## 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
| Units: 5
| UG Reqs: GER:DB-Math, WAY-AQR, WAY-FR

Instructors:
Auelua-Toomey, S. (PI)
;
Cook, N. (PI)
;
Kong, N. (PI)
...
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Instructors:
Auelua-Toomey, S. (PI)
;
Cook, N. (PI)
;
Kong, N. (PI)
;
Poldrack, R. (PI)
;
Walters, J. (PI)
;
Zhu, X. (PI)
;
Bhattacharya, S. (TA)
;
Dey, A. (TA)
;
Harrison, M. (TA)
;
Lemhadri, I. (TA)
;
Schwartz, J. (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
| Units: 5

Instructors:
Auelua-Toomey, S. (PI)
;
Cook, N. (PI)
;
Harrison, M. (PI)
...
more instructors for STATS 160 »

Instructors:
Auelua-Toomey, S. (PI)
;
Cook, N. (PI)
;
Harrison, M. (PI)
;
Kong, N. (PI)
;
Poldrack, R. (PI)
;
Schwartz, J. (PI)
;
Walters, J. (PI)
;
Zhu, X. (PI)
;
Bhattacharya, S. (TA)
;
Dey, A. (TA)
;
Lemhadri, I. (TA)

## STATS 199: Independent Study

For undergraduates.

Terms: Aut, Win, Spr, Sum
| Units: 1-15
| Repeatable for credit

Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
...
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Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
;
Lai, T. (PI)
;
Rogosa, D. (PI)
;
Sabatti, C. (PI)
;
Taylor, J. (PI)
;
Wager, S. (PI)
;
Walther, G. (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:
STATS 116.

Terms: Aut, Win
| Units: 3

Instructors:
Melnikov, O. (PI)
;
Palacios, J. (PI)
;
Cai, F. (TA)
...
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Instructors:
Melnikov, O. (PI)
;
Palacios, J. (PI)
;
Cai, F. (TA)
;
Dey, A. (TA)
;
Fry, K. (TA)
;
Jing, A. (TA)
;
Pavlyshyn, D. (TA)
;
Ray, S. (TA)
;
Ren, Z. (TA)
;
Roquero Gimenez, J. (TA)
;
Seiler, B. (TA)
;
Xu, H. (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. Prerequisites: a post-calculus introductory probability course, e.g.
STATS 116. In addition, a co-requisite post-calculus mathematical statistics course, e.g.
STATS 200, basic computer programming knowledge, and some familiarity with matrix algebra.

Terms: Win
| Units: 3

## STATS 206: Applied Multivariate Analysis

Introduction to the statistical analysis of several quantitative measurements on each observational unit. Emphasis is on concepts, computer-intensive methods. Examples from economics, education, geology, psychology. Topics: multiple regression, multivariate analysis of variance, principal components, factor analysis, canonical correlations, multidimensional scaling, clustering. Pre- or corequisite: 200.

Terms: Win
| Units: 3

Instructors:
Hastie, T. (PI)
;
Zhao, Q. (TA)

## STATS 208: Bootstrap, Cross-Validation, and Sample Re-use

By re-using the sample data, sometimes in ingenious ways, we can evaluate the accuracy of predictions, test the significance of a conclusion, place confidence bounds on an unknown parameter, select the best prediction architecture, and develop more accurate predictors. In this course, we will describe the many ways that samples get reused to achieve these goals, including the bootstrap, the parametric bootstrap, cross-validation, conformal prediction, random forests, and sample splitting. We also develop basic theory justifying such methods. Prerequisite: course in statistics or probability.

Terms: Win
| Units: 3

Instructors:
Donoho, D. (PI)

## STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis (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. 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)
;
Jansen, J. (SI)

## STATS 215: Statistical Models in Biology

Poisson and renewal processes, Markov chains in discrete and continuous time, branching processes, diffusion. Applications to models of nucleotide evolution, recombination, the Wright-Fisher process, coalescence, genetic mapping, sequence analysis. Theoretical material approximately the same as in
STATS 217, but emphasis is on examples drawn from applications in biology, especially genetics. Prerequisite: 116 or equivalent.

Terms: Win
| Units: 3

Instructors:
Linderman, S. (PI)
;
Han, K. (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;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. 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

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