COLLEGE OF ARTS & SCIENCES STATISTICS (2022)

STAT 221 Statistical Concepts and Methods for the Social Sciences (5) NSc, RSN
Develops statistical literacy. Examines objectives & pitfalls of statistical studies; study designs, data analysis, inference; graphical & numerical summaries of numerical &categorical data; correlation and regression; estimation, confidence intervals, & significance tests. Emphasizes social science examples and cases. May only receive credit for one of STAT 220, STAT 221/CS&SS 221/SOC 221, or STAT 290. Offered: jointly with CS&SS 221/SOC 221; AWSp.
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STAT 302 Statistical Computing (3)
An introduction to the foundations of statistical computing and data analysis. Topics include programming fundamentals, data cleaning, data visualization, debugging, and version control. Topics are motivated by methods in statistics and machine learning. Taught using the R programming language. Prerequisite: either STAT 311, STAT 390, or Q SCI 381; recommended: previous coursework in R programming language.
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STAT 303 Introduction to the Ethics of Algorithmic Decision Making (3) DIV
Ethical and social implications of design, implementation, and interpretation of statistical decision-making algorithms. Examples from medicine, education, and criminal justice. Examines how algorithms interact with social categories including race, class, and gender - preserving or reshaping existing inequities. Evaluates statistical frameworks for balancing fairness and privacy with efficiency.
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STAT 311 Elements of Statistical Methods (5) NSc, RSN
Elements of good study design. Descriptive statistics including correlation and regression. Introductory concepts of probability and sampling; binomial and normal distributions. Basic concepts of hypothesis testing, estimation, and confidence intervals; t-tests and chi-square tests. Experience with computer software. Prerequisite: either STAT 220, STAT 221/CS&SS 221/SOC 221, STAT 290, MATH 120, MATH 124, MATH 125, MATH 126, MATH 134, MATH 135, MATH 136, Q SCI 190, or QMETH 201. Offered: AWSpS.
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STAT 321 Data Science and Statistics for Social Sciences I (5) I&S, QSR
Introduction to applied data analysis for social scientists. Focuses on using programming to prepare, explore, analyze, and present data that arise in social science research. Data science topics include loading, cleaning, and exploring data, basic visualization, reproducible research practices. Statistical topics include measurement, probability, modeling, assessment of statistical evidence. Lectures intermixed with programming and lab sessions. Offered: jointly with CS&SS 321/SOC 321; W.
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STAT 340 Introduction to Probability and Mathematical Statistics I (4) RSN
Fundamentals of probability for statistics; axioms of probability, conditional and joint probability, independence; random variables, univariate and multivariate distributions and densities, moments, and moment generating functions; binomial, negative binomial, geometric, Poisson, uniform, normal, exponential distributions; and transformations of a random variable. Prerequisite: either MATH 126 or MATH 136; and either STAT 311, STAT 390/MATH 390, or Q SCI 381. Offered: A.
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STAT 341 Introduction to Probability and Mathematical Statistics II (4) NSc
Brief review of: sample spaces, random variables, probability. Distribution: binomial, normal, Poisson, geometric. Followed by: expectation, variance, central limit theorem. Basic concepts of estimation, testing, and confidence intervals. Maximum likelihood estimators and likelihood ratio tests, efficiency. Introduction to regression. Prerequisite: either STAT 340, or STAT 395/MATH 395; and either STAT 311, STAT 390, or Q SCI 381. Offered: W.
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STAT 342 Introduction to Probability and Mathematical Statistics III (4) NSc
Brief review of: sample spaces, random variables, probability. Distribution: binomial, normal, Poisson, geometric. Followed by: expectation, variance, central limit theorem. Basic concepts of estimation, testing, and confidence intervals. Maximum likelihood estimators and likelihood ratio tests, efficiency. Introduction to regression. Prerequisite: STAT 341. Offered: Sp.
