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The online catalog includes the most recent changes to courses and degree
requirements that have been approved by the Faculty Senate, including changes
that are not yet effective.
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### Courses

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The online catalog includes the most recent changes to courses and degree
requirements that have been approved by the Faculty Senate, including changes
that are not yet effective.
Courses showing two entries of the same number
indicate that the course information is changing. The most recently approved
version is shown first, followed by the older version, in gray, with its
last-effective term preceding the course title. Courses shown in gray with only
one entry of the course number are being discontinued.
Course offerings by term can be accessed by clicking on the term links when viewing a specific campus catalog.
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#### Statistics (STAT)

115 Introduction to Data Analytics 3 Basic concepts, principles, and tools used in data analytics. (Crosslisted course offered as CPT S 115, CS 115, STAT 115).

205 [QUAN] Statistical Thinking 3 Course Prerequisite: MATH 101 with a C or better, MATH 103 with a C or better, or a minimum ALEKS math placement score of 45%. Scientific explanation; correlations and causality; presenting statistical evidence; graphical and numerical methods; chance and gambling; the bell-shaped distribution. Typically offered Fall, Spring, and Summer.

212 [QUAN] Introduction to Statistical Methods 4 (3-2) Course Prerequisite: MATH 101 with a C or better, MATH 103 with a C or better, or MATH 106, 108, 140, 171, 201, or a minimum ALEKS math placement score of 45%. Introduction to descriptive and inferential statistics: t-tests, chi-square tests, one-way ANOVA, simple linear regression and correlation. Typically offered Fall, Spring, and Summer.

360 Probability and Statistics 3 Course Prerequisite: MATH 172 or MATH 182. Probability models, sample spaces, random variables, distributions, moments, comparative experiments, tests, correlation and regression in engineering applications. Credit not granted for both STAT 360 and 370. Typically offered Fall, Spring, and Summer. Cooperative: Open to UI degree-seeking students.

370 Introductory Statistics for Engineers 3 Course Prerequisite: MATH 172 or MATH 182. Probability axioms, probability models, random variables, expectation, confidence intervals, hypothesis testing, analysis of variance, control charts. Credit not granted for both STAT 360 and 370. Typically offered Fall, Spring, and Summer.

380 [M] Decision Making and Statistics 3 Course Prerequisite: STAT 360 or 370. [M] Concepts and methods of decision science using simple mathematical, statistical and computer based tools to solve complex problems for sound decision making. Typically offered Fall.

380 (Effective through Summer 2018) Decision Making and Statistics 3 Course Prerequisite: MATH 171 or 202. Concepts and methods of decision science using simple mathematical, statistical and computer based tools to solve complex problems for sound decision making. Typically offered Fall.

410 Topics in Probability and Statistics 3 May be repeated for credit; cumulative maximum 6 hours. Current topics in probability and statistics of mutual interest to faculty and students. Credit not granted for both STAT 410 and STAT 510. Recommended preparation: One 3-hour 300-level STAT course. Offered at 400 and 500 level. Typically offered Fall and Spring.

412 Statistical Methods in Research I 3 Course Prerequisite: STAT 212, MATH 140, 171, 202, or graduate standing. Intermediate statistical methods, design and analysis of research studies: completely randomized and randomized block designs, multiple regression, categorical data analysis. Typically offered Fall, Spring, and Summer. Cooperative: Open to UI degree-seeking students.

419 Introduction to Multivariate Statistics 3 Course Prerequisite: MATH 220; one 300-400-level STAT. Introductory course covering multidimensional data, multivariate normal distribution, principal components, factor analysis, clustering, and discriminant analysis. Typically offered Spring.

422 Sampling Methods 3 Course Prerequisite: STAT 212, 360, or 370. Simple and stratified random sampling; systematic sampling; cluster sampling; double sampling, area sampling. Typically offered Fall and Spring. Cooperative: Open to UI degree-seeking students.

