Undergraduate Course Descriptions

STAT 105: Introduction to Statistics. (3:3:0)
WHEN TAUGHT: Spring
PREREQUISITE: MATH 97 or equivalent.
DESCRIPTION: Fundamental ideas and applications of statistics.

STAT 121: Principles of Statistics. (3:3:1)
OFFERED: Independent Study also.
WHEN TAUGHT: Fall; Winter; Spring; Summer
RECOMMENDED: MATH 110 or equivalent.
DESCRIPTION: Stemplots, boxplots, histograms, scatterplots; central tendency, variability; confidence intervals and hypothesis testing involving one and two means and proportions; contingency tables, simple linear regression.

STAT 123: Introduction to R Programming.
WHEN TAUGHT: Fall Blk 1; Fall Blk 2; Winter Blk 1; Winter Blk 2
PREREQUISITE: STAT 121 or STAT 151 or STAT 201
DESCRIPTION: Base R programming skills, introductory statistical analysis and graphics, simulation of introductory statistical concepts.

STAT 124: SAS Certification 1. (1:1:1)
WHEN TAUGHT: Fall Blk 1; Fall Blk 2; Winter Blk 1; Winter Blk 2; Spring
PREREQUISITE: STAT 121; or STAT 151; or STAT 201; Stat 301.
RECOMMENDED: Concurrent enrollment in STAT 224.
DESCRIPTION: Base SAS programming skills, introductory statistical analysis and graphics, simulation of introductory statistical concepts.

STAT 199R: Academic Internship. (.5-3:ARR:ARR)
PREREQUISITE: Department internship coordinator's consent
DESCRIPTION: Work experience evaluated by supervisor and posted on student's transcript.

STAT 201: Statistics for Engineers and Scientists. (3:3:0)
WHEN TAUGHT: Fall; Winter
PREREQUISITE: MATH 112; or MATH 119
DESCRIPTION: The scientific method; probability, random variables, common discrete and continuous random variables, central limit theorem; confidence intervals and hypothesis testing; completely randomized experiments; factorial experiments.

Stat 223: Applied R Programming (1.5:1.5:1.5)
WHEN TAUGHT: Fall Blk 1; Fall Blk 2; Winter Blk 1; Winter Blk 2
PREREQUISITE: STAT 123 DESCRIPTION: Base R programming skills, introductory statistical analysis and graphics, simulation of introductory statistical concepts.

STAT 224: Statistical Computing 1. (2:0:2)
WHEN TAUGHT: Fall Blk 1; Fall Blk 2; Winter Blk 1; Winter Blk 2
PREREQUISITE: STAT 124 or concurrent enrollment.
DESCRIPTION: Statistical programming using the data step in SAS; basic Procs; Proc MEAN, SORT, TABULATE, SQL, and REPORT; ODS; simple MACROS.

STAT 230: Analysis of Variance. (3:3:0)
WHEN TAUGHT: Fall; Winter; Spring
PREREQUISITE: STAT 121 or STAT 201.
RECOMMENDED: Concurrent enrollment in MATH 112.
DESCRIPTION: Scientific method, statistical thinking, sources of variation, completely randomized design, ANOVA, power and sample size considerations, multiple testing, randomized complete blocks, factorial designs, interactions. Introduction to statistical software.

STAT 234: Methods of Survey Sampling. (3:3:2)
WHEN TAUGHT: Fall
PREREQUISITE: STAT 121 or STAT 201
DESCRIPTION: Sampling frames, questionnaire design; simple random, systematic, stratified, and cluster sampling methods, comparing domain means, contingency table analysis.

STAT 240: Discrete Probability. (3:3:0)
WHEN TAUGHT: Fall; Winter; Spring
PREREQUISITE: STAT 121 or STAT 201
DESCRIPTION: Set theory; discrete probability; conditional probability; finite sample spaces; discrete random variables (pdf, cdf, moments).

STAT 251: Introduction to Bayesian Statistics. (3:3:0)
WHEN TAUGHT: Fall; Winter; Spring-Summer
PREREQUISITE: STAT 12; STAT 240; MATH 113
DESCRIPTION: The scientific method; conditional probability; Bayes' Theorem; conjugate distributions: Beta-binomial, Poisson-gamma, normal-normal; Gibbs sampling.

