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Statistics 10: Introduction to Statistical Reasoning
5.00 Credits
University of California-Los Angeles
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.
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Statistics 100A: Introduction to Probability
4.00 Credits
University of California-Los Angeles
Lecture, three hours; discussion, one hour. Recommended preparation: course 35A or 35B or 35C. 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.
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Statistics 100A - Introduction to Probability
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Statistics 100B: Introduction to Mathematical Statistics
4.00 Credits
University of California-Los Angeles
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.
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Statistics 100B - Introduction to Mathematical Statistics
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Statistics 100C: Linear Models
4.00 Credits
University of California-Los Angeles
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.
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Statistics 100C - Linear Models
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Statistics 101A: Introduction to Design and Analysis of Experiment
4.00 Credits
University of California-Los Angeles
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.
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Statistics 101A - Introduction to Design and Analysis of Experiment
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Statistics 101B: Introduction to Data Analysis and Regression
4.00 Credits
University of California-Los Angeles
Lecture, three hours; discussion, one hour. Requisites: course 35A or 35B or 35C and 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.
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Statistics 101B - Introduction to Data Analysis and Regression
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Statistics 101C: Introduction to Regression and Data Mining
4.00 Credits
University of California-Los Angeles
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.
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Statistics 101C - Introduction to Regression and Data Mining
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Statistics 102A: Introduction to Computational Statistics with R
4.00 Credits
University of California-Los Angeles
Lecture, three hours. Requisites: course 35A or 35B or 35C and Mathematics 3B or 31B, or Mathematics 32B and 33A. Introduction to programming and data analysis in R. P/NP or letter grading.
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Statistics 102A - Introduction to Computational Statistics with R
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Statistics 102B: Matrix Computation and Optimization for Statistics
4.00 Credits
University of California-Los Angeles
Lecture, three hours. Requisite: one course from 10, 11, 12, 13, 14, 100A, or 110A. Introduction to those parts of matrix algebra and matrix computation that are most useful for statisticians. Use of computer exercises and R programming language. P/NP or letter grading.
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Statistics 102B - Matrix Computation and Optimization for Statistics
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Statistics 102C: Introduction to Monte Carlo Methods
4.00 Credits
University of California-Los Angeles
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.
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Statistics 102C - Introduction to Monte Carlo Methods
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