## 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)
;
Jain, V. (PI)
;
Kong, N. (PI)
...
more instructors for STATS 60 »

Instructors:
Auelua-Toomey, S. (PI)
;
Jain, V. (PI)
;
Kong, N. (PI)
;
Poldrack, R. (PI)
;
Walters, J. (PI)
;
Walther, G. (PI)
;
Dey, A. (TA)
;
Feldman, M. (TA)
;
Harrison, M. (TA)
;
Jeong, Y. (TA)
;
Jing, A. (TA)
;
Kirshenbaum, J. (TA)
;
Xu, H. (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: 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, 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)
;
Harrison, M. (PI)
;
Jain, V. (PI)
...
more instructors for STATS 160 »

Instructors:
Auelua-Toomey, S. (PI)
;
Harrison, M. (PI)
;
Jain, V. (PI)
;
Kirshenbaum, J. (PI)
;
Kong, N. (PI)
;
Poldrack, R. (PI)
;
Walters, J. (PI)
;
Walther, G. (PI)
;
Dey, A. (TA)
;
Feldman, M. (TA)
;
Jeong, Y. (TA)
;
Jing, A. (TA)
;
Xu, H. (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 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. Prerequisite: introductory statistical methods course. Recommended: 60, 110, or 141.

Terms: Win
| Units: 3
| UG Reqs: GER:DB-Math, WAY-AQR

## 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)
...
more instructors for STATS 199 »

Instructors:
Baiocchi, M. (PI)
;
Duchi, J. (PI)
;
Efron, B. (PI)
;
Lai, T. (PI)
;
Linderman, S. (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: 4

Instructors:
Romano, J. (PI)
;
Sabatti, C. (PI)
;
Chen, Z. (TA)
...
more instructors for STATS 200 »

Instructors:
Romano, J. (PI)
;
Sabatti, C. (PI)
;
Chen, Z. (TA)
;
Gibbs, I. (TA)
;
Han, K. (TA)
;
Li, S. (TA)
;
Misiakiewicz, T. (TA)
;
Pan, Z. (TA)
;
Pavlyshyn, D. (TA)
;
Ray, S. (TA)
;
Tay, J. (TA)
;
Tirlea, M. (TA)
;
Wu, Y. (TA)
;
Yang, 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. Prerequisites: A post-calculus introductory probability course, e.g.
STATS 116, basic computer programming knowledge, some familiarity with matrix algebra, and a pre- or co-requisite post-calculus mathematical statistics course, e.g.
STATS 200.

Terms: Win
| Units: 3

## STATS 209B: Applications of Causal Inference Methods (EDUC 260A, EPI 239)

See
http://rogosateaching.com/stat209/. Application of potential outcomes formulation for causal inference to research settings including: mediation, compliance adjustments, time-1 time-2 designs, encouragement designs, heterogeneous treatment effects, aggregated data, instrumental variables, analysis of covariance regression adjustments, and implementations of matching methods. Prerequisite:
STATS 209A/MSE 327 or other introduction to causal inference methods. (Formerly
HRP 239)

Terms: Win
| Units: 2

Instructors:
Rogosa, D. (PI)
;
Lemhadri, I. (TA)

## 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)

## STATS 214: Machine Learning Theory (CS 229M)

How do we use mathematical thinking to design better machine learning methods? This course focuses on developing mathematical tools for answering these questions. This course will cover fundamental concepts and principled algorithms in machine learning. We have a special focus on modern large-scale non-linear models such as matrix factorization models and deep neural networks. In particular, we will cover concepts and phenomenon such as uniform convergence, double descent phenomenon, implicit regularization, and problems such as matrix completion, bandits, and online learning (and generally sequential decision making under uncertainty). Prerequisites: linear algebra (
MATH 51 or
CS 205), probability theory (
STATS 116,
MATH 151 or
CS 109), and machine learning (
CS 229,
STATS 229, or
STATS 315A).

Terms: Win
| Units: 3

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