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, t-tests, correlation, and regression. Possible topics: analysis of variance and chi-square tests, computer statistical packages.
Terms: Aut, Win, Spr, Sum
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR
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
| Units: 5
STATS 191: Introduction to Applied Statistics
Statistical tools for modern data analysis. Topics include regression and prediction, elements of the analysis of variance, bootstrap, and cross-validation. Emphasis is on conceptual rather than theoretical understanding. Applications to social/biological sciences. Student assignments/projects require use of the software package R. Recommended: 60, 110, or 141.
Terms: Win
| Units: 3-4
| UG Reqs: GER:DB-Math, WAY-AQR
Instructors:
Taylor, J. (PI)
STATS 198: Practical Training
For students majoring in Mathematical and Computational Science only. Students obtain employment in a relevant industrial or research activity to enhance their professional experience.
Terms: Aut, Win, Spr, Sum
| Units: 1-3
| Repeatable
2 times
(up to 6 units total)
Instructors:
Efron, B. (PI)
;
Holmes, S. (PI)
;
Rogosa, D. (PI)
...
more instructors for STATS 198 »
Instructors:
Efron, B. (PI)
;
Holmes, S. (PI)
;
Rogosa, D. (PI)
;
Sabatti, C. (PI)
;
Siegmund, D. (PI)
;
Taylor, J. (PI)
STATS 199: Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable
for credit
Instructors:
Candes, E. (PI)
;
Dembo, A. (PI)
;
Diaconis, P. (PI)
...
more instructors for STATS 199 »
Instructors:
Candes, E. (PI)
;
Dembo, A. (PI)
;
Diaconis, P. (PI)
;
Donoho, D. (PI)
;
Efron, B. (PI)
;
Friedman, J. (PI)
;
Hastie, T. (PI)
;
Holmes, S. (PI)
;
Johnstone, I. (PI)
;
Lai, T. (PI)
;
Lemley, M. (PI)
;
Montanari, A. (PI)
;
Olkin, I. (PI)
;
Olshen, R. (PI)
;
Owen, A. (PI)
;
Rajaratnam, B. (PI)
;
Rogosa, D. (PI)
;
Romano, J. (PI)
;
Siegmund, D. (PI)
;
Switzer, P. (PI)
;
Taylor, J. (PI)
;
Tibshirani, R. (PI)
;
Walther, G. (PI)
;
Wong, W. (PI)
;
Zhang, N. (PI)
STATS 200: Introduction to Statistical Inference
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: 116.
Terms: Win, Sum
| Units: 3
Instructors:
Sparks, D. (PI)
STATS 209: Statistical Methods for Group Comparisons and Causal Inference (EDUC 260X, HRP 239)
Critical examination of statistical methods in social science applications, especially for cause and effect determinations. Topics: path analysis, multilevel models, matching and propensity score methods, analysis of covariance, instrumental variables, compliance, longitudinal data, mediating and moderating variables. See
http://www-stat.stanford.edu/~rag/stat209. Prerequisite: intermediate-level statistical methods
Terms: Win
| Units: 3
Instructors:
Rogosa, D. (PI)
STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (HRP 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)
;
Olkin, I. (PI)
STATS 213: Introduction to Graphical Models (STATS 313)
Multivariate Normal Distribution and Inference, Wishart distributions, graph theory, probabilistic Markov models, pairwise and global Markov property, decomposable graph, Markov equivalence, MLE for DAG models and undirected graphical models, Bayesian inference for DAG models and undirected graphical models. Prerequisites:
STATS 116 (preferably
STATS 217),
STATS 200 (preferably
STATS 300A),
MATH 104 or equivalent class in linear algebra.
Terms: Win
| Units: 3
Instructors:
Rajaratnam, B. (PI)
STATS 216: Introduction to Statistical Learning
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). Computing is done in R, through tutorial sessions and homework assignments. This math-light course is offered via video segments (MOOC style), and in-class problem solving sessions. Prerequisites: first courses in statistics, linear algebra, and computing.
Terms: Win
| Units: 3
Instructors:
Hastie, T. (PI)
;
Tibshirani, R. (SI)
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