sta 131a uc davis sta 131a uc davis
Format: Topics include linear mixed models, repeated measures, generalized linear models, model selection, analysis of missing data, and multiple testing procedures. Course Description: Transformed random variables, large sample properties of estimates. Course Description: Focus on linear statistical models widely used in scientific research. /MediaBox [0 0 662.399 899.999] /Parent 8 0 R Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, variable transformation, factorial designs and ANCOVA. Units: 4 Format: Lecture: 3 hours Discussion: 1 hour Catalog Description:Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, laws of large numbers and the central limit theorem. Prerequisite(s): (STA130A, STA130B); (MAT067 or MAT167); or equivalent of STA130A and 130B, or equivalent of MAT167 or MAT067. Practical applications of widely-used designs, including dose-finding, comparative and cluster randomization designs. ), Statistics: Statistical Data Science Track (B.S. Regression. ), Statistics: General Statistics Track (B.S. Course Description: Examination of a special topic in a small group setting. Basic ideas of hypotheses testing, likelihood ratio tests, goodness-of-fit tests. Topics include resampling methods, regularization techniques in regression and modern classification, cluster analysis and dimension reduction techniques. Goals: UC Davis Course ECS 32A or 36A (or former courses ECS 10 or 30 or 40) UC Davis Course ECS 32B (or former course ECS 60) is also strongly recommended. General linear model, least squares estimates, Gauss-Markov theorem. PLEASE NOTE: These are only guidelines to help prepare yourself to transition to UC Davis with sufficient progress made towards your major. Concepts of correlation, regression, analysis of variance, nonparametrics. Pass One restricted to Statistics majors. You can find course articulations for California community colleges using assist.org. Course Description: Time series relationships; univariate time series models: trend, seasonality, correlated errors; regression with correlated errors; autoregressive models; autoregressive moving average models; spectral analysis: cyclical behavior and periodicity, measures of periodicity, periodogram; linear filtering; prediction of time series; transfer function models. Requirements from previous years can be found in the General Catalog Archive. ), Statistics: Computational Statistics Track (B.S. endobj Potential Overlap:There is no significant overlap with any one of the existing courses. The minor is designed to provide students in other disciplines with opportunities for exposure and skill development in advanced . Prerequisite(s): STA200B; or consent of instructor. Course Description: Third part of three-quarter sequence on mathematical statistics. Prerequisite(s): STA200A; or consent of instructor. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. All rights reserved. Statistical methods. Basic ideas of hypotheses testing, likelihood ratio tests, goodness-of- fit tests. Program in Statistics - Biostatistics Track. stream Prerequisite(s): STA130A C- or better or STA131A C- or better or MAT135A C- or better. Emphasizes foundations. ), Statistics: Statistical Data Science Track (B.S. The statistics undergraduate program at UC Davis offers a large and varied collection of courses in statistical theory, methodology, and application. This track emphasizes the underlying computer science, engineering, mathematics and statistics methodology and theory, and is especially recommended as preparation for graduate study in data science or related fields. Prerequisite(s): STA206; STA207; STA135; or their equivalents. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. Course Description: Special study for advanced undergraduates. Prerequisite(s): (STA035A C- or better or STA032 C- or better or STA100 C- or better); (MAT016B (can be concurrent) or MAT017B (can be concurrent) or MAT021B (can be concurrent)). May be taught abroad. Course Description: Fundamental concepts and methods in statistical learning with emphasis on unsupervised learning. STA 290 Seminar: Aidan Miliff Event Date. Topics include algorithms; design; debugging and efficiency; object-oriented concepts; model specification and fitting; statistical visualization; data and text processing; databases; computer systems and platforms; comparison of scientific programming languages. STA 130A addresses itself to a different audience, and contains a brief introduction to probabilistic concepts at a less sophisticated level. Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. Overlap with ECS 171 is more substantial. ), Statistics: General Statistics Track (B.S. The new Data Science major at UC Davis has been published in the general catalog! Course Description: Sign and Wilcoxon tests, Walsh averages. One-way and two-way fixed effects analysis of variance models. Prerequisite(s): (STA222 or BST222); (STA223 or BST223). Prerequisite(s): ((STA222, STA223) or (BST222, BST223)); STA232B; or consent of instructor. All rights reserved. Copyright The Regents of the University of California, Davis campus. It is designed to continue the integration of theory and applications, and to cover hypothesis testing, and several kinds of statistical methodology. Course Description: Advanced study in various fields of statistics with emphasis in applied topics, presented by members of the Graduate Group in Statistics and other guest speakers. Topics include basic concepts in asymptotic theory, decision theory, and an overview of methods of point estimation. >> ), Prospective Transfer Students-Data Science, Ph.D. Introduction to Probability, G.G. You are encouraged to contact the Statistics Department's Undergraduate Program Coordinator atstat-advising@ucdavis.eduif you have any questions about the statistics major tracks. Prerequisite(s): An introductory upper division statistics course and some knowledge of vectors and matrices; STA100, or STA 102, or STA103 suggested or the equivalent. including: (a) likelihood function; finding MLEs (finding a global maximum of a function) invariance of MLE; some limitations of ML-approach; exponential families; (b) Bayes approach, loss/risk functions; conjugate priors, MSE; bias-variance decomposition, unbiased estimation (2 lect) (IV) Sampling distributions: (5 lect) (a) distributions of transformed random variables; (b) t, F and chi^2 (properties:mgf, pdf, moments); (c) sampling distribution of sample variance under normality; independence of sample mean and sample variance under normality (V) Fisher information CR-lower bound efficiency (5 lect), Confidence intervals and bounds; concept of a pivot; (3 lect), Some elements of hypothesis testing: (5 lect) critical regions, level, size, power function, one-sided and two-sided tests; p-value); NP-framework, perhaps t-test. ,1; m"B=n /\zB1Unoj3;w4^+qQg0nS>EYOq,1q@d =_%r*tsP$gP|ar74[1GX!F V Y STA 130A addresses itself to a different audience, and contains a brief introduction to probabilistic concepts at a less sophisticated level. Course Description: Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, laws of large numbers and the central limit theorem. & B.S. Program in Statistics - Biostatistics Track. Regression and correlation, multiple regression. Please be sure to check the minor declaration deadline with your College. Potential Overlap:Statistics 131A and Mathematics 135A cover the topics in the first part of the course but with more in depth and theoretical orientations. Course Description: Resampling, nonparametric and semiparametric methods, incomplete data analysis, diagnostics, multivariate and time series analysis, applied Bayesian methods, sequential analysis and quality control, categorical data analysis, spatial and image analysis, computational biology, functional data analysis, models for correlated data, learning theory. Please check the Undergraduate Admissions website for information about admissions requirements. Course Description: Directed group study. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. I am aware of how Puckett is as a professor because I had friends who took him for MAT 22A Spring Quarter of Freshman year . Most transfer students start UC Davis at the beginning of their junior year and are usually able to complete their major and university requirements in the next two years. >> Apr 28-29, 2023. International Center, UC Davis. Pre-Matriculation Course Recommendations: If the courses above are completed pre-matriculation, your major course schedule at UC Davis will be similar to the one below. Prerequisite(s): STA015C C- or better or STA106 C- or better or STA108 C- or better. Course Description: Probability concepts; programming in R; exploratory data analysis; sampling distribution; estimation and inference; linear regression; simulations; resampling methods. Prerequisite(s): STA106; STA108; STA131A; STA131B; STA131C; MAT167. Prerequisite(s): (STA130B C- or better or STA131B C- or better); (MAT022A C- or better or MAT027A C- or better or MAT067 C- or better). Prerequisite: STA 108 C- or better or STA 106 C- or better. UC Davis Peter Hall Conference: Advances in Statistical Data Science. Location. School: College of Letters and Science LS Prerequisite(s): (MAT 125B, MAT135A) or STA131A; or consent of instructor. Analysis of variance, F-test. Basics of Probability Theory, Multivariate normal Basics of Decision Theory (decision space, decision rule, loss, risk) Exponential families; MLE; Sufficiency, Cramer-Rao Inequality Asymptotics with application to MLEs (and generalization to M-estimation)Illustrative Reading: ), Prospective Transfer Students-Data Science, Ph.D. Course Description: Statistics and probability in daily life. Apr 28-29, 2023. International Center, UC