STATS 32: Introduction to R for Undergraduates
This short course runs for weeks one through five of the quarter. It is recommended for undergraduate students who want to use R in the humanities or social sciences 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 data analysis. Lectures will be interactive with a focus on learning by example, and assignments will be application-driven. No prior programming experience is needed. Topics covered include basic data structures, File I/O, data transformation and visualization, simple statistical tests, etc, and some useful packages in R. Prerequisite: undergraduate student. Priority given to non-engineering students. Laptops necessary for use in class.
Terms: Aut
| Units: 1
Instructors:
Zhang, I. (PI)
;
Citrenbaum, C. (TA)
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, 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 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)
;
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)
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),
STATS 117. See
https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Aut, Win, Sum
| Units: 4
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)
;
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: GER:DB-Math, WAY-AQR
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 195: Introduction to R
This short course runs for four weeks (weeks 2-5 of quarter). 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: Win
| Units: 1
Instructors:
Zhang, I. (PI)
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