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. Prerequisite: STA 131A C- or better or MAT 135A C . Both courses cover the fundamentals of the various methods and techniques, their implementation and applications. Illustrative reading: UC Davis Department of Statistics University of California, Davis , One Shields Avenue, Davis, CA 95616 | 530-752-1011 ), Prospective Transfer Students-Data Science, Ph.D. 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. if you have any questions about the statistics major tracks. Course Description: Classical and Bayesian inference procedures in parametric statistical models. ), Prospective Transfer Students-Data Science, Ph.D. Prerequisite(s): MAT021A; MAT021B; MAT021C; MAT022A; consent of instructor. At minimum, calculus at the level of MAT 16C or 17C or 21C is required. Two-sample procedures. Prerequisite(s): STA207 or STA232B; working knowledge of advanced statistical software and the equivalent of STA207 or STA232B. Admissions decisions are not handled by the Department of Statistics. ), Statistics: Computational Statistics Track (B.S. STA 130A - Mathematical Statistics: Brief Course (MAT 16C or 17C or 21C); (STA 13 or 32 or 100) Fall, Winter . STA 35C STS 101 2nd Year: Fall. Program in Statistics - Biostatistics Track, Random experiments, sample spaces, events, Independence, conditional probability, Bayes Theorem, Covariance and conditional expectation for discrete random variables, Special distributions and models, with applications, Discrete distributions including binomial, poisson, geometric, negative binomial and hypergeometric, Continuous distributions including normal, exponential, gamma, uniform, Sums of independant binomial, poisson, normal and gamma random variables, Central limit theorem and law of large numbers, Approximations for certain discrete random variables, Minimum variance unbiased estimation, Cramer-Rao inequality, Confidence intervals for means, proportions and variances. Illustrative reading:Introduction to Probability, G.G. Prerequisite(s): Senior qualifying for honors. /MediaBox [0 0 662.399 899.999] Prerequisite(s): MAT016A (can be concurrent) or MAT017A (can be concurrent) or MAT021A (can be concurrent). STA 108 ECS 17. General linear model, least squares estimates, Gauss-Markov theorem. The Bachelor of Science has fiveemphases call tracks. Statistics: Applied Statistics Track (A.B. Please check the Undergraduate Admissions website for information about admissions requirements. Statistics: Applied Statistics Track (A.B. History: & B.S. The 92 credit major aims to provide a foundation in the theory and methodology behind data science, and to prepare students for more advanced studies. /Parent 8 0 R First part of three-quarter sequence on mathematical statistics. Prerequisite(s): STA223 or BST223; or consent of instructor. Not open for credit to students who have completed Mathematics 135A. Prerequisite: STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. J} \Ne8pAu~q"AqD2z LjEwD69(-NI3#W3wJ|XRM4l$.z?^YU.*$zIy0IZ5 /H]) G3[LO<=>S#%Ce8g'd/Q-jYY~b}}Dr_9-Me^MnZ(,{[1seh:/$( w*c\SE3kJ_47q(kQP3p FnMP.B\g4hpwsZ4 XMd1vyv@m_gt ,h+3gU *vGoJYO9 T z-7] x Course Description: Practical experience in methods/problems of teaching statistics at university undergraduate level. Course Description: Fundamental concepts and methods in statistical learning with emphasis on supervised learning. Prerequisite(s): STA015A C- or better or STA013 C- or better or STA032 C- or better or STA100 C- or better. A primary emphasis will be on understanding the methodologies through numerical simulations and analysis of real-world data. At most, one course used in satisfaction of your minor may be applied to your major. All rights reserved. Although the two courses, MAT 135A and STA 131A discuss many of the same topics, the orientation and the nature of the discussion are quite distinct. Catalog Description:Basic probability, densities and distributions, mean, variance, covariance, Chebyshev's inequality, some special distributions, sampling distributions, central limit theorem and law of large numbers, point estimation, some methods of estimation, interval estimation, confidence intervals for certain quantities, computing sample sizes. Mathematical Sciences Building 1147. . k#wm/~Aq& >_{cX!Q9J"F\PDk:~y^ y Ei Aw6SWb#(#aBDNe]6_hsqh)X~X2% %af`@H]m6h4 SUxS%l 6j:whN_EGa5=OTkB0a%in=p(4y2(rxX#z"h!hOgoa'j%[c$r=ikV Restrictions: ), Statistics: Machine Learning Track (B.S. Course Description: Seminar on advanced topics in probability and statistics. Multiple comparisons procedures. 