Statistics (ST)

ST 199, SPECIAL TOPICS, 3 Credits

This course can only be taken once unless instructor permission is provided.

ST 201, PRINCIPLES OF STATISTICS, 4 Credits

Study design, descriptive statistics, the use of probability in statistical arguments, sampling, hypothesis tests and confidence intervals for means and proportions. Lec/rec.

Equivalent to: ST 201H, ST 243Z

Recommended: High school algebra

Available via Ecampus

ST 202, PRINCIPLES OF STATISTICS, 4 Credits

Comparisons of means and proportions between two populations (t-tests, chi-square tests, nonparametric tests), simple linear regression, correlation.

Prerequisite: ST 201 with D- or better or ST 243Z with D- or better

Available via Ecampus

ST 243Z, ELEMENTARY STATISTICS I, 4 Credits

A first course in statistics focusing on the interpretation and communication of statistical concepts. Introduces exploratory data analysis, descriptive statistics, sampling methods and distributions, point and interval estimates, hypothesis tests for means and proportions, and elements of probability and correlation. Technology will be used when appropriate.

Equivalent to: ST 201

Recommended: High school algebra

Available via Ecampus

ST 314, INTRODUCTION TO STATISTICS FOR ENGINEERS, 3 Credits

Probability, common probability distributions, sampling distributions, estimation, hypothesis testing, control charts, regression analysis, experimental design.

Prerequisite: MTH 252 with D- or better or MTH 252H with D- or better

Equivalent to: ST 314H

Available via Ecampus

ST 351, INTRODUCTION TO STATISTICAL METHODS, 4 Credits

Study designs, descriptive statistics, collecting and recording data, probability distributions, sampling distributions for means and proportions, hypothesis testing and confidence intervals for means and proportions in one- and two-sample inference, and chi-square tests. Lec/lab.

Equivalent to: ST 351H

Recommended: High school algebra with statistics

Available via Ecampus

ST 351H, INTRODUCTION TO STATISTICAL METHODS, 4 Credits

Study designs, descriptive statistics, collecting and recording data, probability distributions, sampling distributions for means and proportions, hypothesis testing and confidence intervals for means and proportions in one- and two-sample inference, and chi-square tests. Lec/lab.

Attributes: HNRS – Honors Course Designator

Equivalent to: ST 351

Recommended: High school algebra with statistics

ST 352, INTRODUCTION TO STATISTICAL METHODS, 4 Credits

Randomization tests and other nonparametric tests for one- and two-sample inference, simple and multiple linear regression, correlation, one- and two-way analysis of variance, logistic regression. Lec/lab.

Prerequisite: ST 351 with D- or better or ST 351H with D- or better

Available via Ecampus

ST 405, READING AND CONFERENCE, 1-16 Credits

This course is repeatable for 16 credits.

ST 406, PROJECTS, 1-16 Credits

Section 1: Projects. Section 2: Teaching Experience. Section 3: Directed Work.

This course is repeatable for 16 credits.

ST 407, SEMINAR, 1 Credit

Attendance at consulting practicum.

ST 410, INTERNSHIP, 1-16 Credits

This course is repeatable for 16 credits.

ST 411, METHODS OF DATA ANALYSIS, 4 Credits

Graphical, parametric and nonparametric methods for comparing two samples; one-way and two-way analysis of variance; simple linear regression.

Recommended: ST 351

ST 412, METHODS OF DATA ANALYSIS, 4 Credits

Multiple linear regression, including model checking, dummy variables, using regression to fit analysis of variance models, analysis of covariance, variable selection methods.

Prerequisite: ST 411 with D- or better

Recommended: ST 351

ST 413, METHODS OF DATA ANALYSIS, 4 Credits

Principles of experimental design; randomized block and factorial designs; repeated measures; categorical data analysis, including comparison of proportions, tests of homogeneity and independence in cross-classified frequency tables, Mantel-Haenszel test, logistic regression, log-linear regression. Introduction to multivariate statistics.

Prerequisite: ST 412 with D- or better

Recommended: ST 351

ST 415, DESIGN AND ANALYSIS OF PLANNED EXPERIMENTS, 3 Credits

Principles of experimental design; uses, construction and analysis of completely randomized, randomized block and Latin square designs; covariates; factorial treatments, split plotting; random effects and variance components.

Prerequisite: ST 352 with D- or better or ST 411 with D- or better

ST 421, INTRODUCTION TO MATHEMATICAL STATISTICS, 4 Credits

Probability, random variables, expectation, discrete and continuous distributions, multivariate distributions.