STAT 274: Theory of Interest (3:3:0)
WHEN TAUGHT: Fall; Winter
PREREQUISITE: Math 112
DESCRIPTION: Theory of interest, annuities, amortization, financial derivatives, prepares for SOA Exam FM.

STAT 281: Introduction to Analytics (3:3:0)
WHEN TAUGHT: Fall
PREREQUISITE: STAT 121 or STAT 201
DESCRIPTION: Introduction to R, Rstudio, knitr, LaTeX, SQL, beamer, and Git. Build and access an SQL database. Propose and prepare written and oral presentations answering a question of interest to a non-technical decision maker.

STAT 330: Introduction to Regression. (3:3:0)
WHEN TAUGHT: Fall; Winter; Spring-Summer
PREREQUISITE: STAT 230; MATH 112
RECOMMENDED:STAT 123 and STAT 124; MATH 113 or concurrent enrollment.
DESCRIPTION: Regression, transformations, residuals, indicator variables, variable selection, logistic regression, time series, observational studies, statistical software.

STAT 340: Inference. (3:3:0)
WHEN TAUGHT: Fall; Winter; Spring-Summer
PREREQUISITE: STAT 240 & MATH 113
RECOMMENDED: STAT 123 & 124
DESCRIPTION: Continuous random variables (pdf, cdf, moments); sampling distributions; Central Limit Theorem; frequentist inference (estimation, intervals); Bayesian inference (estimation, intervals); simulation.

STAT 372: Actuarial Problems. (1:2:0)
WHEN TAUGHT: Winter; Spring-Summer
RECOMMENDED: Concurrent enrollment in STAT 340
DESCRIPTION: Applying mathematical statistics in actuarial science. Prepares for SOA Exam P.

STAT 377: Statistical Models for Financial Economics. (1:2:0)
WHEN TAUGHT: Fall
PREREQUISITE: STAT 274
DESCRIPTION: Valuation of derivative securities, stock price simulation, risk management techniques, interest rate models. Prepares for SOA exam MFE.

STAT 381: Statistical Computing. (3:3:2)
WHEN TAUGHT: Winter
PREREQUISITE: STAT 223; STAT 224; STAT 330
DESCRIPTION: S Plus, statistical graphics, simulation, advanced SAS (macros, Proc IML, and Proc SQL), and database programming.

STAT 420: Big Data Science 1. (1:2:0)
WHEN TAUGHT: Fall
PREREQUISITE: STAT 123; STAT 223; STAT 330
RECOMMENDED: Students are strongly advised to learn Python prior to the start of the course (see instructor for resources).
DESCRIPTION:Understand server cluster basics; use Apache Spark for data analysis; principles of data mining; regression, classification, and clustering in big data.

STAT 421: Big Data Science 2. (3:3:2)
PREREQUISITE: STAT 420

STAT 435: Nonparametric Statistical Methods. (3:3:0)
WHEN TAUGHT: Winter
PREREQUISITE: STAT 330; or STAT 511
DESCRIPTION: Permutation tests, rank-based methods, analysis of contingency tables, bootstrap methods, curve fitting.

STAT 451: Applied Bayesian Statistics. (3:3.0:)
PREREQUISITE: STAT 151 & STAT 330; Concurrent enrollment in STAT 340.
DESCRIPTION: Bayesian analogs of t-tests, regression, ANOVA, ANCOVA, logistic regression, and Poisson regression implemented using both WinBUGS and Proc MCMC.

STAT 462: Quality Control and Industrial Statistics. (3:3:2)
WHEN TAUGHT: Fall
PREREQUISITE: STAT 201 or STAT 240 & STAT 330
DESCRIPTION: Six sigma; tools with which to define, measure, analyze, improve, control. Advanced concepts in control charts, applying experimental design for process and product improvement.

STAT 466 : Introduction to Reliability. (3:3:2)
WHEN TAUGHT: Winter
PREREQUISITE: STAT 330; or STAT 340
DESCRIPTION: Mathematics, distributions, management, and maintenance of basic reliability concepts; collection and analysis of test data; fault tree analysis; applying reliability in various areas.

STAT 469: Applied Time Series and Forecasting. (3:3:0)
PREREQUISITE: STAT 330 & STAT 340
DESCRIPTION: Data mining, univariate ARIMA time series theory and application, seasonal models, spatial correlation models, conditional heteroscedastic models in financial time series, case studies.

