Course Descriptions & Syllabi

View Actuarial Science course descriptions and syllabi
View Statistics course descriptions and syllabi

Undergraduate Duplication and Regression Policy

Undergraduate students should be aware of the duplication and regression policies concerning the following courses.

Students may earn credit for only two of these:

STAT:1010 Statistics and Society,
STAT:1020 Elementary Statistics and Inference (same as PSQF:1020),
STAT:1030 Statistics for Business, and
STAT:2010 Statistical Methods and Computing.

Credit for STAT:1010 Statistics and Society may be earned only if the course is taken before any of these:

STAT:1020 Elementary Statistics and Inference (same as PSQF:1020),
STAT:1030 Statistics for Business, or
STAT:2010 Statistical Methods and Computing.

Students may receive credit for only one course from each of these pairs:

STAT:2010 Statistical Methods and Computing and STAT:4200 Statistical Methods and Computing,
STAT:3100 Introduction to Mathematical Statistics I and STAT:3120 Probability and Statistics, and
STAT:3510 Biostatistics and STAT:4143 Introduction to Statistical Methods.

Students may not take STAT:3101 Introduction to Mathematical Statistics II and STAT:4101 Mathematical Statistics II at the same time and get credit for both (nor go back to STAT:3101 Introduction to Mathematical Statistics II after taking STAT:4101 Mathematical Statistics II).

  • Course descriptions, meeting times, prerequisites, and registration instructions are also provided on ISIS.

Statistics Course Descriptions & Syllabi

STAT:1000 (22S:029) FIRST-YEAR SEMINAR (1 s.h.)
Small discussion class taught by a faculty member; topics chosen by instructor; may include outside activities (e.g. films, lectures, performances, readings, visits to research facilities).
Prerequisite: first- or second-semester standing. Syllabus

STAT:1010 (22S:002) STATISTICS AND SOCIETY (3 s.h.)
Statistical ideas and their relevance to public policy, business, humanities, and the social, health, and physical sciences; focus on critical approach to statistical evidence.
GE: quantitative or formal reasoning.
Requirement: MATH:0100 or MATH:1005 or ALEKS [30]
Offered fall and spring semesters.
Syllabus

STAT:1020 (22S:025) ELEMENTARY STATISTICS AND INFERENCE (3 s.h.)
Cross-reference: PSQF:1020 (07P:025).
Graphing techniques for presenting data, descriptive statistics, correlation, regression, prediction; logic of statistical inference, elementary probability models, estimation and tests of significance.
GE: quantitative or formal reasoning.
Requirement: MATH:0100 or MATH:1005 or ALEKS [30]
Offered fall and spring semesters.
Syllabus

STAT:1030 (22S:008) STATISTICS FOR BUSINESS (4 s.h.)
Descriptive statistics, graphical presentation, elementary probability, estimation and testing, regression, correlation; statistical computer packages.
GE: quantitative or formal reasoning.
Prerequisite: MATH:1005 (22M:008).
Offered fall and spring semesters.
Syllabus

STAT:2010 (22S:030) STATISTICAL METHODS AND COMPUTING (3 s.h.)
Co-exists with STAT:4200 (22S:105).
Methods of data description and analysis using SAS: descriptive statistics, graphical presentation, estimation, hypothesis testing, sample size, power; emphasis on learning statistical methods and concepts through hands-on experience with real data.  STAT:2010 is a beginning methods course for undergraduate students.
GE: quantitative or formal reasoning.
Prerequisite: MATH:1005 (22M:008).
Offered spring semesters.
Syllabus

STAT:2020 (22S:039) PROBABILITY AND STATISTICS FOR THE ENGINEERING AND PHYSICAL SCIENCES (3 s.h.) Probability, random variables, important discrete and continuous distributions, joint distributions, transformations of random variables, descriptive statistics, point and interval estimation, tests of hypotheses, regression. Prerequisite: MATH:1560 or MATH1860.  Offered fall and spring semesters. Syllabus Course description prior to Fall 2014:  Descriptive statistics, exploratory data analysis, random variables, important discrete and continuous distributions, point and interval estimation, tests of hypotheses, regression.