Davis. Most UC Davis transfer students come from California community colleges. UC Davis Course ECS 32A or 36A (or former courses ECS 10 or 30 or 40) UC Davis Course ECS 32B (or former course ECS 60) is also strongly recommended. Course information: MAT 21D, Winter Quarter, 2021 Lectures: Online (asynchronous): lectures will be posted to Canvas on MWF before 5pm. Prerequisite: STA 141A C- or better; (STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better); STA 131A or MAT 135A preferred. ), Statistics: Statistical Data Science Track (B.S. Prerequisite(s): STA131A; STA232A recommended, not required. STA 131A Introduction to Probability Theory (4 units) Course Description: Fundamental concepts of probability theory, discrete and continuous random variables, standard distributions, moments and moment-generating functions, . One-way random effects model. Admissions to UC Davis is managed by the Undergraduate Admissions Office. ), Statistics: Applied Statistics Track (B.S. UC Davis Department of Statistics. UC Davis Peter Hall Conference: Advances in Statistical Data Science. Effective Term: 2008 Summer Session I. . /Filter /FlateDecode Elective MAT 135A or STA 131A. Test heavy Caring. ), Statistics: Statistical Data Science Track (B.S. Conditional expectation. Prerequisite(s): STA013 or STA013Y or STA032 or STA100 or STA103. Interactive data visualization with Web technologies. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. ), Statistics: Machine Learning Track (B.S. Prentice Hall, Upper Saddle River, N.J. Instructor: Prof. Peter Hall Lecture times: 11.00 am Mondays, Wednesdays and Fridays, in Olson 223. One Introductory Statistics Course UC Davis Course STA 13 or 32 or 100; If the courses above are completed pre-matriculation, your major course schedule at UC Davis will be similar to the one below. Multidimensional tables and log-linear models, maximum likelihood estimation; tests of goodness-of-fit. These methods are useful for conducting research in applied subjects, and they are appealing to employees and graduate schools seeking students with quantitative skills. Lecturing techniques, analysis of tests and supporting material, preparation and grading of examinations, and use of statistical software. Admissions decisions are not handled by the Department of Statistics. Prerequisite(s): Introductory statistics course; some knowledge of vectors and matrices. Course Description: Introduction to consulting, in-class consulting as a group, statistical consulting with clients, and in-class discussion of consulting problems. ), Statistics: General Statistics Track (B.S. Prerequisite:STA 141A C- or better; (STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better); STA 131A or MAT 135A preferred. The course STA 130A with which it is somewhat related, is the first part of a two part course, STA 130A,B covering both probability and statistical inference. Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. Prerequisite(s): (MAT016C C- or better or MAT017C C- or better or MAT021C C- or better); (STA013 C- or better or STA013Y C- or better or STA032 C- or better or STA100 C- or better). ), Statistics: Computational Statistics Track (B.S. ), Statistics: Machine Learning Track (B.S. STA 130A Mathematical Statistics: Brief Course. Course Description: Multivariate analysis: multivariate distributions, multivariate linear models, data analytic methods including principal component, factor, discriminant, canonical correlation and cluster analysis. STA 141A Fundamentals of Statistical Data Science. Untis: 4.0 Prerequisite(s): STA035B C- or better; (MAT016B C- or better or MAT017B C- or better or MAT021B C- or better). STA 131A Introduction to Probability Theory. Topics include basic concepts in asymptotic theory, decision theory, and an overview of methods of point estimation. STA 141A Fundamentals of Statistical Data Science, STA 141BData & Web Technologies for Data Analysis, STA 141CBig Data & High Performance Statistical Computing, STA 160Practice in Statistical Data Science. Statistics 131A and Mathematics 135A cover the topics in the first part of the course but with more in depth and theoretical orientations. Prerequisite(s): Senior qualifying for honors. ), Statistics: General Statistics Track (B.S. ), Statistics: Statistical Data Science Track (B.S. Similar topics are covered in STA 131B and 131C. Prerequisite(s): Two years of high school algebra. Lecture: 3 hours Computational data workflow and best practices. Prerequisite(s): STA231C; STA235A, STA235B, STA235C recommended. Course Description: Subjective probability, Bayes Theorem, conjugate priors, non-informative priors, estimation, testing, prediction, empirical Bayes methods, properties of Bayesian procedures, comparisons with classical procedures, approximation techniques, Gibbs sampling, hierarchical Bayesian analysis, applications, computer implemented data analysis.