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)
...
more instructors for STATS 60 »
Instructors:
Abdelrahim, S. (PI)
;
Dekhtyar, O. (PI)
;
Fan, J. (PI)
;
Schramm, T. (PI)
;
Schwartz, S. (PI)
;
Taylor, J. (PI)
;
Abutto, A. (TA)
;
Anwar, K. (TA)
;
Beller, A. (TA)
;
Chen, C. (TA)
;
Chen, Z. (TA)
;
Hill, H. (TA)
;
Jonas, C. (TA)
;
Klevak, N. (TA)
;
Morrison, T. (TA)
;
Nicollier Sanchez, C. (TA)
;
Schwartz, S. (TA)
;
Sinai, A. (TA)
;
Tan, A. (TA)
;
Zhang, Q. (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: Spr
| Units: 3
| UG Reqs: WAY-AQR, GER:DB-Math
Instructors:
Kim, G. (PI)
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)
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, correlation, and regression.
Terms: Aut, Win, Spr
| Units: 5
Instructors:
Abdelrahim, S. (PI)
;
Fan, J. (PI)
;
Schramm, T. (PI)
...
more instructors for STATS 160 »
Instructors:
Abdelrahim, S. (PI)
;
Fan, J. (PI)
;
Schramm, T. (PI)
;
Schwartz, S. (PI)
;
Taylor, J. (PI)
;
Abutto, A. (TA)
;
Anwar, K. (TA)
;
Beller, A. (TA)
;
Chen, C. (TA)
;
Chen, Z. (TA)
;
Hill, H. (TA)
;
Jonas, C. (TA)
;
Klevak, N. (TA)
;
Morrison, T. (TA)
;
Nicollier Sanchez, C. (TA)
;
Schwartz, S. (TA)
;
Sinai, A. (TA)
;
Tan, A. (TA)
;
Zhang, Q. (TA)
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)
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 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:
Tran, L. (PI)
;
Walther, G. (PI)
;
Chen, C. (TA)
;
Jeong, Y. (TA)
;
Sinai, A. (TA)
;
Smith, H. (TA)
;
Wu, S. (TA)
;
Zhang, Q. (TA)
STATS 203: Regression Models and Analysis of Variance
Modeling and interpretation of observational and experimental data using linear and nonlinear regression methods. Model building and selection methods. Multivariable analysis. Fixed and random effects models. Experimental design. Prerequisites:
Math 51,
Math 104,
STATS 200. See
https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Aut, Spr
| Units: 3
STATS 206: Applied Multivariate Analysis (BIODS 206)
Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts, computer-intensive methods. Examples from economics, education, geology, psychology. Topics: multiple regression, multivariate analysis of variance, principal components, factor analysis, canonical correlations, multidimensional scaling, clustering. Prerequisites:
STATS 116/118,
STATS 191/203,
MATH104 (recommended). See
https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Spr
| Units: 3
Instructors:
Owen, A. (PI)
STATS 208: Resampling Methods: Bootstrap, Cross Validation and Beyond
In this course, we discuss creative and impactful ways to reuse the same dataset multiple times, including techniques such as extracting random or systematic subsets, resampling, shuffling, and introducing noise to the original dataset. These can be used in many tasks, from calculating confidence intervals and p-values, to finding the influential data points within a dataset, to constructing improved models, and choosing parameters we don't know how to specify a priori. By the end of the course, the students will understand classical terms like bootstrap, cross validation, and permutation tests, as well as more recent terms like random forests, bagging, data models, and model collapse.
Terms: Spr
| Units: 3
Instructors:
Dey, A. (PI)
;
Donoho, D. (PI)
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