STAT:3100 (22S:130) INTRODUCTION TO MATHEMATICAL STATISTICS I (3 s.h.)
Descriptive statistics, probability, discrete and continuous distributions, sampling, sampling distributions.
Prerequisite: MATH:1560 (22M:032) or MATH:1860 (22M:026).
Offered fall semesters.
Syllabus

STAT:3101 (22S:131) INTRODUCTION TO MATHEMATICAL STATISTICS II (3 s.h.)
Estimation, testing statistical hypothesis, linear models, multivariate distributions, nonparametric methods.
Prerequisite: STAT:3100 (22S:130).
Offered spring semesters.
Syllabus

STAT:3120 (22S:120) PROBABILITY AND STATISTICS (4 s.h.)
Models, discrete and continuous random variables and their distributions, estimation of parameters, testing statistical hypotheses.
Prerequisite: MATH:1560 (22M:032) or MATH:1860 (22M:026).
Offered fall and spring semesters.
Syllabus

STAT:3200 (22S:152) APPLIED LINEAR REGRESSION (3 s.h.)
Cross-reference: IE:3760 (056:176).
Regression analysis with focus on applications; model formulation, checking, selection; interpretation and presentation of analysis results; simple and multiple linear regression; logistic regression; ANOVA; hands-on data analysis with computer software.
Prerequisite: STAT:2010 (22S:030) or STAT:2020 (22S:039).
Syllabus

STAT:3210 (22S:158) EXPERIMENTAL DESIGN AND ANALYSIS (3 s.h.)
Single- and multifactor experiments; analysis of variance; multiple comparisons; contrasts; diagnostics, fixed, random, and mixed effects models; designs with blocking and/or nesting; two-level factorials and fractions thereof; use of statistical computing packages.
Prerequisites: STAT:3200 (22S:152).
Offered spring semesters.
Syllabus

STAT:3510 (22S:101) BIOSTATISTICS (3 s.h.)
Statistical concepts and methods for the biological sciences: descriptive statistics, elementary probability, sampling distributions, confidence intervals, parametric and nonparametric methods, one-way ANOVA, correlation and regression, categorical data.
Prerequisite: MATH:0100 or MATH:1005 or ALEKS [30]
Offered fall and spring semesters.
Syllabus

STAT:3620 (22S:133) QUALITY CONTROL (3 s.h.)
Cross-reference: IE:3600 (056:162) -adminstrative home.

STAT:4100 (22S:153) MATHEMATICAL STATISTICS I (3 s.h.)
Probability, conditional probability, random variables, distribution and density functions, joint and conditional distributions, various families of discrete and continuous distributions, mgf technique for sums, convergence in distribution, convergence in probability, central limit theorem.
Prerequisites: MATH:2700 (22M:027) and MATH:2850 (22M:028).
Offered fall and spring semesters.
Syllabus 

STAT:4101 (22S:154) MATHEMATICAL STATISTICS II (3 s.h.)
Transformations, order statistics, point estimation, sufficient statistics, Rao-Blackwell Theorem, delta method, confidence intervals, likelihood ratio tests, applications.
Prerequisite: STAT:4100 (22S:153).
Offered fall and spring semesters.
Syllabus

STAT:4143 (22S:102) INTRODUCTION TO STATISTICAL METHODS (3 s.h.)
Cross-reference: PSQF:4143 (07P:143) -administrative home.
All questions regarding this course should be directed to the Department of Psychological and Quantitative Foundations, 335-5577.

STAT:4200 (22S:105) STATISTICAL METHODS AND COMPUTING (3 s.h.)
Co-exists with STAT:2010 (22S:030).
Methods of data description and analysis using SAS: descriptive statistics, graphical presentation, estimation, hypothesis testing, sample size, power; emphasis on learning statistical methods and concepts through hands-on experience with real data.  STAT:4200 is a beginning methods course for graduate students in non-statistics, less quantitative majors.
Prerequisite: MATH:1005 (22M:008).
Syllabus

STAT:4510 (22S:150) REGRESSION, TIME SERIES, AND FORECASTING (3 s.h.)
Regression analysis, forecasting, time series methods; use of statistical computing packages.
Pre-requisite: STAT:4101 with a prerequisite of C+ or STAT:5101 with a prerequisite of C+
Requirements: none
Special Grading: none Recommendation: none
Syllabus

