Courses

Detailed course offerings (Time Schedule) are available for:

STAT 100 Numbers and Reason (5) QSR
Surveys the standard ways in which "arithmetic turns into understanding" across examples from the natural and the social sciences. Main concepts include abduction (inference to the best explanation), consilience (numerical agreement across multiple measurement levels), bell curves, linear models, and the likelihood of hypothesis.

Prerequisite: None. Offered: A.
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STAT 111 Lectures in Applied Statistics (1) NW
Weekly lectures illustrating the importance of statisticians in a variety of fields, including medicine and the biological, physical, and social sciences. Credit/no-credit only.

Prerequisite: None. Offered: jointly with BIOST 111; Sp.
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STAT 180 Introduction to Data Science (4) QSR
Survey course introducing the essential elements of data science: data collection, management, curation, and cleaning; summarizing and visualizing data; basic ideas of statistical inference, machine learning. Students will gain hands-on experience through computing labs.

Prerequisite: Minimum grade of 2.5 in MATH 098, or a minimum grade of 3.0 in MATH 103, or a score of 151-169 on the MPT-GS placement test, or a score of 145-153 on the MPT-AS placement test. Offered: AWSp.
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STAT 220 Principles of Statistical Reasoning (5) NW, QSR  (Students may receive credit for only one of STAT 220, STAT 221/CS&SS 221/SOC 221, and STAT 311.)
Introduces statistical reasoning. Focuses primarily on the what and why rather than the how. Helps students gain an understanding of the rationale behind many statistical methods, as well as an appreciation of the use and misuse of statistics. Encourages and requires critical thinking.

Prerequisite: None. Offered: AWSpS.
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STAT 221 Statistical Concepts and Methods for the Social Sciences (5) NW, QSR (Students may receive credit for only one of STAT 220, STAT 221/CS&SS 221/SOC 221, and STAT 311.)
Develops statistical literacy. Examines objectives and pitfalls of statistical studies; study designs, data analysis, inference; graphical and numerical summaries of numerical and categorical data; correlation and regression; and estimation, confidence intervals, and significance tests. Emphasizes social science examples and cases.

Prerequisite: None. Offered: jointly with CS&SS 221/SOC 221; AWSp.
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STAT 302 Statistical Software and Its Applications (3)
Introduction to data structures and basics of implementing procedures in statistical computing packages, selected from but not limited to R, SAS, STATA, MATLAB, SPSS, and Minitab. Provides a foundation in computation components of data analysis.

Prerequisite: either STAT 311 or STAT 390. Offered: W.
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STAT 311 Elements of Statistical Methods (5) NW, QSR (Students may receive credit for only one of STAT 220, STAT 221/CS&SS 221/SOC 221, and STAT 311.)
Elementary concepts of probability and sampling; binomial and normal distributions. Basic concepts of hypothesis testing, estimation, and confidence intervals; t-tests and chi-square tests. Linear regression theory and the analysis of variance. 

Prerequisite:  QSCI 190, or MATH 120, or MATH 124, or MATH 125, or MATH 126, or MATH 135 or MATH 136 or a score of 154-163 on the MPT-AS placement test. Offered: AWSpS.
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STAT 316 Design of Experiments and Regression Analysis (4) NW
Introduction to the analysis of data from planned experiments. Analysis of variance for multiple factors and applications of orthogonal arrays and linear graphs for fractional factorial designs to product and process design optimization. Regression analysis with applications in engineering.

Prerequisite: IND E 315. Offered: jointly with IND E 316; W.
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STAT 320 Evaluating Social Science Evidence (5) I&S, QSR
A critical introduction to the methods used to collect data in social science: surveys, archival research, experiments, and participant observation. Evaluates "facts and findings" by understanding the strengths and weaknesses of the methods that produce them. Case based.

Prerequisite: None. Offered: jointly with CS&SS 320/SOC 320.
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STAT 321 Case-Based Social Statistics I (5) I&S, QSR
Introduction to statistical reasoning for social scientists. Built around cases representing in-depth investigations into the nature and content of statistical and social-science principles and practice. Hands-on approach: weekly data-analysis laboratory. Fundamental statistical topics: measurement, exploratory data analysis, probabilistic concepts, distributions, assessment of statistical evidence.

Prerequisite: None. Offered: jointly with CS&SS 321/SOC 321.
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STAT 322 Case-Based Social Statistics II (5) I&S, QSR
Continuation of CS&SS 321/SOC 321/STAT 321. Progresses to questions of assessing the weight of evidence and more sophisticated models including regression-based methods. Built around cases investigating the nature and content of statistical principles and practice. Hands-on approach: weekly data analysis laboratory.

Prerequisite: CS&SS/SOC/STAT 321, or permission of instructor. Offered: jointly with CS&SS/SOC 322.
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STAT 340 Introduction to Probability and Mathematical Statistics I (4) QSR
Covers the fundamentals of probability and mathematical statistics; axioms of probability, conditional and joint probability, random variables, univariate and multivariate distributions and densities, and moments; bionomial, negative binomial, geometric, Poisson, normal, exponential distributions, and central limit theorem; and basic estimation and hypothesis testing theory.

