| source UCLA (X) |
level |
department Statistics (X) |
(Formerly numbered 10A.) Lecture, three hours; discussion, one hour; computer laboratory, two hours. Preparation: three years of high school mathematics. Not open for credit to students with credit for course 10H, 11, 12, 13, or 14. Introduction to statistical thinking and understanding, including strengths and limitations of basic experimental designs, graphical and numerical summaries of data, inference, regression as descriptive tool. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Lecture, three hours; discussion, two hours. Preparation: three years of high school mathematics. Not open for credit to students with credit for course 10, 11, 12, or 13. Descriptive statistics, elementary probability, random variables, binomial and normal distributions. Large and small sample inference concerning means. Introduction to statistical software. Letter grading.
Score: 8.484722 Details | Listing | Web page
Lecture, three hours; discussion, one hour; computer laboratory, one hour. Requisite or corequisite: Mathematics 3A or 31A. Not open for credit to students with credit for course 10, 10H, 12, 13, 14, 100A, 100B, 100C, Mathematics 170A, or 170B. Elements of statistical analysis. Presentation and interpretation of data; descriptive statistics; theory of probability and basic sampling distributions; statistical inference, including principles of estimation and tests of hypotheses; introduction to regression and correlation. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
(Formerly numbered M12.) Lecture, four hours; discussion, one hour; laboratory, one hour. Not open for credit to students with credit for course 10, 11, or 13 (or former Statistics M12, Anthropology M80, Geography M40, or Sociology M18). Introduction to statistical thinking and understanding, with emphasis on techniques used in geography and environmental science. Underlying logic behind statistical procedures, role of variation in statistical thinking, strengths and limitations of statistical summaries, and fundamental inferential tools. Emphasis on applications in geography and environmental science in laboratory work using professional statistical analysis package, including spatial statistics. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Lecture, three hours; discussion, one hour; laboratory, one hour. Not open for credit to students with credit for course 10, 10H, 11, 12, or 14. Presentation and interpretation of data, descriptive statistics, introduction to correlation and regression and to basic statistical inference (estimation, testing of means and proportions, ANOVA) using both bootstrap methods and parametric models. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Lecture, three hours; discussion, one hour; laboratory, one hour. Requisite: Mathematics 31A. Not open for credit to students with credit for course 10, 10H, 11, 12, or 13. Introduction to conceptual and technical aspects of statistics, with attention to applications of physical sciences and engineering. Topics include data collection and experimental design, quantifying uncertainty in measurement, descriptive statistics, introduction to time series and regression. Laboratory component to learn data analysis on real data and fundamental techniques of computer statistical analysis, including bootstrap methods. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Seminar, one hour. Discussion of and critical thinking about topics of current intellectual importance, taught by faculty members in their areas of expertise and illuminating many paths of discovery at UCLA. P/NP grading.
Score: 8.484722 Details | Listing | Web page
(Formerly numbered 35.) Lecture, three hours; discussion, one hour. Not open for credit to students with credit for course 35B. Basic introductory probability topics in interactive problem-driven manner. Various applets, interfaces, and demonstrations used to illustrate fundamental properties of distributions, random number generation, combinatorics, expectation, variability, and sampling. Assignment of projects that require light computer programming. Emphasis on practical description, utilization, and graphical presentation of various probabilistic modeling techniques. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Lecture, three hours; discussion, one hour. Not open for credit to students with credit for course 35A. Exploration of some main topics in introductory probability theory, especially discrete probability problems, that are useful in wide variety of scientific applications. Topics include conditional probability and conditional expectation, combinatorics, laws of large numbers, central limit theorem, Bayes theorem, univariate distributions, Markov processes, and Brownian motion. Examination of computer simulation in depth and discussion of computational approximations of solutions to complex problems using R, with examples of situations and concepts that arise naturally when playing Texas Hold'em and other games. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
(Formerly numbered 34.) Lecture, three hours; discussion, one hour. Designed for lower division students in social or life sciences and those who plan to major in Statistics. Topics include methods of sampling from finite populations, sources of sampling and estimation bias, and methods of generating efficient and precise estimates of population characteristics. Practical applications of sampling methods via lectures and hands-on laboratory exercises. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Seminar, two hours. Requisite: one course from 10, 10H, 11, 12, 13, or 14. Limited to 20 lower division students. Readings and discussions designed to introduce students to current statistical consulting research and fieldwork disciplines. Culminating project may be required. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Seminar, three hours. Limited to 20 students. Designed as adjunct to lower division lecture course. Exploration of topics in greater depth through supplemental readings, papers, or other activities and led by lecture course instructor. May be applied toward honors credit for eligible students. Honors content noted on transcript. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Tutorial, three hours. Limited to students in College Honors Program. Designed as adjunct to lower division lecture course. Individual study with lecture course instructor to explore topics in greater depth through supplemental readings, papers, or other activities. May be repeated for maximum of 4 units. Individual honors contract required. Honors content noted on transcript. Letter grading.
