## 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:
Chi, K. (PI)
;
Dekhtyar, O. (PI)
;
Fan, J. (PI)
;
Kim, G. (PI)
;
Lim (Chun Hui), C. (PI)
;
Martinez, J. (PI)
;
Schwartz, S. (PI)
;
Walther, G. (PI)
;
Xiang, V. (PI)
;
Abdelrahim, S. (TA)
;
Dey, A. (TA)
;
Gunawardana, D. (TA)
;
Jeong, Y. (TA)
;
Ji, W. (TA)
;
Kanekar, R. (TA)
;
Kazdan, J. (TA)
;
Lu, S. (TA)
;
Shang, J. (TA)
;
Smith, H. (TA)
;
Song, Z. (TA)
;
Tanoh, I. (TA)
;
Yan, R. (TA)

## STATS 117: Theory of Probability I

Introduction to probability theory, including probability axioms, conditional probability, independence, random variables, and expectation. Joint, marginal, and conditional distributions. Discrete models (binomial, hypergeometric, Poisson) and continuous models (normal, exponential). Prerequisites: Single-variable calculus including infinite series (e.g.,
MATH 21) and at least one MATH course at Stanford. May not be taken for credit by students with credit in
STATS 116,
CS 109,
MATH 151, or MS&E 120.

Terms: Spr, Sum
| Units: 3

Instructors:
Kim, G. (PI)
;
Ho, V. (TA)
;
Krew, J. (TA)
;
Ma, G. (TA)
;
Song, Z. (TA)
;
Tung, N. (TA)
;
Vu, V. (TA)
;
Zhou, Y. (TA)

## STATS 118: Theory of Probability II

Continuation of
STATS 117, with a focus on probability topics useful for statistics. Sampling distributions of sums, means, variances, and order statistics of random variables. Convolutions, moment generating functions, and limit theorems. Probability distributions useful in statistics (gamma, beta, chi-square, t, multivariate normal). Prerequisites: a calculus-based first course in probability (such as
STATS 117,
CS 109, or MS&E 120) and multivariable calculus, including multiple integrals (
MATH 52 or equivalent, can be taken concurrently). May not be taken for credit by students with credit in
STATS 116.

Terms: Sum
| Units: 4

Instructors:
Hwang, J. (PI)
;
Cheng, C. (TA)

## 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:
Chi, K. (PI)
;
Dekhtyar, O. (PI)
;
Fan, J. (PI)
;
Kim, G. (PI)
;
Lim (Chun Hui), C. (PI)
;
Martinez, J. (PI)
;
Schwartz, S. (PI)
;
Walther, G. (PI)
;
Xiang, V. (PI)
;
Abdelrahim, S. (TA)
;
Dey, A. (TA)
;
Gunawardana, D. (TA)
;
Jeong, Y. (TA)
;
Ji, W. (TA)
;
Kanekar, R. (TA)
;
Kazdan, J. (TA)
;
Lu, S. (TA)
;
Shang, J. (TA)
;
Smith, H. (TA)
;
Song, Z. (TA)
;
Tanoh, I. (TA)
;
Yan, R. (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: Spr, Sum
| Units: 3
| UG Reqs: GER:DB-Math, WAY-AQR

Instructors:
DiCiccio, C. (PI)
;
Taylor, J. (PI)
;
Ho, V. (TA)
;
Kanekar, R. (TA)
;
Li, P. (TA)
;
Shang, J. (TA)
;
Shen, R. (TA)
;
Vu, V. (TA)
;
Zhao, S. (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)
;
Palacios, J. (PI)
...
more instructors for STATS 199 »

Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Palacios, J. (PI)
;
Sabatti, C. (PI)
;
Schramm, T. (PI)
;
Taylor, J. (PI)
;
Tibshirani, R. (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, Sum
| Units: 4

Instructors:
Hwang, J. (PI)
;
Johnstone, I. (PI)
;
Palacios, J. (PI)
...
more instructors for STATS 200 »

Instructors:
Hwang, J. (PI)
;
Johnstone, I. (PI)
;
Palacios, J. (PI)
;
Cherian, J. (TA)
;
Jang, J. (TA)
;
Jeong, Y. (TA)
;
Kanekar, R. (TA)
;
Lee, J. (TA)
;
Liang, L. (TA)
;
Lu, S. (TA)
;
Ma, G. (TA)
;
Salerno, M. (TA)
;
Tanoh, I. (TA)
;
Xie, R. (TA)
;
Zhang, I. (TA)
;
Zhao, S. (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). May not be taken for credit by students with credit in
STATS 216 or 216V.

Terms: Aut, Sum
| Units: 3

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

Instructors:
Erdmann-Pham, D. (PI)
;
Tran, L. (PI)
;
Chen, Z. (TA)
;
Gonzalez, X. (TA)
;
Hu, A. (TA)
;
Jang, J. (TA)
;
Ji, W. (TA)
;
Song, Z. (TA)
;
Zhang, I. (TA)
;
Zhang, J. (TA)

## STATS 216V: Introduction to Statistical Learning

Overview of supervised learning, with a focus on regression and classification methods. Syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines; Some unsupervised learning: principal components and clustering (k-means and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This math-light course is offered remotely only via video segments (MOOC style). TAs will host remote weekly office hours using an online platform such as Zoom. There are four homework assignments, a midterm, and a final exam, all of which are administered remotely. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60 or
Stats 101), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105). May not be taken for credit by students with credit in
STATS 202 or
STATS 216.

Terms: Sum
| Units: 3

## STATS 217: Introduction to Stochastic Processes I

Discrete and continuous time Markov chains, poisson processes, random walks, branching processes, first passage times, recurrence and transience, stationary distributions. Non-Statistics masters students may want to consider taking
STATS 215 instead. Prerequisite: a post-calculus introductory probability course e.g.
STATS 116

Terms: Win, Sum
| Units: 3

Instructors:
Hartog, W. (PI)
;
Palacios, J. (PI)
;
Ghandwani, D. (TA)
...
more instructors for STATS 217 »

Instructors:
Hartog, W. (PI)
;
Palacios, J. (PI)
;
Ghandwani, D. (TA)
;
Gunawardana, D. (TA)
;
Katiyar, E. (TA)
;
Yang, J. (TA)

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