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STAT 390 Statistical Methods in Engineering and Science (4) NSc
Concepts of probability and statistics. Conditional probability, independence, random variables, distribution functions. Descriptive statistics, transformations, sampling errors, confidence intervals, least squares and maximum likelihood. Exploratory data analysis and interactive computing. Cannot be taken for credit if credit received for STAT509/CS&SS 509/ECON 580. Prerequisite: either MATH 126 or MATH 136. Offered: AWSpS.
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STAT 391 Quantitative Introductory Statistics for Data Science (4)
The basic concepts of statistics, machine learning and data science, as well as their computational aspects. Statistical models, likelihood, maximum likelihood and Bayesian estimation, regression, classification, clustering, principal component analysis, model validation, statistical testing. Practical implementation and visualization in data analysis. Assumes knowledge of basic probability, mathematical maturity, and ability to program. Prerequisite: either CSE 312, or STAT 394/MATH 394 and STAT 395/MATH 395. Offered: Sp.
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STAT 396 Finite Markov Chains and Monte-Carlo Methods (3) NW
Finite Markov chains; stationary distributions; time reversals; classification of states; classical Markov chains; convergence in total variation distance and L2; spectral analysis; relaxation time; Monte Carlo techniques: rejection sampling, Metropolis-Hastings, Gibbs sampler, Glauber dynamics, hill climb and simulated annealing; harmonic functions and martingales for Markov chains. Prerequisite: a minimum grade of 2.0 in MATH 208; and either a minimum grade of 2.0 in MATH 394/STAT 394 and STAT 395/MATH 395, or a minimum grade of 2.0 in STAT 340 and STAT 341, or a minimum grade of 2.0 in STAT 340 and STAT 395/MATH 395. Offered: jointly with MATH 396; Sp.
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STAT 403 Introduction to Resampling Inference (4) NSc
Introduction to computer-intensive data analysis for experimental and observational studies in empirical sciences. Students design, program, carry out, and report applications of bootstrap resampling, rerandomization, and subsampling of cases. Experience programming in R is beneficial. Credit allowed for STAT 403 or STAT 503 but not both. Prerequisite: either STAT 311, STAT 341, STAT 390/MATH 390, or Q SCI 381 and Q SCI 482. Offered: jointly with Q SCI 403; Sp.
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STAT 406 Research Design and Statistics for HIHIM (3)
Explores healthcare and research statistics. Addresses hospital statistics, used to calculate usage levels of heathcare resources and outcomes of clinical operations, and research statistics, used to summarize and describe significant characteristics of a data set, and to make inferences about a population based on data collected from a sample. In addition, principles of research are described, including the Institutional Review Board process. Offered: jointly with BIOST 406/HIHIM 425.
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STAT 416 Introduction to Machine Learning (4) NW
Provides practical introduction to machine learning. Modules include regression, classification, clustering, retrieval, recommender systems, and deep learning, with a focus on an intuitive understanding grounded in real-world applications. Intelligent applications are designed and used to make predictions on large, complex datasets. Prerequisite: either CSE 123, CSE 143, CSE 160, or CSE 163; and either STAT 311, STAT 390, STAT 391, IND E 315, MATH 394/STAT 394, STAT 395/MATH 395, or Q SCI 381. Offered: jointly with CSE 416.
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STAT 421 Applied Statistics and Experimental Design (4) NSc
Experimental designs, including completely randomized, blocked, Latin Square, factorial, 2 to the k, fractional, nested, and split-plot; fixed effects and random effects models; confounding and aliasing. Analyses of real data, to illustrate concepts. Prerequisite: STAT 342. Offered: A.
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STAT 480 Sampling Theory for Biologists (3) NW
Theory and applications of sampling finite populations including: simple random sampling, stratified random sampling, ratio estimates, regression estimates, systematic sampling, cluster sampling, sample size determinations, applications in fisheries and forestry. Other topics include sampling plant and animal populations, sampling distributions, estimation of parameters and statistical treatment of data. Prerequisite: Q SCI 482. Offered: jointly with Q SCI 480; W, odd years.