Score: 8.484722 Details | Listing | Web page
Tutorial (supervised research or other scholarly work), three hours per week per unit. Entry-level research for lower division students under guidance of faculty mentor. Students must be in good academic standing and enrolled in minimum of 12 units (excluding this course). Individual contract required; consult Undergraduate Research Center. May be repeated. P/NP grading.
Score: 8.484722 Details | Listing | Web page
Lecture, three hours; discussion, one hour. Requisites: Mathematics 32B, 33A. Not open to students with credit for Electrical Engineering 131A or Mathematics 170A; open to graduate students. Students may receive credit for only two of following: courses 100A, 110A, Biostatistics 100A. Probability distributions, random variables, vectors, and expectation. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Lecture, three hours; discussion, one hour. Requisite: course 100A or Mathematics 170A. Survey sampling, estimation, testing, data summary, one- and two-sample problems. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Lecture, three hours; discussion, one hour. Requisite: course 100B. Theory of linear models, with emphasis on matrix approach to linear regression. Topics include model fitting, extra sums of squares principle, testing general linear hypothesis in regression, inference procedures, Gauss/Markov theorem, examination of residuals, principle component regression, stepwise procedures. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Lecture, three hours; discussion, one hour. Requisites: one course from 10, 11, 12, 13, or 14, and Mathematics 32B. Fundamentals of collecting data, including components of experiments, randomization and blocking, completely randomized design and ANOVA, multiple comparisons, power and sample size, and block designs. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
(Formerly numbered 120A.) Lecture, three hours; discussion, one hour. Requisites: Mathematics 3B or 31B, or Mathematics 32B and 33A. Recommended: course 110A. Designed for juniors/seniors. Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and generalized linear model (e.g., logistic regression). Special attention to modern extensions of regression, including regression diagnostics, graphical procedures, and bootstrapping for statistical influence. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
(Formerly numbered 120B.) Lecture, three hours; discussion, one hour. Enforced requisite: course 101B. Designed for juniors/seniors. Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and generalized linear model (e.g., logistic regression). Special attention to modern extensions of regression, including regression diagnostics, graphical procedures, and bootstrapping for statistical influence. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
(Formerly numbered 135.) Lecture, three hours. Requisites: Mathematics 3B or 31B, or Mathematics 32B and 33A. Introduction to programming and data analysis in R. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Score: 8.484722 Details | Listing | Web page
Lecture, three hours; discussion, one hour. Requisite: course 100B. Introduction to Markov chain Monte Carlo (MCMC) algorithms for scientific computing. Generation of random numbers from specific distribution. Rejection and importance sampling and its role in MCMC. Markov chain theory and convergence properties. Metropolois and Gibbs sampling algorithms. Extensions as simulated tempering. Theoretical understanding of methods and their implementation in concrete computational problems. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Lecture, three hours; discussion, one hour. Requisite: course 100A or Electrical Engineering 131A or Mathematics 170A. Foundation of basic concepts and techniques of statistics. Topics include sampling distributions, statistical estimation (including maximum likelihood estimation), statistical intervals, and hypothesis testing, with emphasis on application of these concepts. Discussion of methods for checking whether assumptions required for mathematical foundations are appropriate for given set of data. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page
Lecture, three hours; discussion, one hour. Requisites: course 35A or 35B or 35C and Mathematics 3B or 31B, or Mathematics 32B and 33A. Not open to students with credit for Electrical Engineering 131A. Students may receive credit for only two of following: courses 100A, 110A, Biostatistics 100A. Probability, distributions, expectation, estimation, central limit theorem, confidence intervals, testing. P/NP or letter grading.
Score: 8.484722 Details | Listing | Web page