printer friendly pageSTATS 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, correlation, and regression.
Terms: Aut, Win, Spr, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors:
Abdelrahim, S. (PI)
;
Dekhtyar, O. (PI)
;
Fan, J. (PI)
...
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Instructors:
Abdelrahim, S. (PI)
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Dekhtyar, O. (PI)
;
Fan, J. (PI)
;
Schramm, T. (PI)
;
Schwartz, S. (PI)
;
Taylor, J. (PI)
;
Abutto, A. (TA)
;
Anwar, K. (TA)
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Beller, A. (TA)
;
Chen, C. (TA)
;
Chen, Z. (TA)
;
Citrenbaum, C. (TA)
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Davis, L. (TA)
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Ghosh, A. (TA)
;
Hill, H. (TA)
;
Ho, V. (TA)
;
Howes, M. (TA)
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Jonas, C. (TA)
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Klevak, N. (TA)
;
Liang, L. (TA)
;
Ma, G. (TA)
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Morrison, T. (TA)
;
Nicollier Sanchez, C. (TA)
;
Schwartz, S. (TA)
;
Sinai, A. (TA)
;
Tan, A. (TA)
;
Tanoh, I. (TA)
;
Zhang, Q. (TA)
STATS 110: Introduction to Statistics for Engineering and the Sciences
Introduction to statistics with examples drawn from various fields, including the sciences, engineering, and social sciences. Collecting data (random sampling, randomized experiments); describing data (numerical and graphical summaries); discrete and continuous probability models; statistical inference (hypothesis tests and confidence intervals). Use of software to conduct probability simulations and data analysis. Prerequisite:
MATH 20 or AP Calculus AB.
Terms: Aut, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
Instructors:
Sun, D. (PI)
;
Feldmeier, R. (TA)
;
Katiyar, E. (TA)
...
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Instructors:
Sun, D. (PI)
;
Feldmeier, R. (TA)
;
Katiyar, E. (TA)
;
Lee, J. (TA)
;
Shang, J. (TA)
;
Sinai, A. (TA)
;
Sudijono, T. (TA)
STATS 117: Introduction to Probability Theory
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:
MATH 21 or AP Calculus BC.
Terms: Aut, Spr, Sum
| Units: 3
| UG Reqs: WAY-FR, WAY-AQR
Instructors:
Duchi, J. (PI)
;
Kim, G. (PI)
;
Li, S. (PI)
;
Davis, L. (TA)
;
Echarghaoui, A. (TA)
;
Ghandwani, D. (TA)
;
Jeong, R. (TA)
;
Kanekar, R. (TA)
;
Li, H. (TA)
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Liang, L. (TA)
;
Sood, A. (TA)
;
Tung, N. (TA)
;
Zhou, Y. (TA)
STATS 118: Probability Theory for Statistical Inference
Continuation of
STATS 117, with a focus on probability topics useful for statistical inference. 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). Applications to estimation and hypothesis testing. Prerequisites:
Math 51,
MATH 52 (may be taken concurrently), and
STATS 117, or equivalent courses. See
https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Aut, Win, Sum
| Units: 4
| UG Reqs: WAY-AQR, WAY-FR
Instructors:
Kim, G. (PI)
;
Sun, D. (PI)
;
Ho, V. (TA)
;
Kanekar, R. (TA)
;
Krew, J. (TA)
;
Liu, J. (TA)
;
Tanoh, I. (TA)
;
Tung, N. (TA)
;
Vu, V. (TA)
;
Xie, R. (TA)
STATS 141: Introduction to Statistics for Biology (BIO 141)
Statistical methods for biological and medical applications. Collecting data (random sampling, randomized experiments); describing data (numerical and graphical summaries); probability models; statistical inference (hypothesis tests and confidence intervals). Use of software to conduct probability simulations and data analysis. This is an introductory course; students with previous experience in statistics should consider taking
STATS 191 instead.
Terms: Win, Sum
| Units: 5
| UG Reqs: WAY-AQR, GER:DB-Math
STATS 191: Introduction to Applied Statistics
Intermediate statistics course covering statistical models, such as linear regression, analysis of variance, categorical data analysis, and logistic regression. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Prerequisites: Introductory statistics course, such as
STATS 60,
STATS 110,
STATS 141, or 5 on the AP Statistics exam. See
https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Spr, Sum
| Units: 3
| UG Reqs: GER:DB-Math, WAY-AQR
Instructors:
Walther, G. (PI)
;
Ji, W. (TA)
;
Liu, J. (TA)
;
Shang, J. (TA)
;
Shen, R. (TA)
;
Xie, R. (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)
...
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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 Theoretical Statistics
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 118 or equivalent. See
https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Aut, Win, Sum
| Units: 4
Instructors:
Gunawardana, D. (PI)
;
Johnstone, I. (PI)
;
Walther, G. (PI)
...
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Instructors:
Gunawardana, D. (PI)
;
Johnstone, I. (PI)
;
Walther, G. (PI)
;
Cowan, N. (TA)
;
Echarghaoui, A. (TA)
;
Feldmeier, R. (TA)
;
Fry, K. (TA)
;
Ghosh, A. (TA)
;
Lopotenco, A. (TA)
;
Lu, S. (TA)
;
Ma, G. (TA)
;
Park, B. (TA)
;
Spector, A. (TA)
;
Tanoh, I. (TA)
;
Xie, R. (TA)
;
Zhang, Q. (TA)
;
Zhao, S. (TA)
STATS 202: Statistical Learning and Data Science
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). Prerequisites:
STATS 117,
CS 106A,
MATH 51, or equivalent. Recommended:
STATS 191 or
STATS 203. See
https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Aut, Spr, Sum
| Units: 3
Instructors:
Huang, D. (PI)
;
Tran, L. (PI)
;
Walther, G. (PI)
;
Chen, C. (TA)
;
Jeong, Y. (TA)
;
Katiyar, E. (TA)
;
Kazdan, J. (TA)
;
Lopotenco, A. (TA)
;
Sinai, A. (TA)
;
Smith, H. (TA)
;
Wu, S. (TA)
;
Xie, R. (TA)
;
Zhang, I. (TA)
;
Zhang, Q. (TA)
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. Prerequisites: (recommended)
STATS 117,
MATH 51,
MATH 104, or equivalent. See
https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Win, Sum
| Units: 3
Instructors:
Alsmeyer, G. (PI)
;
Palacios, J. (PI)
;
Echarghaoui, A. (TA)
...
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Instructors:
Alsmeyer, G. (PI)
;
Palacios, J. (PI)
;
Echarghaoui, A. (TA)
;
Jeong, R. (TA)
;
Lee, J. (TA)
;
Zhou, Y. (TA)
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