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STAT 486 Experimental Design (4) NW
Emphasizes data modeling using structured means resulting from choice of experimental and treatment design. Examines experimental designs, including crossed, nested designs; block; split-plot designs; and covariance analysis. Also covers multiple comparisons, efficiency, power, sample size, and pseudo-replication. Prerequisite: Q SCI 482. Offered: jointly with Q SCI 486; W, even years.
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STAT 491 Introduction to Stochastic Processes (3) NW
Random walks, Markov chains, branching processes, Poisson process, point processes, birth and death processes, queuing theory, stationary processes. Prerequisite: minimum grade of 2.0 in MATH 394/STAT 394 and MATH 395/STAT 395, or minimum grade of 2.0 in STAT 340 and STAT 341 and MATH 396/STAT 396. Offered: jointly with MATH 491; A.
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STAT 492 Introduction to Stochastic Processes II (3)
Introduces elementary continuous-time discrete/continuous-state stochastic processes and their applications. Covers useful classes of continuous-time stochastic processes (e.g., Poisson process, renewal processes, birth and birth-and-death processes, Brownian motion, diffusion processes, and geometric Brownian motion) and shows how useful they are for solving problems of practical interest. Prerequisite: a minimum grade of 2.0 in MATH 491/STAT 491. Offered: jointly with MATH 492.
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STAT 493 Stochastic Calculus for Option Pricing (3) NW
Introductory stochastic calculus mathematical foundation for pricing options and derivatives. Basic stochastic analysis tools, including stochastic integrals, stochastic differential equations, Ito's formula, theorems of Girsanov and Feynman-Kac, Black-Scholes option pricing, American and exotic options, bond options. Prerequisite: minimum grade of 2.0 in either STAT 395/MATH 395, or a minimum grade of 2.0 in STAT 340 and STAT 341. Offered: jointly with MATH 493.
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STAT 503 Practical Methods for Data Analysis (4)
Basic exploratory data analysis with business examples. Data summaries, multivariate data, time series, multiway tables. Techniques include graphical display, transformation, outlier identification, cluster analysis, smoothing, regression, robustness. Departmental credit allowed for only one of 403 and 503. Prerequisite: B A 500 or QMETH 500 or equivalent or permission of instructor. Offered: jointly with QMETH 503.
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STAT 504 Applied Regression (4)
Least squares estimation. Hypothesis testing. Interpretation of regression coefficients. Categorical independent variables. Interactions. Assumption violations: outliers, residuals, robust regression; nonlinearity, transformations, ACE, CART; nonconstant variance. Variable selection and model averaging. Prerequisite: either STAT 342, STAT 390/MATH 390, STAT 421, STAT 509/CS&SS 509/ECON 580, or SOC 505. Offered: jointly with CS&SS 504.
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STAT 509 Econometrics I: Introduction to Mathematical Statistics (4)
Examines methods, tools, and theory of mathematical statistics. Covers, probability densities, transformations, moment generating functions, conditional expectation. Bayesian analysis with conjugate priors, hypothesis tests, the Neyman-Pearson Lemma. Likelihood ratio tests, confidence intervals, maximum likelihood estimation, Central limit theorem, Slutsky Theorems, and the delta-method. Prerequisite: STAT 311/ECON 311; either MATH 136 or MATH 126 with either MATH 308 or MATH 309. (Credit allowed for only one of STAT 390, STAT 481, and ECON 580.) Offered: jointly with CS&SS 509/ECON 580.
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STAT 512 Statistical Inference (4)
Review of random variables; transformations, conditional expectation, moment generating functions, convergence, limit theorems, estimation; Cramer-Rao lower bound, maximum likelihood estimation, sufficiency, ancillarity, completeness. Rao-Blackwell theorem. Hypothesis testing: Neyman-Pearson lemma, monotone likelihood ratio, likelihood-ratio tests, large-sample theory. Contingency tables, confidence intervals, invariance. Decision theory. Prerequisite: STAT 395 and STAT 421, STAT 423, STAT 504, or BIOST 512 (concurrent registration permitted for these three). Offered: A.
