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STATS 191 - Introduction to Applied Statistics

STATS 191 - Introduction to Applied Statistics#

Syllabus#

Instructor & TAs#

Instructors#

Jonathan Taylor

Teaching Assistants & Office Hours#

TA : Rex Shen

  • Email

  • Office hours: TBD

  • Location: TBD

TA : Valerie Ho

  • Email

  • Office hours: TBD

  • Location: TBD

TA : Jing Shang

  • Email

  • Office hours: TBD

  • Location: TBD

TA : Sarah Zhao

  • Email

  • Office hours: TBD

  • Location: TBD

Email list#

The course has an email list that reaches all TAs as well as the professors: stats191-spr2324-staff@lists.stanford.edu

As a general rule, you should send course related to this email list.

Questions can also be posed on gradescope.

Evaluation#

  • 5 assignments (30%)

  • 3 quizzes (30%)

  • final exam (40%) (according to Stanford calendar: F 06/07/24 @ 3:30PM)

Late policy#

  • Late assignments will not be accepted.

  • Lowest of 5 assignment scores will be dropped.

Final exam#

Following the Stanford calendar: Friday, June 7, 2024 @ 3:30PM-6:30PM

Schedule & Location#

MWF 10:30-11:20, Skilling Auditorium

Textbook#

Computing environment#

We will use R for most calculations. Class notes are in the form of jupyter notebooks or RMarkdown files.

RStudio#

Materials presented in class will be available as Quarto files that can be run in RStudio.

Jupyter for R#

In order to run the R notebooks from class, you will need to install Jupyter (easily done through Anaconda as well as enable the R kernel:

install.packages('IRkernel', repos='http://cloud.r-project.org')
library(IRkernel)
IRkernel::installspec()

Prerequisites#

An introductory statistics course, such as STATS 60.

Course description#

By the end of the course, students should be able to:

  • Understand one and two sample t-tests and variants

  • Enter tabular data using R.

  • Plot data using R, to help in exploratory data analysis.

  • Formulate regression models for the data, while understanding some of the limitations and assumptions implicit in using these models.

  • Fit models using R and interpret the output.

  • Test for associations in a given model.

  • Use diagnostic plots and tests to assess the adequacy of a particular model.

  • Find confidence intervals for the effects of different explanatory variables in the model.

  • Use some basic model selection procedures, as found in R, to find a best model in a class of models.

  • Fit simple ANOVA models in R, treating them as special cases of multiple regression models.

  • Fit simple logistic and Poisson regression models.

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