Feedback: We welcome student feedback regarding the course at any point. Please feel free to email us directly, or leave anonymous feedback for the instructional team by using our online form.
Rationale: It is impossible to understand the modern world without an understanding of statistics. From public opinion polls to clinical trials in medicine to online systems that recommend purchases to us, statistics play a role in nearly every aspect of our lives. The goal of this course is to provide an understanding of the essential concepts and “big ideas” of statistics — describing data and making decisions and predictions based on data – as well as the skills to employ these concepts on real data to solve authentic problems. At the end of the course, students will possess:
Instructional philosophy. My instructional philosophy is based on the overwhelming scientific evidence that the standard instructional model (a lecturer talking at students for a long period) is an ineffective way to instill lasting skills and knowledge. Instead, I will aim for the course to be:
Statistics is a broad field, and the goal of this course is not “coverage” of the entire field or simple familiarity with the concepts. Instead, we will work towards a deep understanding and ability to apply a set of core concepts, and we will actively adjust the course based on the results from our daily assessments.
Prerequisites: Students should have a solid understanding of high school mathematics (specifically algebra). The course will also assume facility with using the internet and a personal computer. A portion of the course involves programming in R, but no prior programming experience is required.
Course Requirements: You will need a laptop computer for use in every class. If you do not have access to a laptop please contact the instructor ASAP and we will help you obtain access to one.
Lectures: We will meet three times per week:
We will be using Stanford Zoom to interact during class. The Zoom links are available on the calendar. A working Stanford SUNet account is required for access.
Website: The primary web site for the class is stats60.stanford.edu. We will also use Canvas for class lecture recordings.
Discussion. We will Piazza to allow you to get help efficiently from both your classmates and the instructors (class Piazza page). Please post your questions about the course material and course logistics to Piazza so that everyone can benefit from the answer. We also highly encourage you to answer your classmates’ questions whenever possible – you will get extra practice with the material and receive feedback from the teaching team about your answers. As an added incentive, students who provide frequent, high-quality answers may receive extra credit on their final course grades. Additional guidelines about posting on Piazza are available on the Piazza page.
Materials: The main two resources for this class are:
We will supplement these textbooks with other free online resources.
We will use the R programming language, and the RStudio software. You will need to download and install R and the RStudio software directly on your personal computer. Here is a tutorial to download and install R and Rstudio. Thanks and acknowledgments to Kenneth Tay for providing the tutorial.
Lecture slides and video recordings will be available on Canvas. They are not intended to replace attendance, since much of the class activity will go beyond the slides. But they will provide some additional flexibility if you can’t make the lecture at the scheduled time.
The VPTL office offers tutoring for this class by appointment, which can be scheduled here.
Grades will be determined from problem sets, quizzes and a final project. There will be a total of 4 problem sets, worth 50% of your grade. Each problem set will count equally towards the final grade, based on a percentage of available points. The project will count towards 40% of your grade. And the quizzes will constitute the remaining 10%.
There is no midterm or final examination for this class: the scheduled final exam time will be used for a regular lecture. Unless otherwise stated, you can use any published resource you wish to complete the assessments (textbook, Internet, etc). However, you should not discuss the answers with your fellow students in person or electronically unless instructed to do so by the instructors; sharing answers (including computer code) will be viewed as a violation of the Honor Code.
Assignments: You will be given 4 problem sets to complete. Unless otherwise noted, these will be due at 5PM PDT on Fridays, submitted via Gradescope. If you do not see “STATS60 / Summer 2020” on your Gradescope account, you can self-enroll using the entry code 9EWEZR.
R Labs: The Wednesday session is dedicated to problem sessions and programming labs. Live attendance is highly recommended as it will give you a chance to interact with the section leader. The tools and techniques taught in labs will be essential for your final project.
Quizzes: You will complete a series of online activities to assess your learning of the important concepts and skills for the previous week. These will be announced in due time and will be due a week after they are released.
Grade disputes: Students must wait 24 hours after receiving a grade before they can dispute it, after which disputes must be received within 7 days of receipt of the grade. Grade disputes must be submitted to the instructional team on Gradescope.
Gender expression/identity: This course affirms people of all gender expressions and gender identities. If you prefer to be called a different name than what is indicated on the class roster, please let me know. Feel free to correct me on your preferred gender pronoun. If you have any questions or concerns, please do not hesitate to contact me.
Code of conduct: You are expected to treat the instructional team and your fellow students with courtesy and respect. This class should be a harassment-free learning experience for everyone regardless of gender, gender identity and expression, sexual orientation, disability, physical appearance, body size, race, age or religion. Harassment of any form will not be tolerated. If someone makes you or anyone else feel unsafe or unwelcome, please report it as soon as possible to one of the instructors. If you are not comfortable approaching the instructional team, you may also contact the Stanford Office of the Ombuds
Students with Documented Disabilities: If you have an OAE letter, please present it to us (by email to the staff llist) at your earliest convenience, so we can ensure that the course materials and staff support comply with your needs.
Financial accessibility Stanford University and its instructors are committed to ensuring that all courses are financially accessible to all students. If you are an undergraduate who needs assistance with the cost of course textbooks, supplies, materials and/or fees, you are welcome to approach me directly. If would prefer not to approach me directly, please note that you can ask the Diversity & First-Gen Office for assistance by completing their questionnaire on course textbooks & supplies or by contacting Joseph Brown, the Associate Director of the Diversity and First-Gen Office (jlbrown@stanford.edu). Dr. Brown is available to connect you with resources and support while ensuring your privacy.
In every quarter we have taught, there have been individual students who have encountered life-altering challenges, so it is not the case that empathy and compassion have only just become relevant. However, the magnitude of the current crisis underscores the need to support each other. If you feel overwhelmed for any reason—by work for this class, or a family issue, or just the weight of the present moment for the globe, please don’t hesitate to reach out. Or, if you just need to talk, or have us send you a kitten video, or if you need extra tutoring support in the class, we are here for you. Please ask us. Please ask us.
This course would not be possible without the help of many dedicated teachers and colleagues. I will borrow heavily from Russ Poldrack’s material for the lectures, and from Kenneth Tay’s material for the labs.