Prerequisite: STAT 311 or STAT 390; AND a minimum grade of 2.5 in either MATH 327 or MATH 136. Offered: A.
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STAT 341 Introduction to Probability and Mathematical Statistics II (4) NW
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:  Minimum of 2.0 in STAT 340; or STAT/MATH 394 and STAT/MATH 395. Offered: W.
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STAT 342 Introduction to Probability and Mathematical Statistics III (4) NW
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: Minimum of 2.0 in STAT 341. Offered: Sp.
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STAT 390 Statistical Methods in Engineering and Science (4) NW (Students may receive credit for only one of STAT 390/STAT 509/CS&SS 509/ECON 580.)
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.

Prerequisite: 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:  CSE 312; or STAT/MATH 394 and STAT/MATH 395. Offered: Sp.
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STAT 394 Probability I (3) NW
Sample spaces; basic axioms of probability; combinatorial probability; conditional probability and independence; binomial, Poisson, and normal distributions.

Prerequisite: Minimum grade of 2.0 in  MATH 126 OR MATH 136. Offered: jointly with MATH 394; AWS.
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STAT 395 Probability II (3) NW
Random variables; expectation and variance; laws of large numbers; normal approximation and other limit theorems; multidimensional distributions and transformations.

Prerequisite: Minimum grade of 2.0 in STAT/MATH 394. Offered: jointly with MATH 395; WSpS.
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STAT 396 Probability III (3) NW
Characteristic functions and generating functions; recurrent events and renewal theory; random walk.

Prerequisite: Minimum grade of 2.0 in MATH/STAT 395; or minimum grade of 2.0 in STAT 340 and in STAT 341. Offered: jointly with MATH 396; Sp.
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STAT 403 Introduction to Resampling Inference (4) NW (Students may receive credit for only one of STAT 403 or STAT 503.)

Introduction to computer-intensive data analysis for experimental and observational studies in empirical sciences. Students design, program, carry out, and report applications of bootstrap re-sampling, re-randomization, and sub-sampling of cases. Experience with programming in R is beneficial.

Prerequisite: STAT 311; or  STAT 341; or STAT 390; or STAT/CS&SS 509; or both 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.

Prerequisite: None. Offered: jointly with BIOST 406/HIHIM 425.
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STAT 416 Introduction to Machine Learning (4) NW
Provides practical introduction to machines 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: CSE 143 or CSE 160; and either STAT 311 or STAT 390 Offered: jointly with CSE 416.
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STAT 421 Applied Statistics and Experimental Design (4) NW
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 or STAT/CS&SS 509/ECON 580. Offered: A.
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STAT 423 Applied Regression and Analysis of Variance (4) NW
Least squares; Simple/multiple linear regression including interpretation; Variable selection; Analysis of covariance; Assumptions and diagnostics/remedies; Weighting and generalized least squares; Hypothesis testing. Analyses of real data to illustrate concepts.

Prerequisite: STAT 342 or STAT 509. Offered: W.
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STAT 425 Introduction to Nonparametric Statistics (3) NW
Overview of non-parametric methods, such as rank tests, goodness of fit tests, 2 x 2 tables. Useful for students with only a statistical methods course background.

Prerequisite: STAT 311 and STAT 340; or STAT 390; or STAT 391. Offered: jointly with BIOST 425.
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STAT 427 Introduction to Analysis of Categorical Data (4) NW
Techniques for analysis of count data. Log-linear models, logistic regression, and analysis of ordered response categories. Illustrations from the behavioral and biological sciences. Computational procedures.

Prerequisite:  STAT 342 or STAT 421.
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STAT 428 Multivariate Analysis for the Social Sciences (4) NW
Multivariate techniques commonly used in the social and behavioral sciences. Linear models for dependence analysis (multivariate regression, MANOVA, and discriminant analysis) and for interdependence analysis (principal components and factor analysis). Techniques applied to social science data using computer statistical packages.

Prerequisite: STAT 342 or STAT 421.
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STAT 435 Introduction to Statistical Machine Learning (4)
Introduces the theory and application of statistical machine learning. Topics may include supervised versus unsupervised learning; cross-validation; the bias-variance trade-off; regression and classification; regularization and shrinkage approaches; non-linear approaches; tree-based methods; and support vector machines. Includes applications in R.

Prerequisite: STAT 341, or STAT 390, or STAT 391; recommended: MATH 308. Offered: Sp.
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STAT 441 Multivariate Statistical Methods (4) QSR
Introduces statistical methods for analysis of multidimensional data. Methods include tools for exploratory analysis of high-dimensional data, statistical modeling approaches to parameter estimation and hypothesis testing, and nonparametric methods for classification and clustering. Includes applications in R.

Prerequisite: MATH 308 and one of STAT 341, STAT 390, or STAT 391. Offered: W.
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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/STAT 394 and MATH/STAT 395, or minimum grade of 2.0 in STAT 340 and STAT 341 and MATH/STAT 396. Offered: jointly with MATH 491; A.
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STAT 492 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 STAT 395/MATH 395, or a minimum grade of 2.0 in STAT 340 and STAT 341. Offered: jointly with MATH 492; W.
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STAT 495 Service Learning: K-12 Tutoring Experience (1-5, max. 5)
Tutoring mathematics in local K-12 schools. Offered: AWSp.
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STAT 498 Special Topics (1-5, max. 15) NW
Reading and lecture course intended for special needs of students.
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STAT 499 Undergraduate Research (1-5, max. 15)
Offered: AWSpS.
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