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STAT 513 Statistical Inference (4)
Review of random variables; transformations, conditional expectation, moment generating functions, convergence, limit theorems, estimation; Cramer-Rao lower bound, maximum likelihood estimation, sufficiency, ancillarity, completeness. Rao-Blackwell theorem. Hypothesis testing: Neyman-Pearson lemma, monotone likelihood ratio, likelihood-ratio tests, large-sample theory. Contingency tables, confidence intervals, invariance. Decision theory. Prerequisite: STAT 512. Offered: W.
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STAT 520 Spectral Analysis of Time Series (4)
Estimation of spectral densities for single and multiple time series. Nonparametric estimation of spectral density, cross-spectral density, and coherency for stationary time series, real and complex spectrum techniques. Bispectrum. Digital filtering techniques. Aliasing, prewhitening. Choice of lag windows and data windows. Use of the fast Fourier transform. Prerequisite: either STAT 342, STAT 390, STAT 509/CS&SS 509/ECON 580, or IND E 315. Offered: jointly with E E 520.
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STAT 524 Design of Medical Studies (3)
Design of medical studies, with emphasis on randomized controlled clinical trials. Bias elimination, controls, treatment assignment and randomization, precision, replication, power and sample size calculations, stratification, and ethics. Suitable for graduate students in biostatistics and for research-oriented graduate students in other scientific fields. Prerequisite: BIOST 511 or equivalent, and one of BIOST 513, BIOST 518, STAT 421, STAT 423, STAT 512, or EPI 512; or permission of instructor. Offered: jointly with BIOST 524; Sp.
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STAT 529 Sample Survey Techniques (3)
Design and implementation of selection and estimation procedures. Emphasis on human populations. Simple, stratified, and cluster sampling; multistage and two-phase procedures; optimal allocation of resources; estimation theory; replicated designs; variance estimation; national samples and census materials. Prerequisite: either STAT 421, STAT 423, STAT 504, QMETH 500, BIOST 511, or BIOST 517, or equivalent; or permission of instructor. Offered: jointly with BIOST 529/CS&SS 529.
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STAT 536 Analysis of Categorical and Count Data (3)
Analysis of categorical data in the social sciences. Binary, ordered, and multinomial outcomes, event counts, and contingency tables. Focuses on maximum likelihood estimations and interpretations of results. Prerequisite: SOC 504, SOC 505, SOC 506, or equivalent. Offered: jointly with CS&SS 536/SOC 536.
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STAT 538 Statistical Learning: Modeling, Prediction, and Computing (3)
Reviews optimization and convex optimization in its relation to statistics. Covers the basics of unconstrained and constrained convex optimization, basics of clustering and classification, entropy, KL divergence and exponential family models, duality, modern learning algorithms like boosting, support vector machines, and variational approximations in inference. Prerequisite: experience with programming in a high level language. Offered: W.
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STAT 547 Options and Derivatives (4)
Covers theory, computation, and statistics of options and derivatives pricing, including options on stocks, stock indices, futures, currencies, and interest rate derivatives. Prerequisite: STAT 506 or permission of instructor.
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STAT 548 Machine Learning for Big Data (4)
Covers machine learning and statistical techniques for analyzing datasets of massive size and dimensionality. Representations include regularized linear models, graphical models, matrix factorization, sparsity, clustering, and latent factor models. Algorithms include sketching, random projections, hashing, fast nearest-neighbors, large-scale online learning, and parallel learning (Map-Reduce, GraphLab). Prerequisite: either STAT 535 or CSE 546. Instructors: Fox, Guestrin Offered: jointly with CSE 547; W.
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STAT 549 Statistical Methods for Portfolios (4)
Covers the fundamentals of modern statistical portfolio construction and risk measurement, including theoretical foundations, statistical methodology, and computational methods using modern object-oriented software for data analysis, statistical modeling, and numerical portfolio optimization. Prerequisite: ECON 424 or equivalent, or permission of instructor.