Recommended: MTH 253

ST 422, INTRODUCTION TO MATHEMATICAL STATISTICS, 4 Credits

Sampling distributions, Central Limit Theorem, estimation, confidence intervals, properties of estimators, and hypothesis testing.

Prerequisite: ST 421 with D- or better

Recommended: MTH 253

ST 431, SAMPLING METHODS, 3 Credits

Estimation of means, totals and proportions; sampling designs including simple random, stratified, cluster, systematic, multistage and double sampling; ratio and regression estimators; sources of errors in surveys; capture-recapture methods.

Recommended: ST 411 or ST 511

Available via Ecampus

ST 436, R PROGRAMMING FOR DATA, 3 Credits

Focus on R programming from a data science perspective. Combine tools from the tidyverse set of packages to import, clean, prepare, and visualize data. Master basic data types, writing functions, automating repetitive tasks, and good practices for producing readable, reusable, and efficient R code.

Recommended: ST 201 or ST 351, experience working with data in spreadsheets or a point and click interface, or familiarity with calculating summary statistics (e.g., mean, median, standard deviation) and reading basic statistical charts (e.g., barcharts, histograms, scatterplots); critical thinking, problem solving, and reasoning skills

Available via Ecampus

ST 439, SURVEY METHODS, 3 Credits

Survey design, data collection and analysis, general methodology.

Prerequisite: ST 201 with D- or better or ST 243Z with D- or better or ST 351 with D- or better or ST 351H with D- or better

Available via Ecampus

ST 441, PROBABILITY, COMPUTING, AND SIMULATION IN STATISTICS, 4 Credits

Review of probability, including univariate distributions and limit theorems. Random-number generation and simulation of statistical distributions. Bootstrap estimates of standard error. Variance reduction techniques. Emphasis on the use of computation in statistics using the MATLAB programming language.

Prerequisite: ST 422 with D- or better

ST 443, APPLIED STOCHASTIC MODELS, 3 Credits

Development of stochastic models commonly arising in statistics and operations research, such as Poisson processes, birth-and-death processes, discrete-time and continuous-time Markov chains, renewal and Markov renewal processes. Analysis of stochastic models by simulation and other computational techniques.

Prerequisite: ST 421 with D- or better

Recommended: Experience with a high-level programming language or mathematical computation package

ST 499, SPECIAL TOPICS, 1-4 Credits

This course is repeatable for 8 credits.

Available via Ecampus

ST 501, RESEARCH, 1-16 Credits

This course is repeatable for 16 credits.

ST 503, THESIS, 1-16 Credits

This course is repeatable for 999 credits.

ST 505, READING AND CONFERENCE, 1-16 Credits

This course is repeatable for 16 credits.

Available via Ecampus

ST 506, PROJECTS, 1-16 Credits

Section 1: Projects. Section 2: Teaching Experience. Section 3: Directed Work.

This course is repeatable for 16 credits.

Available via Ecampus

ST 507, SEMINAR, 1 Credit

This course is repeatable for 99 credits.

ST 509, CONSULTING PRACTICUM, 2 Credits

The student provides statistical advice, under faculty guidance, on university-related research projects.

This course is repeatable for 99 credits.

Recommended: ST 507 and ST 553

ST 510, INTERNSHIP, 1-16 Credits

This course is repeatable for 16 credits.

ST 511, METHODS OF DATA ANALYSIS, 4 Credits

Graphical, parametric and nonparametric methods for comparing two samples; one-way and two-way analysis of variance; simple linear regression.

Recommended: ST 351

ST 512, METHODS OF DATA ANALYSIS, 4 Credits

Multiple linear regression, including model checking, dummy variables, using regression to fit analysis of variance models, analysis of covariance, variable selection methods.

Prerequisite: ST 511 with C or better

Recommended: ST 351

ST 513, METHODS OF DATA ANALYSIS, 4 Credits

Principles of experimental design; randomized block and factorial designs; repeated measures; categorical data analysis, including comparison of proportions, tests of homogeneity and independence in cross-classified frequency tables, Mantel-Haenszel test, logistic regression, log-linear regression. Introduction to multivariate statistics.

Prerequisite: ST 512 with C or better

Recommended: ST 351

ST 515, DESIGN AND ANALYSIS OF PLANNED EXPERIMENTS, 3 Credits

Principles of experimental design; uses, construction and analysis of completely randomized, randomized block and Latin square designs; covariates; factorial treatments, split plotting; random effects and variance components.