2 0 obj << You are encouraged to contact the Statistics Department's Undergraduate Program Coordinator at. Models for experimental data, measures of dependence, large-sample theory, statistical estimation and inference. Copyright The Regents of the University of California, Davis campus. Format: Course Description: Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. Apr 28-29, 2023. International Center, UC Davis. ), Statistics: Statistical Data Science Track (B.S. There is no significant overlap with any one of the existing courses. Emphasis on concepts, methods and data analysis using SAS. Xiaodong Li. Roussas, Academic Press, 2007. Similar topics are covered in STA 131B and 131C. 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. /Contents 3 0 R /Filter /FlateDecode The course MAT 135A is an introduction to probability theory from purely MAT and more advanced viewpoint. Copyright The Regents of the University of California, Davis campus. Prerequisite:STA 130A C- or better or STA 131A C- or better or MAT 135A C- or better. STATISTICS 131A | Probability Theory Textbook: Ross, S. (2010). 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. O?"cNlCs*/{GE>! Roussas, Academic Press, 2007None. >> endobj Graduate standing. Prerequisite(s): STA106; STA108; STA131C; STA232B; MAT167. STA 141A Fundamentals of Statistical Data Science. ), Statistics: Machine Learning Track (B.S. Prerequisite(s): STA235A or MAT235A; or consent of instructor. You can find course articulations for California community colleges using assist.org. Units: 4. STA 290 Seminar: Sam Pimentel Event Date. endstream Prerequisite(s): STA106; STA108; STA131A; STA131B; STA131C; MAT167. Prospective Transfer Students-Statistics, A.B. If you want to have completion of a minor certified on your transcript, you must submit an online Minor Declaration Form by the 10th day of instruction of the quarter that you are graduating. Transformed random variables, large sample properties of estimates. stream Course Description: Topics may include Bayesian analysis, nonparametric and semiparametric regression, sequential analysis, bootstrap, statistical methods in high dimensions, reliability, spatial processes, inference for stochastic process, stochastic methods in finance, empirical processes, change-point problems, asymptotics for parametric, nonparametric and semiparametric models, nonlinear time series, robustness. Effective Term: 2008 Summer Session I. Prerequisite(s): STA231C; STA235A, STA235B, STA235C desirable. Course Description: Introduction to computing for data analysis & visualization, and simulation, using a high-level language (e.g., R). ), Prospective Transfer Students-Data Science, Ph.D. Basics of text mining. It's definitely hard, but so far I'm having a better time with the material than I did with 131A. Chi square and Kolmogorov-Smirnov tests. STA 130A addresses itself to a different audience, and contains a brief introduction to probabilistic concepts at a less sophisticated level. Goals: Some topics covered in STA 231A are covered, at a more elementary level, in the sequence STA 131A,B,C. All rights reserved. Thu, May 11, 2023 @ 4:10pm - 5:30pm. Course Description: Linear and nonlinear statistical models emphasis on concepts, methods/data analysis using professional level software. STA 131A - Introduction to Probability Theory Statistics: Applied Statistics Track (A.B. Regression. Pass One restricted to Statistics majors. Oh ok. Thing is that MAT 22A is a prereq for STA 131A and the STA 131 series is far from easy, so I would rather play it safe on this one. All rights reserved. Prerequisite(s): STA200A; or consent of instructor. %PDF-1.5 Prerequisite(s): Consent of instructor; high school algebra. Relation to other probability courses provided by the statistics department at Davis STA 130A: Basic probability concepts/results and estimation theory; STA 200A: More serious in the mathematics of . Although the two courses, MAT 135A and STA 131A discuss many of the same topics, the orientation and the nature of the discussion are quite distinct. stream Weak convergence in metric spaces, Brownian motion, invariance principle. Course Description: Testing theory, tools and applications from probability theory, Linear model theory, ANOVA, goodness-of-fit. STA 290 Seminar: Aidan Miliff Event Date. ): Concept of a statistical model; observations as random variables, definition/examples of a statistic, statistical inference and examples throughout the entire course: emphasize the difference between population quantities, random variables and observables, Methods of estimation: MLEs, Bayes, MOM (5 lect.) May be taught abroad. /ProcSet [ /PDF /Text ] ), Statistics: Machine Learning Track (B.S. Statistics: Applied Statistics Track (A.B. ), Statistics: General Statistics Track (B.S. Prerequisite(s): STA013 C- or better or STA013Y C- or better or STA032 C- or better or STA100 C- or better. Course Description: First part of three-quarter sequence on mathematical statistics. Advanced statistical procedures for analysis of data collected in clinical trials. ), Statistics: General Statistics Track (B.S. 