423 Statistical Methods for Engineers and Scientists 3 Hypothesis testing; linear, multilinear, and nonlinear regression; analysis of variance for designed experiments; quality control; statistical computing. Credit not normally granted for both STAT 423 and 430. Recommended preparation: One 3-hour 300-level STAT course. Offered at 400 and 500 level. Typically offered Spring.

424 [CAPS] [M] Data Analytics Capstone 3 Course Prerequisite: CPT S/CS 315; STAT 360; STAT 436 or concurrent enrollment; CPT S 451/CS 351 or concurrent enrollment; certified major in Data Analytics; junior standing. Team-based project that integrates the main aspects of data analytics. (Crosslisted course offered as CPT S 424, CS 424, STAT 424).

430 Statistical Methods in Engineering 3 Course Prerequisite: MATH 172 or 182; MATH 220. Random variables, sampling, hypothesis testing; linear, multilinear, and nonlinear regression; analysis of variance for designed experiments; statistical computing.

435 [M] Statistical Modeling for Data Analytics 3 (2-2) Course Prerequisite: STAT 360; STAT 412, 423, 430, or ECONS 311. Multiple linear regression with model selection, dealing with multicolinearity, assessing model assumptions, the LASSO, ridge regression, elastic nets, Loess smoothing, logistic regression, Poisson regression, and the application of the bootstrap to regression modeling. Typically offered Fall.

436 Statistical Computing with SAS and R 3 (2-2) Course Prerequisite: One 300-400-level STAT. Introduction to the SAS and R statistical software packages; covers data entry, variable creation, debugging, graphics, and basic statistical methods. Typically offered Fall.

437 Statistical Analytics and Learning 3 Course Prerequisite: STAT 435. Statistical modeling and data analysis using supervised and unsupervised learning methods. Typically offered Spring.

443 Applied Probability 3 Course Prerequisite: MATH 172 or MATH 182; MATH 220 or MATH 230. Axioms of probability theory; random variables; expectation; generating function; law of large numbers; central limit theorem; Markov chains. Typically offered Fall.

446 Statistical Applications in Insurance 3 Course Prerequisite: STAT 443. Introduction to the application of mathematics and statistics to the insurance field with a focus on actuarial science. Typically offered Spring.

447 Introduction to Time Series Analysis 3 Course Prerequisite: STAT 423. Introduction to the analysis and application of time series including AR, MA, ARMA, and ARIMA models. Typically offered Fall.

456 Introduction to Statistical Theory 3 Course Prerequisite: STAT 430 or 443. Sampling distributions; hypothesis testing and estimation; maximum likelihood; likelihood ratio tests; theory of least squares; nonparametrics. (Crosslisted course offered as STAT 456, MATH 456). Recommended preparation: One 3-hour 400-level STAT or probability course. Offered at 400 and 500 level. Typically offered Spring.

508 Environmental Spatial Statistics 3 Theoretical introduction and practical training in spatial data analysis for graduate students in the environmental sciences. (Crosslisted course offered as SOIL SCI 508, STAT 508). Required preparation must include undergraduate statistics through applied multiple regression. Typically offered Fall and Spring. Cooperative: Open to UI degree-seeking students.

510 Topics in Probability and Statistics 3 May be repeated for credit; cumulative maximum 6 hours. Current topics in probability and statistics of mutual interest to faculty and students. Credit not granted for both STAT 410 and STAT 510. Recommended preparation: One 3-hour 300-level STAT course. Offered at 400 and 500 level. Typically offered Fall and Spring.

511 Statistical Methods for Graduate Researchers 4 (3-2) Fundamentals of experimental design and statistical methods for graduate students in the sciences. Covers t-test for one and two means, ANOVA through completely randomized designs with one and two factors, chi-square tests and regression analysis using R. Recommended preparation: One prior course in statistics. Cannot be used for credit in the Department of Mathematics and Statistics graduate programs. (Crosslisted course offered as STAT 511, AFS 511). Typically offered Fall and Spring.

512 Analysis of Variance of Designed Experiments 3 (2-2) Principles of experimental design and analysis and interpretation of data. Recommended preparation: One 3-hour 300-level STAT course. Typically offered Fall, Spring, and Summer.