STAT 475: Life Contingencies. (3:3:0)
WHEN TAUGHT: Winter
PREREQUISITE: STAT 274
DESCRIPTION: Life tables, survival functions, contingent annuities, insurance, premiums, reserves, joint annuities and insurance. Prepares for SOA Exam MLC.

STAT 477: Statistical Distributions for Actuarial Modeling and Data Analytics. (3:3:1)
WHEN TAUGHT: Winter
PREREQUISITE: STAT 340
RECOMMENDED: Pass SOA Exam P.
DESCRIPTION: Analyze data from a risk-based application; select a suitable model and estimate parameters using MLE, method of moments, and Bayes methods; determine goodness of fit and measures of confidence for decision making.

STAT 496R: Academic Internship: Statistics. (.5-9:ARR:ARR)
PREREQUISITE: Department internship coordinator's consent.
DESCRIPTION: On-the-job experience or internships for undergraduates. Report is required.

STAT 497R: Introduction to Statistical Research. (.5-3:0:6)
PREREQUISITE: Instructor’s consent.
DESCRIPTION: Review of current literature and survey of present status of significant statistical research; collaborative work between student and faculty.

STAT 499R: Honors Thesis. (.5-3:ARR:ARR)
PREREQUISITE: Departmental consent.
500-Level Graduate Courses (available to advanced undergraduates)

STAT 500: Business Career Essentials in Science and Math. (1.5:1.5:0)
DESCRIPTION: Introduction for science, math, and statistics majors to careers in industry. Project planning, oral and written business presentations, business accounting, and technology readiness.

STAT 511: Statistical Methods for Research 1. (3:3:2)
WHEN TAUGHT: Fall; Winter
PREREQUISITE: STAT 121; or equivalent.
DESCRIPTION: Basic statistical methodologies and experimental design. Topics include simple analysis of variance, multiple regression, analysis of covariance, model selection.

STAT 512: Statistical Methods for Research 2. (3:3:2)
WHEN TAUGHT: Winter
PREREQUISITE: STAT 511
DESCRIPTION: Advanced statistical methodologies and experimental design. Topics include multi-way analysis of variance, mixed models analysis of variance, logistic regression, log-linear models, time series models, principal components, canonical correlation, common experimental designs.

STAT 535: Linear Models. (3:3:0)
WHEN TAUGHT: Fall
PREREQUISITE: MATH 313; STAT 340.
DESCRIPTION: Theory of estimation and testing in linear models. Analysis of full-rank model, over-parameterized model, cell-means model, unequal subclass frequencies, and missing and fused cells. Estimability issues, diagnostics.

STAT 536: Stat Learning and Data Mining. (3:3:0)
PREREQUISITE: STAT 535 & STAT 624; or departmental consent.
DESCRIPTION: Weighted least squares, Bayesian linear models, robust regression, nonlinear regression, local regression, generalized additive models, tree-structured regression.

STAT 537: Mixed Model Methods.
WHEN TAUGHT: Winter On Demand
PREREQUISITE: Stat 535, STAT 642
DESCRIPTION: Generalized linear models framework, binary data, polytomous data, log-linear models.

STAT 538: Survival Analysis. (3:3:0)
WHEN TAUGHT: Winter
PREREQUISITE: STAT 340
DESCRIPTION: Basic concepts of survival analysis; hazard functions; types of censoring; Kaplan-Meier estimates; Logrank tests; proportional hazard models; examples drawn from clinical and epidemiological literature.

STAT 590R: Statistical Consulting. (1-3:Arr:0)
WHEN TAUGHT: Winter PREREQUISITE: Departmental consent.
DESCRIPTION: Introduction to statistical consulting, oral presentations, presentation packages, written reports. Extensive applied experience in the Center for Collaborative Research and Statistical Consulting.

STAT 591R: Graduate Seminar in Statistics. (0:1:0)
WHEN TAUGHT: Fall; Winter

STAT 595R: Special Topics in Statistics. (1-3:ARR:0)
PREREQUISITE:
Instructor's consent; Statistical Computations; Theory of Risk; Expert Systems in Statistics; Biostatistical Methods; Quality Methods; Sampling Practicum.

STAT 599R: Academic Internship: Statistics. (1-9:0:0)
PREREQUISITE: Departmental consent.
DESCRIPTION: On-the-job experience. Report required.