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)
;
Poldrack, R. (PI)
;
Xia, L. (PI)
...
more instructors for STATS 60 »
Instructors:
DiCiccio, C. (PI)
;
Poldrack, R. (PI)
;
Xia, L. (PI)
;
Cao, S. (TA)
;
Panigrahi, S. (TA)
;
Sesia, M. (TA)
STATS 141: Biostatistics (BIO 141)
Introductory statistical methods for biological data: describing data (numerical and graphical summaries); introduction to probability; and statistical inference (hypothesis tests and confidence intervals). Intermediate statistical methods: comparing groups (analysis of variance); analyzing associations (linear and logistic regression); and methods for categorical data (contingency tables and odds ratio). Course content integrated with statistical computing in R.
Terms: Win

Units: 35

UG Reqs: GER:DBMath, WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Zhu, X. (PI)
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)
;
Cao, S. (TA)
;
Panigrahi, S. (TA)
;
Sesia, M. (TA)
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 crossvalidation. 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: 34

UG Reqs: GER:DBMath, WAYAQR

Grading: Letter or Credit/No Credit
Instructors:
Walther, G. (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)
;
Candes, E. (PI)
;
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 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; NeymanPearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: 116.
Terms: Aut, Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Romano, J. (PI)
;
Sabatti, C. (PI)
;
Bhattacharya, S. (TA)
...
more instructors for STATS 200 »
Instructors:
Romano, J. (PI)
;
Sabatti, C. (PI)
;
Bhattacharya, S. (TA)
;
Ghosh, S. (TA)
;
Gupta, S. (TA)
;
Hamidi, N. (TA)
;
Hwang, J. (TA)
;
Roquero Gimenez, J. (TA)
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: Win, Sum

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Johndrow, J. (PI)
STATS 209: Statistical Methods for Group Comparisons and Causal Inference (EDUC 260A, HRP 239)
See
http://rogosateaching.com/stat209/. Critical examination of statistical methods in social science and life sciences applications, especially for cause and effect determinations. Topics: mediating and moderating variables, potential outcomes framework, encouragement designs, multilevel models, matching and propensity score methods, analysis of covariance, instrumental variables, compliance, path analysis and graphical models, group comparisons with longitudinal data. Prerequisite: intermediatelevel statistical methods.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Rogosa, D. (PI)
STATS 211: Metaresearch: Appraising Research Findings, Bias, and Metaanalysis (CHPR 206, 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. Metaanalysis 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 metaresearch project or reworking and evaluation of an existing published metaanalysis. Prerequisite: knowledge of basic statistics.
Terms: Win

Units: 3

Grading: Medical Satisfactory/No Credit
Instructors:
Ioannidis, J. (PI)
;
Michael, H. (TA)
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;crossvalidation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; treebased methods, random forests and boosting; supportvector machines; Some unsupervised learning: principal components and clustering (kmeans and hierarchical). Computing is done in R, through tutorial sessions and homework assignments. This mathlight course is offered via video segments (MOOC style), and inclass problem solving sessions. Prereqs: Introductory courses in statistics or probability (e.g.,
Stats 60 or
Stats 101), linear algebra (e.g.,
Math 51), and computer programming (e.g.,
CS 105).
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Tibshirani, R. (PI)
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