essential Student Information

For essential information about student academic success, please see the SDSU Student Academic Success Handbook.

Additional Resources