516 Time Series 3 ARIMA models; identification, estimation, diagnostics, and forecasting; seasonal adjustments, outlier detection, intervention analysis and transfer function modeling. (Crosslisted course offered as MGTOP 516, STAT 516). Recommended preparation: STAT 443. Typically offered Fall. Cooperative: Open to UI degree-seeking students.

519 Applied Multivariate Analysis 3 Multivariate normal distribution, principal components, factor analysis, discriminant function, cluster analysis, Hotteling's T2 and MANOVA. (Crosslisted course offered as MGTOP 519, STAT 519). Recommended preparation: STAT 443. Typically offered Fall and Spring.

520 Statistical Analysis of Qualitative Data 3 Binomial, Poisson, multinomial distribution; contingency tables, Fisher's tests, log-linear models; ordinal data; applications in biology, business, psychology, and sociology. Recommended preparation: Linear Algebra or Calculus I and one 3-hour 300-level STAT course. Typically offered Odd Years - Fall. Cooperative: Open to UI degree-seeking students.

522 Biostatistics and Statistical Epidemiology 3 Rigorous approach to biostatistical and epidemiological methods including relative risk, odds ratio, cross-over designs, survival analysis and generalized linear models. Recommended preparation: Linear Algebra or Calculus I and one 3-hour 300-level STAT course. Typically offered Odd Years - Spring. Cooperative: Open to UI degree-seeking students.

523 Statistical Methods for Engineers and Scientists 3 Hypothesis testing; linear, multilinear, and nonlinear regression; analysis of variance for designed experiments; quality control; statistical computing. Credit not normally granted for both STAT 423 and 430. Recommended preparation: One 3-hour 300-level STAT course. Offered at 400 and 500 level. Typically offered Spring.

530 Applied Linear Models 3 (2-2) The design and analysis of experiments by linear models. Recommended preparation: One 3-hour 300-level STAT course. Typically offered Spring.

533 Theory of Linear Models 3 Theoretical basis of linear regression and analysis of variance models; a unified approach based upon the generalized inverse. Recommended preparation: Linear Algebra and one 3-hour 400-level STAT theory course. Typically offered Fall. Cooperative: Open to UI degree-seeking students.

535 Regression Analysis 3 Conceptual development of regression; estimation, prediction, tests of hypotheses, variable selection, diagnostics, model validation, correlation, and nonlinear regression. Recommended preparation: One 3-hour 400-level STAT course. Typically offered Spring. Cooperative: Open to UI degree-seeking students.

536 Statistical Computing 3 (2-3) Generation of random variables, Monte Carlo simulation, bootstrap and jackknife methods, EM algorithm, Markov chain Monte Carlo methods. (Crosslisted course offered as STAT 536, MATH 536). Recommended preparation: One 3-hour 400-level probability or STAT course. Typically offered Fall. Cooperative: Open to UI degree-seeking students.

544 Applied Stochastic Processes 3 Poisson and Markov processes; queuing theory; auto-covariance; stationarity; power spectra; harmonic analysis; linear mean-square predictions. Recommended preparation: One 3-hour 400-level STAT or Applied Probability course. Typically offered Spring. Cooperative: Open to UI degree-seeking students.

548 Statistical Theory I 3 Probability spaces, combinatorics, multidimensional random variables, characteristic function, special distributions, limit theorems, stochastic processes, order statistics. (Crosslisted course offered as STAT 548, MATH 568). Recommended preparation: Calculus III and one 3-hour 400-level probability course.

549 Statistical Theory II 3 Continuation of STAT 548. Statistical inferences; estimation and testing hypotheses; regression analysis; sequential analysis and nonparametric methods. (Crosslisted course offered as STAT 549, MATH 569). Recommended preparation: STAT 548.

556 Introduction to Statistical Theory 3 Sampling distributions; hypothesis testing and estimation; maximum likelihood; likelihood ratio tests; theory of least squares; nonparametrics. (Crosslisted course offered as STAT 456, MATH 456). Recommended preparation: One 3-hour 400-level STAT or probability course. Offered at 400 and 500 level. Typically offered Spring.