STAT:4520 (22S:138) BAYESIAN STATISTICS (3 s.h.)
Cross-reference: PSQF:4520 (07P:148).
Bayesian statistical analysis, with focus on applications; Bayesian and frequentist methods compared; Bayesian model specification, choice of priors, computational methods; hands-on Bayesian data analysis using appropriate software; interpretation and presentation of analysis results.
Prerequisite: STAT:3100-3101 (22S:130-131), or STAT:3120 (22S:120), or STAT:4100-4101 (22S:153-154); and STAT:3200 (22S:152).
Syllabus

STAT:4540 STATISTICAL LEARNING (3 s.h.)
Introduction to supervised and unsupervised statistical learning, with a focus on regression, classification, and clustering.  Methods will be applied to real data using appropriate software. Supervised learning topics include:  linear and non-linear (e.g. logistic) regression; linear discriminant analysis; cross-validation, bootstrapping, model selection, and regularization methods (e.g. ridge and lasso); generalized additive and spline models; tree-based methods, random forests and boosting; and support-vector machines.  Unsupervised learning topics include:  principal components and clustering.  Requirements:  An introductory statistics course and a regression course Recommendations:  Prior exposure to programming and/or software, such as R, SAS, and Matlab is recommended, but not required.

Syllabus

STAT:4580 DATA VISUALIZATION AND DATA TECHNOLOGIES (3 s.h.)
Introduces common techniques for visualizing univariate and multivariate data, data summaries, and modeling results. Students will learn how to create, and interpret these visualizations, and to assess effectiveness of different visualizations based on an understanding of human perception and statistical thinking.  Data technologies for obtaining and preparing data for visualization and further analysis will also be discussed.

Requirements:  An introductory statistics course and a regression course. Prerequisites:see instructor/advisor.
Recommendations:  Prior exposure to basic use of statistical programming software, such as R or SAS, as obtained from a regression course, is strongly recommended.
Syllabus 

STAT:4740 LARGE DATA ANALYSIS (3 s.h.)
Cross-reference: CS:4740 -administrative home. Current areas that deal with problem of Big Data; techniques from computer science, mathematics, statistics; high performance and parallel computing, matrix techniques, cluster analysis, visualization; variety of applications including Google PageRank, seismology, Netflix-type problems, weather forecasting; fusion of data with simulation; projects.  Prerequisites: (CS:1210 or ENGR:2730) and (MATH:2700 or MATH:2550) and (STAT:2010 or STAT:2020).

STAT:5090 (22S:170) ALPHA SEMINAR (1 s.h.)
Resources available to students, program requirements, tips for academic success, professional statistical organizations, library and career center resources, statistical computing, scientific document preparation, history of statistics. Corequisite: Graduate standing in the Statistics program.
Syllabus

STAT:5100 (22S:193) STATISTICAL INFERENCE I) (3 s.h.)
Review of probability, distribution theory (multiple random variables, moment-generating functions, transformations, conditional distributions), sampling distributions, order statistics, convergence concepts, generating random samples.
Corequisites: MATH:2850 (22M:028) and STAT:3101 (22S:131).
Offered fall semesters.
Syllabus

STAT:5101 (22S:194) STATISTICAL INFERENCE II (3 s.h.)
Continuation of STAT:5100 (22S:193); principles of data reduction, point estimation theory (MLE, Bayes, UMVU), hypothesis testing, interval estimation, decision theory, asymptotic evaluations.
Prerequisite: STAT:5100 (22S:193).
Offered spring semesters.
Syllabus

STAT:5120 (22S:190) MATHEMATICAL METHODS FOR STATISTICS (3 s.h.)
Real numbers, point set theory, limit points, limits, sequences and series, Taylor series (multivariate), uniform convergence, Riemann-Stieltjes integrals.
Prerequisite: graduate standing in Statistics.
Offered spring semesters.
Syllabus

