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)
;
Taylor, J. (PI)
;
Thomas, E. (PI)
...
more instructors for STATS 60 »
Instructors:
DiCiccio, C. (PI)
;
Taylor, J. (PI)
;
Thomas, E. (PI)
;
Walther, G. (PI)
;
Xia, L. (PI)
;
Chin, A. (TA)
;
DiCiccio, C. (TA)
;
Donnat, C. (TA)
;
Janson, L. (TA)
;
Miao, J. (TA)
;
Orenstein, P. (TA)
;
Patterson, E. (TA)
;
SUR, P. (TA)
;
YAN, J. (TA)
;
YANG, 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, 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)
;
Taylor, J. (PI)
;
Thomas, E. (PI)
...
more instructors for STATS 160 »
Instructors:
DiCiccio, C. (PI)
;
Taylor, J. (PI)
;
Thomas, E. (PI)
;
Walther, G. (PI)
;
Xia, L. (PI)
;
DiCiccio, C. (TA)
;
Donnat, C. (TA)
;
Miao, J. (TA)
;
Orenstein, P. (TA)
;
SUR, P. (TA)
;
YAN, J. (TA)
;
YANG, J. (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
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. Students may enroll in summer quarters only for a total of three times. For corresponding Statistics master's course see
Stats 298.
Terms: Win, Spr, Sum

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors:
Duchi, J. (PI)
;
Efron, B. (PI)
;
Holmes, S. (PI)
;
Ioannidis, J. (PI)
;
Sabatti, C. (PI)
;
Siegmund, D. (PI)
;
Taylor, J. (PI)
STATS 199: Independent Study
For undergraduates.
Terms: Aut, Win, Spr, Sum

Units: 115

Repeatable for credit

Grading: Satisfactory/No 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)
;
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)
;
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; NeymanPearson theory. Bayesian analysis; maximum likelihood, large sample theory. Prerequisite: 116.
Terms: Aut, Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Reid, S. (PI)
;
Siegmund, D. (PI)
;
Arthur, J. (TA)
...
more instructors for STATS 200 »
Instructors:
Reid, S. (PI)
;
Siegmund, D. (PI)
;
Arthur, J. (TA)
;
Bai, Y. (TA)
;
DiCiccio, C. (TA)
;
Friedberg, R. (TA)
;
Gao, K. (TA)
;
Orenstein, P. (TA)
;
Rosenman, E. (TA)
;
Walsh, D. (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

Units: 3

Grading: Letter or Credit/No Credit
STATS 209: Statistical Methods for Group Comparisons and Causal Inference (EDUC 260A, HRP 239)
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. See
http://rogosateaching.com/stat209/. Prerequisite: intermediatelevel statistical methods.
Terms: Win

Units: 3

Grading: Letter or Credit/No Credit
Instructors:
Rogosa, D. (PI)
;
Janson, L. (TA)
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
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 WrightFisher 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

Grading: Letter or Credit/No Credit
Instructors:
Siegmund, D. (PI)
;
Hamidi, N. (TA)
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