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
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
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
| Units: 4
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
| Units: 5
| UG Reqs: WAY-AQR, GER:DB-Math
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
STATS 195: Introduction to R
This short course runs for four weeks. 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)
STATS 199: Independent Study
For undergraduates.
Terms: Aut, Win, Spr
| 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 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. See
https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites. Please note that students must enroll in one section in addition to the main lecture.
Terms: Aut, Win
| Units: 4
Instructors:
Johnstone, I. (PI)
;
Walther, G. (PI)
;
Cowan, N. (TA)
...
more instructors for STATS 200 »
Instructors:
Johnstone, I. (PI)
;
Walther, G. (PI)
;
Cowan, N. (TA)
;
Fry, K. (TA)
;
Ghosh, A. (TA)
;
Ma, G. (TA)
;
Spector, A. (TA)
;
Zhao, S. (TA)
STATS 202F: Statistical Learning and Data Science [Flipped]
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. Recommended:
STATS 191 or
STATS 203. This class is taught in flipped format, where students watch videos at home and work on assignments during class.
Terms: Win
| Units: 3
Instructors:
Tibshirani, R. (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.Prerequisites:
STATS 191 or
STATS 203,
STATS 200. See
https://statistics.stanford.edu/course-equiv for equivalent courses in other departments that satisfy these prerequisites.
Terms: Win
| Units: 3
STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (CHPR 206, EPI 206, MED 206)
Open to graduate, medical, and undergraduate students. Appraisal of the quality and credibility of research findings; evaluation of sources of bias. Meta-analysis as a quantitative (statistical) method for combining results of independent studies. Examples from medicine, epidemiology, genomics, ecology, social/behavioral sciences, education. Collaborative analyses. Project involving generation of a meta-research project or reworking and evaluation of an existing published meta-analysis. Prerequisite: knowledge of basic statistics.
Terms: Win
| Units: 3
Instructors:
Ioannidis, J. (PI)
;
Kvarven, A. (PI)
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