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: ), Statistics: Applied Statistics Track (B.S. Course Description: Varieties of categorical data, cross-classifications, contingency tables, tests for independence. Prepare SAS base programmer certification exam. All rights reserved. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. The statistics undergraduate program at UC Davis offers a large and varied collection of courses in statistical theory, methodology, and application. Why Choose UC Davis? Only 2 units of credit allowed to students who have taken course 131A. . The Department offers a minor program in Statistics that consists of five upper division level courses focusing on the fundamentals of mathematical statistics and of the most widely used applied statistical methods. Course Description: Topics from balanced and partially balanced incomplete block designs, fractional factorials, and response surfaces. Please check our Frequently Asked Questions page if you have any questions. >> Probability 4 STA 131A - Introduction to Probability Theory 4 Statistics 12 STA 108 - Applied Stat Methods . -- A. J. Izenman. Computational data workflow and best practices. Based on these offerings, a student can complete a Bachelor of Arts or a Bachalor of Science degree in Statistics. Principles, methodologies and applications of parametric and nonparametric regression, classification, resampling and model selection techniques. All rights reserved. Copyright The Regents of the University of California, Davis campus. ), Statistics: General Statistics Track (B.S. Inferences concerning scale. Goals: This course is a continuations of STA 130A. 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. Course Description: Essentials of using relational databases and SQL. Prerequisite(s): STA013 or STA013Y or STA032 or STA100 or STA103. zluM;TNNEkn8>"s|yDs+YZ4A+P3+pc-gGF7Piq1.IMw[v(vFI@!oyEgK!'%d"P~}`VU?RS7N4w4Z/8M--\HE?UCt3]L3?64OE`>(x4hF"A7=L&DpufI"Q$*)H$*>BP8YkjpqMYsPBv{R* ), Statistics: Statistical Data Science Track (B.S. UC Davis Course STA 13 or STA 35A; If the courses above are completed pre-matriculation, your major course schedule at UC Davis will be similar to the one below. Learning Activities: Lecture 3 hour(s), Discussion/Laboratory 1 hour(s). All rights reserved. UC Davis 2022-2023 General Catalog. Selected topics. Prerequisite(s): STA206; knowledge of vectors and matrices. Please check the Undergraduate Admissions website for information about admissions requirements. Test heavy Caring. Course Description: Basic probability, densities and distributions, mean, variance, covariance, Chebyshev's inequality, some special distributions, sampling distributions, central limit theorem and law of large numbers, point estimation, some methods of estimation, interval estimation, confidence intervals for certain quantities, computing sample sizes. The statistics undergraduate program at UC Davis offers a large and varied collection of courses in statistical theory, methodology, and application. In order to ensure that you are able to transfer to UC Davis with sufficient progress made towards your major, below is information regarding the courses you are recommended to take before transferring. Basic ideas of hypotheses testing, likelihood ratio tests, goodness-of- fit tests. ), Statistics: Applied Statistics Track (B.S. Discussion: 1 hour. Course Description: Descriptive statistics, probability, sampling distributions, estimation, hypothesis testing, contingency tables, ANOVA, regression; implementation of statistical methods using computer package. Scraping Web pages and using Web services/APIs. Admissions decisions are not handled by the Department of Statistics. MAT 021C C- or better; (MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better); MAT 021D strongly recommended. 