STATS 50: Mathematics of Sports (MCS 100)
The use of mathematics, statistics, and probability in the analysis of sports performance, sports records, and strategy. Topics include mathematical analysis of the physics of sports and the determinations of optimal strategies. New diagnostic statistics and strategies for each sport. Corequisite:
STATS 60, 110 or 116.
Terms: Spr

Units: 3

UG Reqs: GER:DBMath

Grading: Letter or Credit/No Credit
Instructors:
DiCiccio, C. (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, ttests, correlation, and regression. Possible topics: analysis of variance and chisquare tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum

Units: 5

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
DiCiccio, C. (PI)
;
Khazenzon, A. (PI)
;
Leong, Y. (PI)
...
more instructors for STATS 60 »
Instructors:
DiCiccio, C. (PI)
;
Khazenzon, A. (PI)
;
Leong, Y. (PI)
;
Poldrack, R. (PI)
;
Schwartz, J. (PI)
;
Xia, L. (PI)
;
bonnen, t. (PI)
;
ten Brink, M. (PI)
;
Cao, S. (TA)
;
Panigrahi, S. (TA)
;
Sesia, M. (TA)
STATS 101: Data Science 101
http://web.stanford.edu/class/stats101/ . This course will provide a handson 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 realworld 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: Aut, Spr, Sum

Units: 5

UG Reqs: GER: DBNatSci, WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Mohanty, P. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
...
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Instructors:
Mohanty, P. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
;
Walther, G. (PI)
;
Xia, L. (PI)
;
Du, W. (TA)
;
Misiakiewicz, T. (TA)
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.
Terms: Aut, Spr, Sum

Units: 35

UG Reqs: GER:DBMath, WAYAQR, WAYFR

Grading: Letter or Credit/No Credit
Instructors:
Donoho, D. (PI)
;
Kaluwa Devage, P. (PI)
;
Zhang, Y. (PI)
...
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Instructors:
Donoho, D. (PI)
;
Kaluwa Devage, P. (PI)
;
Zhang, Y. (PI)
;
Cauchois, M. (TA)
;
SUR, P. (TA)
;
YAN, J. (TA)
;
YANG, J. (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, ttests, correlation, and regression. Possible topics: analysis of variance and chisquare tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum

Units: 5

Grading: Letter or Credit/No Credit
Instructors:
DiCiccio, C. (PI)
;
Poldrack, R. (PI)
;
Xia, L. (PI)
...
more instructors for STATS 160 »
Instructors:
DiCiccio, C. (PI)
;
Poldrack, R. (PI)
;
Xia, L. (PI)
;
bonnen, t. (PI)
;
Cao, S. (TA)
;
Panigrahi, S. (TA)
;
Sesia, M. (TA)
STATS 195: Introduction to R (CME 195)
This short course runs for four weeks beginning in the second week of the quarter and is offered in fall and spring. It is recommended for students who want to use R in statistics, science, or engineering courses 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 scientific computing. Lectures will be interactive with a focus on learning by example, and assignments will be applicationdriven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, graphs, control structures, etc, and some useful packages in R.
Terms: Aut, Spr

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Nguyen, L. (PI)
;
Sesia, M. (PI)
STATS 196A: Multilevel Modeling Using R (EDUC 401D)
See
http://rogosateaching.com/stat196/ . Multilevel data analysis examples using R. Topics include: twolevel nested data, growth curve modeling, generalized linear models for counts and categorical data, nonlinear models, threelevel analyses. Class meets April 11, April 18, April 25, May 2, May 16.
Terms: Spr

Units: 1

Grading: Satisfactory/No Credit
Instructors:
Rogosa, D. (PI)
STATS 199: Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum

Units: 115

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors:
Baiocchi, M. (PI)
;
Candes, E. (PI)
;
Dembo, A. (PI)
...
more instructors for STATS 199 »
Instructors:
Baiocchi, M. (PI)
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Candes, E. (PI)
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Dembo, A. (PI)
;
Diaconis, P. (PI)
;
Donoho, D. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
;
Friedman, J. (PI)
;
Hastie, T. (PI)
;
Holmes, S. (PI)
;
Jackman, S. (PI)
;
Johnstone, I. (PI)
;
Lai, T. (PI)
;
Mackey, L. (PI)
;
Montanari, A. (PI)
;
Mukherjee, R. (PI)
;
Owen, A. (PI)
;
Palacios, J. (PI)
;
Rajaratnam, B. (PI)
;
Rogosa, D. (PI)
;
Romano, J. (PI)
;
Sabatti, C. (PI)
;
Siegmund, D. (PI)
;
Switzer, P. (PI)
;
Taylor, J. (PI)
;
Tibshirani, R. (PI)
;
Walther, G. (PI)
;
Wong, W. (PI)
STATS 208: Introduction to the Bootstrap
The bootstrap is a computerbased method for assigning measures of accuracy to statistical estimates. By substituting computation in place of mathematical formulas, it permits the statistical analysis of complicated estimators. Topics: nonparametric assessment of standard errors, biases, and confidence intervals; related resampling methods including the jackknife, crossvalidation, and permutation tests. Theory and applications. Prerequisite: course in statistics or probability.
Terms: Spr

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Donoho, D. (PI)
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