STAT:5200 (22S:164) APPLIED STATISTICS I (4 s.h.)
Introduction to computing environments and statistical packages, descriptive statsitics, basic inferential methods (confidence intervals, chi-square tests); linear models (regression and ANOVA models -- specification and assumption, fitting, diagnostics, selection, testing, interpretation.
Prerequisite: STAT:3101 (22S:131)
Corequisite: STAT:5100 (22S:193) or STAT:4100 (22S:153)

Requirements: facility with matrix algebra.
Offered fall semesters.
Syllabus

STAT:5201 (22S:165) APPLIED STATISTICS II (3 s.h.)
Design of experiments, analysis of designed experiments.  Recommendation: Prior exposure to SAS software.
Prerequisite: STAT:5200 (22S:164).
Offered spring semesters.
Syllabus

STAT:5400 (22S:166) COMPUTING IN STATISTICS (3 s.h.)
R; database management; graphical techniques; importing graphics into word-processing documents (e.g., LaTeX); creating reports in LaTeX; SAS; simulation methods (Monte Carlos studies, bootstrap, etc.).  Prerequisites:  STAT:3200 and (STAT:3120 or STAT:3101 or STAT:4101).
Corerequisites:  STAT:5100 and STAT:5200 if not already taken.Offered fall semesters. Syllabus

STAT:5543 (22S:102) INTRODUCTION TO STATISTICAL METHODS (3 s.h.) has been CHANGED (10/28/13)  to: STAT:4143 / PSQF:4143

STAT:5610 (22S:140) DESIGN AND ANALYSIS OF BIOMEDICAL STUDIES (3 s.h.)
Cross-reference: BIOS:5120 (171:161) -administrative home.

STAT:5810 (22S:201) RESEARCH DATA MANAGEMENT (3 s.h.)
Cross-Reference:  BIOS:5310 (administrative home). 
Overview of problems encountered in gathering and processing data from biomedical investigations; introduction to data management techniques useful in biomedical studies; introduction to Microsoft Access.  Offered fall semesters of odd years.  Requirements:  Fortran, C or Python programming capability or equivalent programming experience.

STAT:6220 (22S:173) STATISTICAL CONSULTING (3 s.h.)
Realistic supervised data analysis experiences, including statistical packages, statistical graphics, writing statistical reports, dealing with complex or messy data.
Prerequisites: STAT:3200 (22S:152) and STAT:3210 (22S:158), or STAT:5200 (22S:164) and STAT:5201 (22S:165).
Offered spring semesters.  Requirement:  Undergraduate majors should have within major GPA of 3.0 or higher.
Syllabus

STAT:6300 (22S:195) PROBABILITY AND STOCHASTIC PROCESSES I (3 s.h.)
Conditional expectations; Markov chains, including random walks and gambler's ruin; classification of states; stationary distributions; branching processes; Poisson processes; Brownian motion.
Prerequisites: STAT:4100 (22S:153) or consent of the instructor. 
Syllabus

STAT:6301 (22S:196) PROBABILITY AND STOCHASTIC PROCESSES II (3 s.h.)
Markov chains with continuous state space, Martingales, random walks, Brownian motion and other continuous-time Markov chains, simulation methods.
Prerequisite: STAT:6300 (22S:195).
Syllabus

STAT:6510 (22S:162) APPLIED GENERALIZED REGRESSION (3 s.h.)
Applications of semiparametric models, generalized linear models, nonlinear normal errors models, correlated response models; use of statsitical packages, especially R and SAS.
Requirements: introductory statistics and applied linear models.
Syllabus

STAT:6513 (22S:148) INTERMEDIATE STATISTICAL METHODS (3 s.h.)
Cross-reference: PSQF:6243 (07P:243) -administrative home.

STAT:6514 (22S:157) CORRELATION AND REGRESSION (4 s.h.)
Cross-reference: PSQF:6244 (07P:244) -administrative home.

STAT:6516 (22S:159) DESIGN OF EXPERIMENTS (4 s.h.)
Cross-reference: PSQF:6246 (07P:246) -administrative home.