Recommended: ST 352 or ST 411 or ST 511

Available via Ecampus

ST 516, FOUNDATIONS OF DATA ANALYTICS, 4 Credits

Foundations of estimation and hypothesis testing; desirable properties of estimators; maximum likelihood; one- and two-sample problems; theoretical results are explored through simulations and analysis using R. Offered via Ecampus only.

Recommended: ST 351

Available via Ecampus

ST 517, DATA ANALYTICS I, 4 Credits

Methods for modeling quantitative data and statistical learning--simple and multiple linear regression; linear mixed effects models; data imputation; prediction and cross-validation; scaling up to large datasets. Simulations and data analysis using R. Offered via Ecampus only.

Prerequisite: ST 516 with C+ or better

Available via Ecampus

ST 518, DATA ANALYTICS II, 4 Credits

Statistical methods and data analysis techniques for count data. Topics include tests for tables of counts, logistic regression, log-linear regression, generalized linear mixed models, and issues for large datasets. Data analysis in R.

Prerequisite: ST 517 with C+ or better

Available via Ecampus

ST 521, INTRODUCTION TO MATHEMATICAL STATISTICS, 4 Credits

Probability, random variables, expectation, discrete and continuous distributions, multivariate distributions.

Recommended: MTH 253

ST 522, INTRODUCTION TO MATHEMATICAL STATISTICS, 4 Credits

Sampling distributions, Central Limit Theorem, estimation, confidence intervals, properties of estimators, and hypothesis testing.

Prerequisite: ST 521 with C or better

Recommended: MTH 253

ST 525, APPLIED SURVIVAL ANALYSIS, 3 Credits

Statistical methods for analyzing survival data or time-to-event data, which may be censored and/or truncated. Specific topics can vary term to term, and could include Kaplan-Meier estimator; K-sample hypothesis tests for survival data; Accelerated failure time model; Cox proportional hazard regression model.

Prerequisite: ST 516 with C or better and ST 517 [C] and ST 518 [C]

Available via Ecampus

ST 531, SAMPLING METHODS, 3 Credits

Estimation of means, totals and proportions; sampling designs including simple random, stratified, cluster, systematic, multistage and double sampling; ratio and regression estimators; sources of errors in surveys; capture-recapture methods.

Recommended: ST 411 or ST 511

Available via Ecampus

ST 536, R PROGRAMMING FOR DATA, 3 Credits

Focus on R programming from a data science perspective. Combine tools from the tidyverse set of packages to import, clean, prepare, and visualize data. Master basic data types, writing functions, automating repetitive tasks, and good practices for producing readable, reusable, and efficient R code.

Available via Ecampus

ST 537, DATA VISUALIZATION, 3 Credits

Perceptual principles for displaying data; critique and improvement of data visualizations; use of color in visualization; principles of tidy data; strategies for data exploration; select special topics.

Prerequisite: ST 512 with C or better or ST 517 with C or better or ST 552 with C or better

Recommended: Familiarity with linear regression and using R

Available via Ecampus

ST 538, MODERN STATISTICAL METHODS FOR LARGE AND COMPLEX DATA SETS, 3 Credits

Provides students with the tools and experience to analyze big and messy data and work effectively in a data science team. Covers the tools to handle big data and answer statistical questions based on the data. Includes three big data analysis projects that students work on in groups. Focuses on proper use of modern data analysis techniques related to regression, classification and clustering for data coming from a variety of application fields. R will be the lingua franca.

Prerequisite: ST 512 with C or better or ST 517 with C or better or ST 552 with C or better or ST 412 with C or better

Available via Ecampus

ST 539, SURVEY METHODS, 3 Credits

Survey design, data collection and analysis, general methodology.

Recommended: ST 243Z or ST 351

Available via Ecampus

ST 541, PROBABILITY, COMPUTING, AND SIMULATION IN STATISTICS, 4 Credits

Review of probability, including univariate distributions and limit theorems. Random-number generation and simulation of statistical distributions. Bootstrap estimates of standard error. Variance reduction techniques. Emphasis on the use of computation in statistics using the S-Plus or MATLAB programming language.

Recommended: ST 422 or ST 522

ST 543, APPLIED STOCHASTIC MODELS, 3 Credits

Development of stochastic models commonly arising in statistics and operations research, such as Poisson processes, birth-and-death processes, discrete-time and continuous-time Markov chains, renewal and Markov renewal processes. Analysis of stochastic models by simulation and other computational techniques.