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. *Choose one of MAT 108 or 127C. Topics include linear mixed models, repeated measures, generalized linear models, model selection, analysis of missing data, and multiple testing procedures. Department: Statistics STA Instructor O ce hours: 12.00{2.00 pm Friday TA O ce hours: 12{1 pm Tuesday, 1{2 pm Thursday, 1117 MSB Regression and correlation, multiple regression. Program in Statistics - Biostatistics Track. Discussion: 1 hour. Course Description: Descriptive statistics; probability; random variables; expectation; binomial, normal, Poisson, other univariate distributions; joint distributions; sampling distributions, central limit theorem; properties of estimators; linear combinations of random variables; testing and estimation; Minitab computing package. Topics include basic concepts in asymptotic theory, decision theory, and an overview of methods of point estimation. Course Description: Measure-theoretic foundations, abstract integration, independence, laws of large numbers, characteristic functions, central limit theorems. ), Statistics: Machine Learning Track (B.S. Copyright The Regents of the University of California, Davis campus. Concepts of correlation, regression, analysis of variance, nonparametrics. Basics of text mining. Statistics: Applied Statistics Track (A.B. Prerequisite(s): Consent of instructor; advancement to candidacy for Ph.D. Prerequisite: STA 108 C- or better or STA 106 C- or better. ), Statistics: General Statistics Track (B.S. Prerequisite(s): (EPI 202 or STA 130A or STA 131A or STA 133); EPI 205; a basic epidemiology course (EPI 205 or equivalent). Prerequisite:MAT 021C C- or better; (MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better); MAT 021D strongly recommended. ), Prospective Transfer Students-Data Science, Ph.D. Prerequisite(s): STA206; STA207; STA135; or their equivalents. Basic ideas of hypotheses testing, likelihood ratio tests, goodness-of- fit tests. May be taught abroad. Packaged computer programs, analysis of real data. Emphasis on practical training. ), Prospective Transfer Students-Data Science, Ph.D. ), Statistics: Statistical Data Science Track (B.S. Prerequisite: MAT 021C C- or better; (MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better); MAT 021D . STA 131B Introduction to Mathematical Statistics. All rights reserved. Prerequisite(s): MAT016B C- or better or MAT021B C- or better or MAT017B C- or better. xko{~{@ DR&{P4h`'Rw3J^809+By:q2("BY%Eam}v{Y5~~x{{Qy%qp3rT"x&vW6Y It is designed to continue the integration of theory and applications, and to cover hypothesis testing, and several kinds of statistical methodology. Course Description: Likelihood and linear regression; generalized linear model; Binomial regression; case-control studies; dose-response and bioassay; Poisson regression; Gamma regression; quasi-likelihood models; estimating equations; multivariate GLMs. Some topics covered in STA 231A are covered, at a more elementary level, in the sequence STA 131A,B,C. Course Description: Focus on linear statistical models. Lecturing techniques, analysis of tests and supporting material, preparation and grading of examinations, and use of statistical software. Apr 28-29, 2023. International Center, UC Davis. PLEASE NOTE: These are only guidelines to help prepare yourself to transition to UC Davis with sufficient progress made towards your major. Catalog Description: Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. STA 290 Seminar: Sam Pimentel. Overview of computer networks, TCP/IP protocol suite, computer-networking applications and protocols, transport-layer protocols, network architectures, Internet Protocol (IP), routing, link-layer protocols, local area and wireless networks, medium access control, physical aspects of data transmission, and network-performance analysis. I'm taking 130B and find the material a bit more intuitive than 131A. Course Description: Programming in R; Summarization and visualization of different data types; Concepts of correlation, regression, classification and clustering. ), Statistics: Statistical Data Science Track (B.S. Mathematical Sciences Building 1147. . ~.S|d&O`S4/ COkahcoc B>8rp*OS9rb[!:D >N1*iyuS9QG(r:| 2#V`O~/ 4ClJW@+d Format: Emphasizes: hyposthesis testing (including multiple testing) as well as theory for linear models. Topics selected from: martingales, Markov chains, ergodic theory. General linear model, least squares estimates, Gauss-Markov theorem. ECS 116. Format: Prerequisite: (STA 130B C- or better or STA 131B C- or better); (MAT 022A C- or better or MAT 027A C- or better or MAT 067 C- or better). Course Description: Incomplete data; life tables; nonparametric methods; parametric methods; accelerated failure time models; proportional hazards models; partial likelihood; advanced topics. Univariate and multivariate spectral analysis, regression, ARIMA models, state-space