STAT:6530 (22S:167) ENVIRONMENTAL AND SPATIAL STATISTICS (3 s.h.)
Methods for sampling environmental populations, sampling design, trend detection and estimation, geostatistics, kriging, variogram estimation, lattice data analysis, analysis of spatial point patterns.
Prerequisites: STAT:3200 (22S:152) and STAT:4101 (22S:154).
Offered spring semesters of odd years.
Syllabus

STAT:6540 (22S:161) APPLIED MULTIVARIATE ANALYSIS (3 s.h.)
Cross-reference: PSQF:6245 (07P:245) -administrative home.
Multivariate descriptive statistics, multivariate normal distribution, Hotelling's T-squared, MANOVA, multivariate regression, principal components, discrimination and classification, cluster analysis.
Prerequisites: STAT:3200 (22S:152) and STAT:3210 (22S:158).
Requirements: facility with matrix algebra.
Syllabus

STAT:6547 (22S:163) NONPARAMETRIC STATISTICAL METHODS (3 s.h.)
Cross-reference: PSQF:6247 -administrative home.

STAT:6550 (22S:160) INTRODUCTORY LONGITUDINAL DATA ANALYSIS (3 s.h.)
Same as BIOS:6310 -administrative home.

STAT:6560 (22S:156) APPLIED TIME SERIES ANALYSIS (3 s.h.)
General stationary, nonstationary models, autocovariance autocorrelation functions; stationary, nonstationary autoregressive integrated moving average models; identification, estimation, forecasting in linear models; use of statistical computer packages.
Prerequisites: STAT:3101 (22S:131), and STAT:3200 (22S:152) or STAT:5200 (22S:164).
Offered spring semesters.
Syllabus

STAT:6970 (22S:172) TOPICS IN STATISTICS (3 s.h.)
Repeatable.
Prerequisite: STAT:4101 (22S:154).
Syllabus

STAT:6990 (22S:197) READINGS IN STATISTICS
Repeatable.
Instructor has the option of using S-U grades.

STAT:7100 (22S:253) ADVANCED INFERENCE I (3 s.h.)
Concepts of convergence, asymptotic methods including the delta method, sufficiency, asymptotic efficiency, Fisher information and information bounds for estimation, maximum likelihood estimation, the EM-algorithm, Bayes estimation, decision theory.
Prerequisites: STAT:5101 (22S:194) and STAT:5120 (22S:190).
Syllabus

STAT:7101 (22S:254) ADVANCED INFERENCE II (3 s.h.)
Hypothesis testing, asymptotics of the likelihood ratio test, asymptotic efficiency, statistical functionals, robustness, bootstrap and jackknife, estimation with dependent data.
Prerequisite: STAT:7100 (22S:253).
Syllabus

STAT:7190 (22S:291) SEMINAR: MATHEMATICAL STATISTICS (arr.)
Repeatable.
Instructor has the option of using S-U grades.

STAT:7200 (22S:255) LINEAR MODELS (4 s.h.)
Linear spaces and selected topics in matrix theory, multivariate normal distribution and distributions of quadratic forms, full-rank and non-full-rank linear models, estimability, least squares and best linear unbiased estimator, interval estimation, hypothesis testing, random and mixed models, best linear unbiased prediction, variance component estimation. 
Prerequisites: STAT:5101 (22S:194), STAT:5200 (22S:164) and STAT:5201 (22S:165).
Syllabus

STAT:7290 (22S:295) SEMINAR: APPLIED STATISTICS (arr.)
Repeatable.
Instructor has the option of using S-U grades. Syllabus

STAT:7300 (22S:203) FOUNDATIONS OF PROBABILITY I (3 s.h.)
Probability theory, with emphasis on constructing rigorous proofs; measure spaces, measurable functions, random variables and induced measures, distribution functions, Lebesque integral, product measure and independence, Borel Cantelli lemma, modes of convergence.
Prerequisite: STAT:5120 (22S:190).
Syllabus

STAT:7301 (22S:204) FOUNDATIONS OF PROBABILITY II (3 s.h.)
Laws of large numbers, characteristic functions and properties, central limit theorem, Radon-Nikodym derivatives, conditional expected value and martingales.
Prerequisite: STAT:7300 (22S:203).
Syllabus

STAT:7390 (22S:293) SEMINAR: PROBABILITY (arr.)
Repeatable.
Instructor has the option of using S-U grades.

