printer friendly pageSTATS 50: Mathematics of Sports (MCS 100)
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
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Units: 3
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UG Reqs: GER:DB-Math
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Grading: Letter or Credit/No Credit
Instructors:
Cauchois, M. (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
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Units: 5
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UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
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Grading: Letter or Credit/No Credit
Instructors:
Baiocchi, M. (PI)
;
Miolane, N. (PI)
;
Poldrack, R. (PI)
...
more instructors for STATS 60 »
Instructors:
Baiocchi, M. (PI)
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Miolane, N. (PI)
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Poldrack, R. (PI)
;
Xia, L. (PI)
;
Cao, S. (TA)
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Lemhadri, I. (TA)
;
Ruan, F. (TA)
STATS 101: Data Science 101
http://web.stanford.edu/class/stats101/ . This course will provide a hands-on 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 real-world 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
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Units: 5
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UG Reqs: GER: DB-NatSci, WAY-AQR
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Grading: Letter or Credit/No Credit
Instructors:
Duchi, J. (PI)
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Johndrow, J. (PI)
;
Walther, G. (PI)
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Instructors:
Duchi, J. (PI)
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Johndrow, J. (PI)
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Walther, G. (PI)
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Ghosh, S. (TA)
;
Kluger, D. (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
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Units: 3-5
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UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
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Grading: Letter or Credit/No Credit
Instructors:
Kaluwa Devage, P. (PI)
;
Mohanty, P. (PI)
;
Siegmund, D. (PI)
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Instructors:
Kaluwa Devage, P. (PI)
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Mohanty, P. (PI)
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Siegmund, D. (PI)
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Zhu, X. (PI)
;
Bi, N. (TA)
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Misiakiewicz, T. (TA)
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SUR, P. (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
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Units: 5
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Grading: Letter or Credit/No Credit
Instructors:
Baiocchi, M. (PI)
;
Miolane, N. (PI)
;
Poldrack, R. (PI)
...
more instructors for STATS 160 »
Instructors:
Baiocchi, M. (PI)
;
Miolane, N. (PI)
;
Poldrack, R. (PI)
;
Xia, L. (PI)
;
Cao, S. (TA)
;
Lemhadri, I. (TA)
;
Ruan, F. (TA)
STATS 195: Introduction to R (CME 195)
This short course runs for four weeks 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 data science with R. The goal of the short course is to familiarize students with some of the most important R tools for data analysis. Lectures will focus on learning by example and assignments will be application-driven. No prior programming experience is assumed.
Terms: Aut, Spr
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Units: 1
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Grading: Satisfactory/No Credit
Instructors:
Nguyen, L. (PI)
;
Rosenman, E. (PI)
STATS 196A: Multilevel Modeling Using R (EDUC 401D)
See
http://rogosateaching.com/stat196/ . Multilevel data analysis examples using R. Topics include: two-level nested data, growth curve modeling, generalized linear models for counts and categorical data, nonlinear models, three-level analyses. Class meets April 10, April 17, April 24, May 1, May 15.
Terms: Spr
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Units: 1
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Grading: Satisfactory/No Credit
Instructors:
Rogosa, D. (PI)
STATS 199: Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum
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Units: 1-15
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Repeatable for credit
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Grading: Satisfactory/No Credit
Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
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more instructors for STATS 199 »
Instructors:
Baiocchi, M. (PI)
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Duchi, J. (PI)
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Efron, B. (PI)
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Rogosa, D. (PI)
;
Sabatti, C. (PI)
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Taylor, J. (PI)
;
Wager, S. (PI)
STATS 203: Introduction to 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. Pre- or corequisite: 200.
Terms: Spr
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Units: 3
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Grading: Letter or Credit/No Credit
Instructors:
Johnstone, I. (PI)
STATS 204: Sampling
How best to take data and where to sample it. Examples include surveys and sampling from data warehouses. Emphasis is on methods for finite populations. Topics: simple random sampling, stratified sampling, cluster sampling, ratio and regression estimators, two stage sampling.
Terms: Spr
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Units: 3
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Grading: Letter or Credit/No Credit
Instructors:
Diaconis, P. (PI)
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