## STATS 32: Introduction to R for Undergraduates

This short course runs for weeks one through five of the quarter. It is recommended for undergraduate students who want to use R in the humanities or social sciences and for students who want to learn the basics of R programming. The goal of the short course is to familiarize students with R's tools for data analysis. Lectures will be interactive with a focus on learning by example, and assignments will be application-driven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, data transformation and visualization, simple statistical tests, etc, and some useful packages in R. Prerequisite: undergraduate student. Priority given to non-engineering students. Laptops necessary for use in class.

Terms: Aut, Spr
| Units: 1

Instructors:
Li, H. (PI)
;
Tuzhilina, E. (PI)

## STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.

Terms: Aut, Win, Spr, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

Instructors:
Auelua-Toomey, S. (PI)
;
Erdmann-Pham, D. (PI)
;
Jain, V. (PI)
...
more instructors for STATS 60 »

Instructors:
Auelua-Toomey, S. (PI)
;
Erdmann-Pham, D. (PI)
;
Jain, V. (PI)
;
Kong, N. (PI)
;
Poldrack, R. (PI)
;
Walters, J. (PI)
;
Harrison, M. (TA)
;
Howes, M. (TA)
;
Kanekar, R. (TA)
;
Kirshenbaum, J. (TA)
;
Liu, S. (TA)
;
Pan, Z. (TA)
;
Tanoh, I. (TA)

## STATS 100: Mathematics of Sports

This course will teach you how statistics and probability can be applied in sports, in order to evaluate team and individual performance, build optimal in-game strategies and ensure fairness between participants. Topics will include examples drawn from multiple sports such as basketball, baseball, soccer, football and tennis. The course is intended to focus on data-based applications, and will involve computations in R with real data sets via tutorial sessions and homework assignments. Prereqs: No statistical or programming background is assumed, but introductory courses, e.g,
Stats 60,101 or 116, are recommended. A prior knowledge of Linear Algebra (e.g.,
Math 51) and basic probability is strongly recommended.

Terms: Win
| Units: 3
| UG Reqs: GER:DB-Math

Instructors:
Gonzalez, X. (PI)

## STATS 101: Data Science 101

https://statweb.stanford.edu/~tibs/stat101.html This course will provide a hands-on introduction to statistics and data science. Students will engage with the fundamental ideas in inferential and computational thinking. Each week, we will explore a core topic comprising three lectures and two labs (a module), in which students will manipulate real-world data and learn about statistical and computational tools. Students will engage in statistical computing and visualization with current data analytic software (Jupyter, R). The objectives of this course are to have students (1) be able to connect data to underlying phenomena and to think critically about conclusions drawn from data analysis, and (2) be knowledgeable about programming abstractions so that they can later design their own computational inferential procedures. No programming or statistical background is assumed. Freshmen and sophomores interested in data science, computing and statistics are encouraged to attend. Open to graduates as well.

Terms: Spr
| Units: 5
| UG Reqs: GER: DB-NatSci, WAY-AQR

Instructors:
Sklar, M. (PI)

## STATS 110: Statistical Methods in Engineering and the Physical Sciences

Introduction to statistics for engineers and physical scientists. Topics: descriptive statistics, probability, interval estimation, tests of hypotheses, nonparametric methods, linear regression, analysis of variance, elementary experimental design. Prerequisite: one year of calculus. Please note that students must enroll in one section in addition to the main lecture.

Terms: Aut, Spr
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

## STATS 116: Theory of Probability

Probability spaces as models for phenomena with statistical regularity. Discrete spaces (binomial, hypergeometric, Poisson). Continuous spaces (normal, exponential) and densities. Random variables, expectation, independence, conditional probability. Introduction to the laws of large numbers and central limit theorem. Prerequisites:
MATH 52 and familiarity with infinite series, or equivalent. Please note that students must enroll in one section in addition to the main lecture.

Terms: Aut, Spr, Sum
| Units: 4
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

Instructors:
Duchi, J. (PI)
;
Palacios, J. (PI)
;
Dey, A. (TA)
;
Ghandwani, D. (TA)
;
Zhong, C. (TA)

## STATS 141: Biostatistics (BIO 141)

Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.

Terms: Aut
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR

## STATS 155: Modern Statistics for Modern Biology (BIOS 221, STATS 256, STATS 366)

Application based course in nonparametric statistics. Modern toolbox of visualization and statistical methods for the analysis of data, examples drawn from immunology, microbiology, cancer research and ecology. Methods covered include multivariate methods (PCA and extensions), sparse representations (trees, networks, contingency tables) as well as nonparametric testing (Bootstrap, permutation and Monte Carlo methods). Hands on, use R and cover many Bioconductor packages. Prerequisite: Working knowledge of R and two core Biology courses. Note that the 155 offering is a writing intensive course for undergraduates only and requires instructor consent. (WIM). See
https://web.stanford.edu/class/bios221/index.html

Terms: Aut
| Units: 3

## STATS 160: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 60)

Techniques for organizing data, computing, and interpreting measures of central tendency, variability, and association. Estimation, confidence intervals, tests of hypotheses, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.

Terms: Aut, Win, Spr, Sum
| Units: 5

Instructors:
Auelua-Toomey, S. (PI)
;
Erdmann-Pham, D. (PI)
;
Harrison, M. (PI)
...
more instructors for STATS 160 »

Instructors:
Auelua-Toomey, S. (PI)
;
Erdmann-Pham, D. (PI)
;
Harrison, M. (PI)
;
Jain, V. (PI)
;
Kirshenbaum, J. (PI)
;
Kong, N. (PI)
;
Poldrack, R. (PI)
;
Walters, J. (PI)
;
Howes, M. (TA)
;
Kanekar, R. (TA)
;
Liu, S. (TA)
;
Pan, Z. (TA)
;
Tanoh, I. (TA)

## STATS 191: Introduction to Applied Statistics

Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Prerequisite: introductory statistical methods course. Recommended: 60, 110, or 141.

Terms: Win
| Units: 3
| UG Reqs: GER:DB-Math, WAY-AQR

Instructors:
Walther, G. (PI)

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