STAT:7400 (22S:248) COMPUTER INTENSIVE STATISTICS (3 s.h.)
Computer arithmetic; random variate generation; numerical optimization; numerical linear algebra; smoothing techniques; bootstrap methods; cross-validation; MCMC; EM and related algorithms, other topics per student/instructor interests.
Prerequisites: STAT:5200 (22S:164) or BIOS:5610 (171:201); and STAT:3101 (22S:131). Requirements: proficiency in Fortran or C or C++ or Java.
Offered spring semesters.
Syllabus

STAT:7510 (22S:220) ANALYSIS OF CATEGORICAL DATA (3 s.h.)
Models for discrete data, distribution theory, maximum likelihood and weighted least squares estimation for categorical data, tests of fit, models selection.
Prerequisites: STAT:4101 (22S:154) or STAT:5101 (22S:194), and STAT:5200 (22S:164) or BIOS:5620 (171:202).
Same as BIOS:7410 (171:262).
Offered spring semesters.
Syllabus  

STAT:7520 (22S:238) BAYESIAN ANALYSIS (3 s.h.)
Decision theory, conjugate families, structure of Bayesian inference, hierarchical models, asymptotic approximations for posterior distributions, Markov chain Monte Carlo methods and convergence assessment, model adequacy and model choice.
Prerequisites: STAT:5101 (22S:194), STAT:5200 (22S:164) and STAT:5400 (22S:166).
Syllabus

STAT:7560 (22S:235) TIME SERIES ANALYSIS (3 s.h.)
Stationary time series, ARIMA models, spectral representation, linear prediction inference for the spectrum, multivariate time series, state space models and processes, nonlinear time series.
Prerequisites: STAT:4101 (22S:154) and STAT:6560 (22S:156).
Syllabus

STAT:7570 (22S:225) SURVIVAL DATA ANALYSIS (3 s.h.)
Cross-reference: BIOS:7210 -administrative home.

STAT:7990 (22S:299) READING RESEARCH (arr.)
Repeatable.
Instructor has the option of using S-U grades.

Actuarial Science Course Descriptions & Syllabi

ACTS:1000 FIRST YEAR SEMINAR (1 s.h.)
Introduction to actuarial science; U.S. actuarial organizations and actuarial qualification process; program requirements and tips for academic success; career center, actuarial club, and internships; actuarial career; ethics; communication; introduction to actuarial computing.   Students will investigate and report on different actuarial career paths.  Students will use Excel worksheets to produce annuity and loan schedules and use these worksheets for sensitivity analysis.  Students will gain valuable skills and resources for success in the Actuarial Science major.
Pre-requisites:  none
Co-requisites:  none
Requirements:  first- or second-semester standing
Recommendations:  none
Special Grading:  none

ACTS:1001 (22S:28) INTRODUCTORY SEMINAR ON ACTUARIAL SCIENCE (1 s.h.)
Introduction to actuarial science; US actuarial organizations and the actuarial qualification process; program requirements and tips for academic success; career center, actuarial club and internships; actuarial career; ethics; communication; introduction to actuarial computing.  New:  Fall 2013.  Requirements:  Actuarial science interest major, first-year standing, or consent of instructor.  

ACTS:3080 (22S:180) MATHEMATICS OF FINANCE I (3 s.h.)
Mathematics of compound interest, including annuities certain, amortization schedules, yield rates, sinking funds, bonds.
Prerequisites: STAT:3100 (22S:130).
Requirements: grade of B- or higher in STAT:3100 (22S:130).
Offered fall and spring semesters.
Syllabus

ACTS:3110 (22S:188) ACTUARIAL EXAM P PREPARATION (1 s.h.)
Preparation for the Society of Actuaries exam P.
Offered on S-F basis only for undergraduates; optional use of S-U grades for gradaute students.

ACTS:3210 (22S:189) ACTUARIAL EXAM FM PREPARATION (1 s.h.)
Preparation for the Society of Actuaries exam FM.
Offered on S-F basis only for undergraduates; optional use of S-U grades for gradaute students.
Prerequisites: ACTS:3080 (22S:180) or ACTS:3085 (22S:179).
Corequisites: ACTS:3080 (22S:180) or ACTS:3085 (22S:179).
Requirements: Students enrolled in ACTS:3210 (22S:189) must either have taken or be taking ACTS:3080 (22S:180) or ACTS:3085 (22S:179).