565 Analyzing Microarray and Other Genomic Data 3 Statistical issues from pre-processing (transforming, normalizing) and analyzing genomic data (differential expression, pattern discovery and predictions). Recommended preparation: Linear Algebra and one 3-hour 300-level STAT course. Typically offered Even Years - Fall. Cooperative: Open to UI degree-seeking students.

572 Quality Control 3 Simple quality assurance tools; process monitoring; Shewhart control charts; process characterization and capability; sampling inspection; factorial experiments. Recommended preparation: One 3-hour 300-level STAT or probability course. Typically offered Spring.

573 Reliability 3 Probabilistic modeling and inference; product-limit estimator; probability plotting; maximum likelihood estimation with censored data; regression models for accelerated life testing. Recommended preparation: One 3-hour 300-level STAT or probability course. Typically offered Spring.

574 Linear and Nonlinear Mixed Models 3 Course Prerequisite: STAT 530; STAT 533; STAT 556. The theoretical development and application of linear and nonlinear mixed models covering the theory of linear, generalized linear, and nonlinear mixed models. Typically offered Spring.

575 The Theory of Multivariate Analysis 3 Course Prerequisite: STAT 519; STAT 536; STAT 556. The theoretical development and application of multivariate statistical methods; topics include multivariate distributions, MANOVA, principal components, factor analysis and classification. Typically offered Spring.

576 Bayesian Analysis 3 Course Prerequisite: STAT 536; STAT 556. Statistical principle for combing new evidence with prior beliefs, inference and simulation procedures for accommodating complex data and producing interpretable output. Typically offered Spring.

577 Statistical Learning Theory 3 Focus on learning and interpreting from data; both prediction and classification will be discussed for supervised and unsupervised learning. Recommended preparation: STAT 533; STAT 536; STAT 556. Typically offered Fall.

590 Statistical Consulting Practicum V 1-2 May be repeated for credit; cumulative maximum 6 hours. Theory and practice of statistical consulting, participation in consulting session. Recommended preparation: STAT 512 and STAT 530. Typically offered Fall and Spring. S, F grading.

600 Special Projects or Independent Study V 1-18 May be repeated for credit. Independent study, special projects, and/or internships. Students must have graduate degree-seeking status and should check with their major advisor before enrolling in 600 credit, which cannot be used toward the core graded credits required for a graduate degree. Typically offered Fall, Spring, and Summer. S, F grading.

702 Master's Special Problems, Directed Study, and/or Examination V 1-18 May be repeated for credit. Independent research in special problems, directed study, and/or examination credit for students in a non-thesis master's degree program. Students must have graduate degree-seeking status and should check with their major advisor/committee chair before enrolling for 702 credit. Typically offered Fall, Spring, and Summer. S, U grading.

800 Doctoral Research, Dissertation, and/or Examination V 1-18 May be repeated for credit. Course Prerequisite: Admitted to the Statistics PhD program. Independent research and advanced study for students working on their doctoral research, dissertation and/or final examination. Students must have graduate degree-seeking status and should check with their major advisor/committee chair before enrolling for 800 credit. Typically offered Fall, Spring, and Summer. S, U grading.

- Mathematics and Statistics
### Courses

- Mathematics
- Statistics
### Schedules of Studies

- Mathematics - Statistics Option
- Data Analytics - Actuarial Science Option
- Data Analytics - Business Option
- Data Analytics - Agriculture and Environmental Systems Option
- Data Analytics - Computation Option
- Data Analytics - Economics Option
- Data Analytics - Life Sciences Option
- Data Analytics - Physical Sciences Option
- Data Analytics - Social Sciences Option
- Mathematics - Secondary Teaching Option with Certification
- Secondary Mathematics Teaching Option Without Certification
- Mathematics – Actuarial Science Option
- Mathematics – Applied Option
- Mathematics – Theoretical Option
### Minors

- Statistics
- Mathematics
### Certificates

- Certificate in Quantitative Biology