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1 - 2 of 2 results for: STATS305A

STATS 305A: Introduction to Statistical Modeling

Review of univariate regression. Multiple regression. Geometry, subspaces, orthogonality, projections, normal equations, rank deficiency, estimable functions and Gauss-Markov theorem. Computation via QR decomposition, Gramm-Schmidt orthogonalization and the SVD. Interpreting coefficients, collinearity, graphical displays. Fits and the Hat matrix, leverage & influence, diagnostics, weighted least squares and resistance. Model selection, Cp/Aic and crossvalidation, stepwise, lasso. Basis expansions, splines. Multivariate normal distribution theory. ANOVA: Sources of measurements, fixed and random effects, randomization. Emphasis on problem sets involving substantive computations with data sets. Prerequisites: consent of instructor, 116, 200, applied statistics course, CS 106A, MATH 114. (NB: prior to 2016-17 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Aut | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Palacios, J. (PI)

STATS 305C: Methods for Applied Statistics II: Applied Multivariate Statistics

Theory, computational aspects, and practice of a variety of important multivariate statistical tools for data analysis. Topics include classical multivariate Gaussian and undirected graphical models, graphical displays. PCA, SVD and generalizations including canonical correlation analysis, linear discriminant analysis, correspondence analysis, with focus on recent variants. Factor analysis and independent component analysis. Topic modeling. Multidimensionalnscaling and its variants (e.g. Isomap, spectral clustering). Matrix completion. nStudents will be expected to program - ideally in R. Prerequisites: Stats305a and Stats 305b or equivalent. (NB: prior to 2016-17 the 305ABC series was numbered as 305, 306A and 306B).
Terms: Spr | Units: 3 | Grading: Letter or Credit/No Credit
Instructors: Hastie, T. (PI)
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