ACTS:4110 (22S:199) ACTUARIAL EXAM MLC PREPARATION (1 s.h.)
Preparation for the Society of Actuaries and the Casualty Actuarial Society exams.
Offered on S-F basis only for undergraduates; optional use of S-U grades for gradaute students.
Prerequisites: ACTS:4280 (22S:182).
Corequisites: ACTS:4280 (22S:182).
Requirements: Students enrolled in ACTS:4110 (22S:199) must either have taken ACTS:4280 (22S:182) or be taking ACTS:4280 (22S:182).

ACTS:4130 (22S:174) QUANTITATIVE METHODS FOR ACTUARIES (3 s.h.)
Survival distributions, life tables, mathematics of derivatives. Offered fall and spring semesters.
Requirements: multivariate calculus and linear algebra.
Pre-requisites:  none Co-requisites:  ACTS:3080 and (STAT:4100 or STAT:5100)

Syllabus

ACTS:4160 TOPICS IN ACTUARIAL SCIENCE (arr. s.h.) Selected topics in actuarial science, financial mathematics and quantitative risk management, not covered in other courses. Pre-requisites:  none Co-requisites:  none Requirements:  none Recommendations:  none Special Grading: Offered on S-F basis only for undergraduates; instructor has the option of using S-U grades for graduate level students

ACTS:4180 (22S:181) LIFE CONTINGENCIES I (3 s.h.)
Life insurance, life annuities, benefit premiums and reserves.
Prerequisites: STAT:4100 (22S:153) or STAT:5100 (22S:193); ACTS:3080 (22S:180) or ACTS:3085 (22S:179); and ACTS:4130 (22S:174). Co-requisites:  none Requirements:  none

Recommendations:  none
Syllabus

ACTS:4280 (22S:182) LIFE CONTINGENCIES II (3 s.h.)
Continuation of ACTS:4180 (22S:181); net and gross premium reserves, multistate models, universal life insurance, interest rate risk.
Prerequisites: ACTS:4180 (22S:181).
Requirements: grade of C+ or higher in ACTS:4180 (22S:181).
Offered fall semesters.
Syllabus

 ACTS:4380 (22S:183) MATHEMATICS OF FINANCE II (3 s.h.)
Derivatives markets, options on stocks and interest rates, financial applications. Pre-requisites:  ACTS:3080 with a minimum grade of C+ Co-requisites:  STAT:4100 or STAT:5100 Requirements:  multivariate calculus and linear algebra
Syllabus

ACTS:6160 (22S:171) TOPICS IN ACTUARIAL SCIENCE (3 s.h.)
Repeatable.
Prerequisites: ACTS:4180 (22S:181) and ACTS:4380 (22S:183).
Requirements: grades of C+ or higher in ACTS:4180 (22S:181) and in ACTS:4380 (22S:183).
Syllabus

ACTS:6480 (22S:177) LOSS DISTRIBUTIONS (3 s.h.)
Severity, frequency, and aggregate models and their modifications; risk measures; construction of empirical models.
Prerequisites: STAT:4101 (22S:154) or STAT:5101 (22S:194).
Corequisites: ACTS:6580 (22S:176).
Requirements: grade of C+ or higher in STAT:4101 (22S:154) or in STAT:5101 (22S:194).
Offered spring semesters.
Syllabus

ACTS:6580 (22S:176) CREDIBILITY AND SURVIVAL ANALYSIS (3 s.h.)
Construction and selection of parametric models; credibility; simulation.
Prerequisites: STAT:4101 (22S:154) or STAT:5101 (22S:194).
Corequisites: ACTS:6480.
Requirements: grade of C+ or higher in STAT:4101 (22S:154) or in STAT:5101 (22S:194).
Offered spring semesters.
Syllabus

ACTS:7730 (22S:273) ADVANCED TOPICS IN ACTUARIAL SCIENCE/FINANCIAL MATHEMATICS (arr.)
Repeatable.
Instructor has the option of using S-U grades for graduate students.
Syllabus