Recommended: (ST 421 or ST 521) and experience with a high-level programming language or mathematical computation package.

ST 551, STATISTICAL METHODS, 4 Credits

Properties of t, chi-square and F tests; randomized experiments; sampling distributions and standard errors of estimators, delta method, comparison of several groups of measurements; two-way tables of measurements.

Recommended: Concurrent enrollment in MTH 341 and (ST 422 or ST 522)

ST 552, STATISTICAL METHODS, 4 Credits

Simple and multiple linear regression including polynomial regression, indicator variables, weighted regression, and influence statistics, nonlinear regression and linear models for binary data.

Prerequisite: ST 551 with C or better

Recommended: ST 422 or ST 522.

ST 553, STATISTICAL METHODS, 4 Credits

Principles and analysis of designed experiments, including factorial experiments, analysis of covariance, random and mixed effect models. Lec/lab.

Prerequisite: ST 552 with C or better

ST 555, ADVANCED EXPERIMENTAL DESIGN, 3 Credits

Designs leading to mixed models including split plots, repeated measures, crossovers and incomplete blocks. Introduction to experimental design in industry including confounding, fractional factorials and response surface methodology. Analysis of unbalanced data.

Prerequisite: ST 553 with C or better

ST 557, APPLIED MULTIVARIATE ANALYSIS, 3 Credits

Multivariate data structures, linear combinations; principal components, factor and latent structure analysis, canonical correlations, discriminant analysis; cluster analysis, multidimensional scaling. Not offered every year.

Recommended: (ST 412 or ST 512) and (MTH 252 or MTH 245)

ST 558, MULTIVARIATE ANALYTICS, 3 Credits

Basics of matrix algebra, principal components analysis, cluster analysis, factor analysis, multidimensional scaling.

Prerequisite: ST 518 with C- or better

Available via Ecampus

ST 559, BAYESIAN STATISTICS, 3 Credits

Bayesian statistics for data analysis. Characterizations of probability; comparative (Bayesian versus frequentist) inference; prior, posterior and predictive distributions; hierarchical modeling. Computational methods include Markov Chain Monte Carlo for posterior simulation.

Recommended: ST 562

ST 561, THEORY OF STATISTICS, 4 Credits

Distributions of functions of random variables, joint and conditional distributions, sampling distributions, convergence concepts, order statistics.

Recommended: ST 422 or ST 522

ST 562, THEORY OF STATISTICS, 4 Credits

Sufficiency, exponential families, location and scale families; point estimation: maximum likelihood, Bayes, and unbiased estimators; asymptotic distributions of maximum likelihood estimators; Taylor series approximations.

Prerequisite: ST 561 with C or better

Recommended: ST 422 or ST 522

ST 563, THEORY OF STATISTICS, 4 Credits

Hypothesis testing: likelihood ratio, Bayesian, and uniformly most powerful tests; similar tests in exponential families; asymptotic distributions of likelihood ratio test statistics; confidence intervals.

Prerequisite: ST 562 with C or better

Recommended: ST 422 or ST 522

ST 565, TIME SERIES, 3 Credits

Analysis of serially correlated data in both time and frequency domains. Autocorrelation and partial autocorrelation functions, autoregressive integrated moving average models, model building, forecasting; filtering, smoothing, spectral analysis, frequency response studies, Offered winter term in even years.

Recommended: (ST 412 or ST 512) and (ST 422 or ST 522)

ST 566, TIME SERIES ANALYTICS, 3 Credits

Focuses on statistical and analytical tools for analyzing data that are observed sequentially over time. Specific topics can vary term to term, and could include methods for exploratory time series analysis, linear time series models (ARMA, ARIMA), forecasting, spectral analysis and state-space models. The focus will be on applied problems, though some mathematical statistics is necessary for a solid understanding of the statistical issues. This course is designed for students in Data Analytics MS and Certificate programs.

Prerequisite: ST 516 with C or better and ST 517 [C] and ST 518 [C]

Available via Ecampus

ST 567, SPATIAL STATISTICS, 3 Credits

The analysis of spatial data. Graphical tools for exploring spatial data, geostatistics, variogram estimation, kriging, areal models, hierarchical spatial models, and spatio-temporal modelling. Offered winter term in odd years.

