## 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 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: WAY-AQR, GER:DB-Math

## 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 199: Independent Study

For undergraduates.

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit

Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Lai, T. (PI)
;
Mohanty, P. (PI)
;
Rogosa, D. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
;
Wager, S. (PI)
;
Walther, G. (PI)

## STATS 200: Introduction to Statistical Inference

Modern statistical concepts and procedures derived from a mathematical framework. Statistical inference, decision theory; point and interval estimation, tests of hypotheses; Neyman-Pearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite:
STATS 116. Please note that students must enroll in one section in addition to the main lecture.

Terms: Aut, Win
| Units: 4

Instructors:
Johnstone, I. (PI)
;
Romano, J. (PI)
;
Bhattacharya, S. (TA)
...
more instructors for STATS 200 »

Instructors:
Johnstone, I. (PI)
;
Romano, J. (PI)
;
Bhattacharya, S. (TA)
;
Gibbs, I. (TA)
;
Li, S. (TA)
;
Pavlyshyn, D. (TA)
;
Tirlea, M. (TA)
;
Xu, H. (TA)

## STATS 202: Data Mining and Analysis

Data mining is used to discover patterns and relationships in data. Emphasis is on large complex data sets such as those in very large databases or through web mining. Topics: decision trees, association rules, clustering, case based methods, and data visualization. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105).

Terms: Aut, Sum
| Units: 3

Instructors:
Erdmann-Pham, D. (PI)
;
Tran, L. (PI)
;
GAO, Z. (TA)
...
more instructors for STATS 202 »

Instructors:
Erdmann-Pham, D. (PI)
;
Tran, L. (PI)
;
GAO, Z. (TA)
;
Gablenz, P. (TA)
;
Han, K. (TA)
;
Hartog, W. (TA)
;
Hu, A. (TA)
;
Jin, Y. (TA)
;
MacKay, M. (TA)
;
Miao, J. (TA)
;
Paul, D. (TA)
;
Rosenbaum, A. (TA)
;
Tirlea, M. (TA)
;
Wang, X. (TA)
;
Xie, R. (TA)

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