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41 - 50 of 108 results for: STATS

STATS 253: Analysis of Spatial and Temporal Data

A unified presentation of statistical methods for correlated data, such as spatial data and time series. Classical techniques based on Gaussian data, such as ARMA and kriging. State-space approaches, such as Kalman filtering and hidden Markov models. Point processes. Frequency domain and Bayesian approaches. Emphasis on emerging applications, such as music analysis, image processing, and digital humanities -- although classical applications, such as geology and finance, will also be covered. Prerequisites: linear algebra, STATS 200, exposure to linear regression
Terms: Sum | Units: 3
Instructors: Sun, D. (PI)

STATS 260A: Workshop in Biostatistics (HRP 260A)

Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Terms: Aut | Units: 1-2 | Repeatable for credit

STATS 260B: Workshop in Biostatistics (HRP 260B)

Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Terms: Win | Units: 1-2 | Repeatable for credit

STATS 260C: Workshop in Biostatistics (HRP 260C)

Applications of statistical techniques to current problems in medical science. To receive credit for one or two units, a student must attend every workshop. To receive two units, in addition to attending every workshop, the student is required to write an acceptable one page summary of two of the workshops, with choices made by the student.
Terms: Spr | Units: 1-2 | Repeatable for credit

STATS 261: Intermediate Biostatistics: Analysis of Discrete Data (BIOMEDIN 233, HRP 261)

Methods for analyzing data from case-control and cross-sectional studies: the 2x2 table, chi-square test, Fisher's exact test, odds ratios, Mantel-Haenzel methods, stratification, tests for matched data, logistic regression, conditional logistic regression. Emphasis is on data analysis in SAS. Special topics: cross-fold validation and bootstrap inference.
Terms: Win | Units: 3
Instructors: Sainani, K. (PI)

STATS 262: Intermediate Biostatistics: Regression, Prediction, Survival Analysis (HRP 262)

Methods for analyzing longitudinal data. Topics include Kaplan-Meier methods, Cox regression, hazard ratios, time-dependent variables, longitudinal data structures, profile plots, missing data, modeling change, MANOVA, repeated-measures ANOVA, GEE, and mixed models. Emphasis is on practical applications. Prerequisites: basic ANOVA and linear regression.
Terms: Spr | Units: 3
Instructors: Sainani, K. (PI)

STATS 267: Probability: Ten Great Ideas About Chance (PHIL 166, PHIL 266, STATS 167)

Foundational approaches to thinking about chance in matters such as gambling, the law, and everyday affairs. Topics include: chance and decisions; the mathematics of chance; frequencies, symmetry, and chance; Bayes great idea; chance and psychology; misuses of chance; and harnessing chance. Emphasis is on the philosophical underpinnings and problems. Prerequisite: exposure to probability or a first course in statistics at the level of STATS 60 or 116.
Last offered: Spring 2013

STATS 270: A Course in Bayesian Statistics (STATS 370)

Advanced-level Bayesian statistics. Topics: Discussion of the mathematical and theoretical foundation for Bayesian inferential procedures. Examination of the construction of priors and the asymptotic properties of likelihoods and posterior densities. Discussion including but not limited to the case of finite dimensional parameter space. Prerequisite: familiarity with standard probability and multivariate distribution theory.
Terms: Win | Units: 3
Instructors: Wong, W. (PI)

STATS 290: Paradigms for Computing with Data

Advanced programming and computing techniques to support projects in data analysis and related research. For Statistics graduate students and others whose research involves data analysis and development of associated computational software. Prerequisites: Programming experience including familiarity with R; computing at least at the level of CS 106; statistics at the level of STATS 110 or 141.
Terms: Win | Units: 3

STATS 297: Practical Training

For students in the M.S. program in Financial Mathematics only. Students obtain employment, with the approval and supervision of a faculty member, in a relevant industrial or research activity to enhance their professional experience. Students must submit a written final report upon completion of the internship in order to receive credit. May be repeated once for credit. Prerequisite: consent of adviser.
Terms: Aut, Win, Spr, Sum | Units: 1-3 | Repeatable 2 times (up to 6 units total)
Instructors: Lai, T. (PI)
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