Recommended: (ST 412 or ST 512) and (ST 422 or ST 522)

ST 591, INTRODUCTION TO QUANTITATIVE GENOMICS, 3 Credits

Provides an overview of how genomic data is generated and analyzed. It focuses on the underlying biological motivation, theoretical concepts, and analytical challenges associated with genomic research, especially the generation of statistics that summarize genomic data. The class is organized as a combination of lectures and group literature review discussions. Students are expected to actively participate in the class. Students from diverse backgrounds, including quantitative, biological, and computations sciences, are encouraged to enroll.

Recommended: ST 411 or ST 511

Available via Ecampus

ST 592, STATISTICAL METHODS FOR GENOMICS RESEARCH, 3 Credits

Lectures include an overview of statistical methods commonly applied in genomics research. Specific methods can vary term to term, and could include cluster analysis, decision trees, dimension reduction tools, regression models, multiple testing adjustment, variable selection methods, etc. Journal clubs include team-based review and presentations of landmark papers in both statistical methodology and genomics research. Research experience includes whole-term collaboration between students from statistics and other disciplines on real projects.

Recommended: ST 411 or ST 511 or a higher level course such as ST 551

Available via Ecampus

ST 595, CAPSTONE PROJECT, 3 Credits

Integrates and applies the analytics skills learned in the MS in Data Analytics program to solve real-world problems and interpret and communicate results. Engages student teams in the entire process of solving data science projects in realistic settings, from placing the problem into appropriate statistical framework to applying suitable analytic methods to the problem. Emphasizes problem solving, written and oral communication skills.

Prerequisite: ST 516 with C or better and ST 517 [C] and ST 518 [C] and ST 558 [C]

This course is repeatable for 6 credits.

Available via Ecampus

ST 599, SPECIAL TOPICS, 1-4 Credits

This course is repeatable for 16 credits.

Available via Ecampus

ST 601, RESEARCH, 1-16 Credits

This course is repeatable for 16 credits.

ST 603, THESIS, 1-16 Credits

This course is repeatable for 999 credits.

ST 606, PROJECTS, 1-16 Credits

Section 1: Projects; Section 2: Teaching Experience; Section 3: Directed Work.

This course is repeatable for 16 credits.

ST 623, GENERALIZED REGRESSION MODELS, 3 Credits

Maximum likelihood analysis for frequency data; regression-type models for binomial and Poisson data; iterative weighted least squares and maximum likelihood; analysis of deviance and residuals; over-dispersion and quasilikelihood models; log-linear models for multidimensional contingency tables.

Prerequisite: ST 553 with C or better and ST 563 [C]

ST 625, SURVIVAL ANALYSIS, 3 Credits

Prepares students to understand and analyze survival data. Concepts to be discussed include: hazard function (failure rate function); nonparametric likelihood; empirical processes; empirical distribution function; censoring (mostly right independent censoring); Kaplan-Meier estimator; Bias of the KM estimator; Cox proportional hazards model; Accelerated Failure Time Model; Partial Likelihood; log-rank test.

Prerequisite: ST 553 with C or better or ST 563 with C or better

ST 651, LINEAR MODEL THEORY, 3 Credits

Least squares estimation, best linear unbiased estimation, parameterizations, multivariate normal distributions, distributions of quadratic forms, testing linear hypotheses, simultaneous confidence intervals. Offered alternate years.

Recommended: ST 553 and ST 563

ST 652, LINEAR MODEL THEORY, 3 Credits

Explores advanced topics in linear and generalized linear mixed models: estimation, tests, confidence intervals, prediction, model diagnostics, model selection.

Prerequisite: ST 651 with C or better

Recommended: ST 553 and ST 563

ST 661, ADVANCED THEORY OF STATISTICS, 3 Credits

Exponential families, sufficient statistics; unbiased, equivariant, Bayes, and admissible estimation. Offered alternate years.

Recommended: ST 563 and MTH 511

ST 662, ADVANCED THEORY OF STATISTICS, 3 Credits

Uniformly most powerful, unbiased, similar, and invariant tests. Offered alternate years.

Prerequisite: ST 661 with C or better

Recommended: ST 563 and MTH 511

ST 663, ADVANCED THEORY OF STATISTICS, 3 Credits

First-order and higher-order asymptotics; likelihood ratio, score, and Wald tests; Edgeworth and saddlepoint approximations. Offered alternate years.

Prerequisite: ST 662 with C or better

Recommended: ST 563 and MTH 511