models, Kalman filtering. ), Statistics: General Statistics Track (B.S. University of California, Davis, One Shields Avenue, Davis, CA 95616 | 530-752-1011. The deadline to file your minor petition may vary by College. Computational reasoning, computationally intensive statistical methods, reading tabular and non-standard data. Conditional expectation. Introduction to Probability, G.G. Description. Please follow the links below to find out more information about our major tracks. Course Description: Third part of three-quarter sequence on mathematical statistics. Course Description: Focus on linear and nonlinear statistical models. Mathematical Statistics and Data Analysis -- by J. RiceMathematical Statistics: A Text for Statisticians and Quantitative Scientists -- by F. J. Samaniego. Restrictions: Only 2 units of credit allowed to students who have taken course 131A . Use professional level software. . School: College of Letters and Science LS Applications in the social, biological, and engineering sciences. 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. The minor is designed to provide students in other disciplines with opportunities for exposure and skill development in advanced . STA 130B - Mathematical Statistics: Brief Course STA 130A or 131A or MAT 135A : Winter, Spring . /Resources 1 0 R ), Prospective Transfer Students-Data Science, Ph.D. The course MAT 135A is an introduction to probability theory from purely MAT and more advanced viewpoint. Prerequisite(s): STA131A; STA232A recommended, not required. ), Statistics: Applied Statistics Track (B.S. Analysis of variance, F-test. ), Statistics: Statistical Data Science Track (B.S. Prerequisite(s): STA131A; STA131B; STA131C; MAT 025; MAT 125A; or equivalent of MAT 025 and MAT 125A. Course Description: Multivariate analysis: multivariate distributions, multivariate linear models, data analytic methods including principal component, factor, discriminant, canonical correlation and cluster analysis. Course Description: Comprehensive treatment of nonparametric statistical inference, including the most basic materials from classical nonparametrics, robustness, nonparametric estimation of a distribution function from incomplete data, curve estimation, and theory of resampling methodology. Topics include basic concepts in asymptotic theory, decision theory, and an overview of methods of point estimation. Subject: STA 231A 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, . Processing data in blocks. Nonparametric methods; resampling techniques; missing data. Prerequisite(s): STA035B C- or better; (MAT016B C- or better or MAT017B C- or better or MAT021B C- or better). Prerequisite(s): STA015C C- or better or STA106 C- or better or STA108 C- or better. One-way and two-way fixed effects analysis of variance models. Winter. 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. ), Statistics: General Statistics Track (B.S. Location. 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. ), Prospective Transfer Students-Data Science, Ph.D. Practical applications of widely-used designs, including dose-finding, comparative and cluster randomization designs. Course Description: Estimation and testing for the general linear model, regression, analysis of designed experiments, and missing data techniques. Some of the broad topics, such as classification and regression overlap with STA 135. Prentice Hall, Upper Saddle River, N.J. Instructor: Prof. Peter Hall Lecture times: 11.00 am Mondays, Wednesdays and Fridays, in Olson 223. Use of professional level software. It is not a course of statistics, but very fundamental and useful for statistics; . My friends refer to 131B as the hardest class in the series. Catalog Description:Sampling, methods of estimation, bias-variance decomposition, sampling distributions, Fisher information, confidence intervals, and some elements of hypothesis testing. Prerequisite(s): STA108 C- or better or STA106 C- or better. ), Statistics: General Statistics Track (B.S. Untis: 4.0 The course material for STA 200A is the same as for STA 131A with the exception that students in STA 200A are given additional advanced reading material and additional homework assignments. Grade Mode: Letter. Computational reasoning, computationally intensive statistical methods, reading tabular & non-standard data. You are encouraged to contact the Statistics Department's Undergraduate Program Coordinator at. Program in Statistics. The minor is flexible, so that students from most majors can find a path to the minor that serves their needs. 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