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STAT 566 Causal Modeling (4)
Construction of causal hypotheses. Theories of causation, counterfactuals, intervention vs. passive observation. Contexts for causal inference: randomized experiments; sequential randomization; partial compliance; natural experiments, passive observation. Path diagrams, conditional independence, and d-separation. Model equivalence and causal under-determination. Prerequisite: course in statistics, SOC 504, SOC 505, SOC 506, or equivalent. Offered: jointly with CS&SS 566.
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STAT 570 Advanced Regression Methods for Independent Data (4)
Covers linear models, generalized linear and non-linear regression, and models. Includes interpretation of parameters, including collapsibility and non-collapsibility, estimating equations; likelihood; sandwich estimations; the bootstrap; Bayesian inference: prior specification, hypothesis testing, and computation; comparison of approaches; and diagnostics. Prerequisite: STAT 512 and STAT 513; either BIOST 533/STAT 533, or STAT 502 and STAT 504/CS&SS 504; and a course in matrix algebra. Offered: jointly with BIOST 570; A.
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STAT 571 Advanced Regression Methods for Dependent Data (4)
Covers longitudinal data models, generalized linear and non-linear mixed models; marginal versus conditional models; generalized estimating equations, likelihood-based inference, REML, BLUP, and computation of integrals; Bayesian inference: Markov chain Monte Carlo; covariance models, including models for split plot designs; comparison of approaches; and diagnostics. Prerequisite: BIOST570/STAT 570. Offered: jointly with BIOST 571; W.
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STAT 576 Statistical Methods for Survival Data (3)
Statistical methods for censored survival data arising from follow-up studies on human or animal populations. Parametric and nonparametric methods, Kaplan-Meier survival curve estimator, comparison of survival curves, log-rank test, regression models including the Cox proportional hazards model, competing risks. Prerequisite: STAT 581 and either BIOST 515, STAT 473, or equivalent. Offered: jointly with BIOST 576.
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STAT 581 Advanced Theory of Statistical Inference I (4)
Foundations of parametric statistics: elementary decision theory, Bayesian methods, modes of convergence, central limit theorems, delta method, maximum likelihood estimation, regularity, hypothesis testing under fixed and local alternatives, parametric efficiency theory. Prerequisite: STAT 513. ; recommended: mathematical analysis from a course at the level of either MATH 426 or STAT 559. Offered: jointly with BIOST 583; A.
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STAT 582 Advanced Theory of Statistical Inference II (4)
Semiparametric and nonparametric estimation of irregular parameters: minimax rates of convergence, kernel methods, bias-variance tradeoff, concentration inequalities, empirical risk minimization, Rademacher complexity, Vapnik-Chervonenkis dimension, covering and bracketing numbers, empirical process theory (Glivenko-Cantelli results). Prerequisite: STAT 581/BIOST 583. ; recommended: mathematical analysis from a course at the level of either MATH 426 or STAT 559. Offered: jointly with BIOST 584; W.
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STAT 583 Advanced Theory of Statistical Inference (3)
Semiparametric and nonparametric estimation of regular parameters: weak convergence, empirical process theory (Donsker results), asymptotic linearity, estimating equations, U-statistics, functional delta method, efficiency theory, construction of efficient estimators. Prerequisite: STAT 582/BIOST 584. ; recommended: mathematical analysis from a course at the level of either MATH 426 or STAT 559. Offered: jointly with BIOST 585; Sp.
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STAT 598 Techniques of Statistical Consulting (1)
Seminar series covering technical and non-technical aspects of statistical consulting, including skills for effective communication with clients, report writing, statistical tips and rules of thumb, issues in survey sampling, and issues in working as a statistical consultant in academic, industrial, and private-practice settings. Prerequisite: entry code. Offered: jointly with BIOST 598; ASp.
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