Courses - Spring 2024
STAT
Statistics and Probability
Open Seats as of
09/11/2024 at 06:30 PM
STAT100
Elementary Statistics and Probability
Credits: 3
GenEd: ,
Prerequisite: MATH110, MATH112, MATH113, or MATH115; or permission of CMNS-Mathematics department; or must have math eligibility of STAT100 or higher and math eligibility is based on the Math Placement Exam or the successful completion of Math 003 with appropriate eligibility.
Restriction: Must not have completed MATH111; or must not have completed any STAT course with a prerequisite of MATH141.
Cross-listed with: DATA100.
Credit only granted for: DATA100 or STAT100.
Simplest tests of statistical hypotheses; applications to before-and-after and matched pair studies. Events, probability, combinations, independence. Binomial probabilities, confidence limits. Random variables, expected values, median, variance. Tests based on ranks. Law of large numbers, normal approximation. Estimates of mean and variance.
STAT110
Applications of R for Data Science
Credits: 1
Prerequisite: DATA100, STAT100, or MATH135; or any 400-level STAT course.
Cross-listed with: DATA110.
Credit only granted for: STAT110 or DATA110.
Intended to prepare students for subsequent courses requiring computation with R, providing powerful and easy to use tools for statistical data analysis. Covers basics of R and R Studio including file handling, data simulation, graphical displays, vector and function operations, probability distributions, and inferential techniques for data analysis.
STAT400
Applied Probability and Statistics I
Credits: 3
Prerequisite: 1 course with a minimum grade of C- from (MATH131, MATH141); or students who have taken courses with comparable content may contact the department.
Cross-listed with: DATA400.
Credit only granted for: DATA400, ENEE324, or STAT400.
Random variables, standard distributions, moments, law of large numbers and central limit theorem. Sampling methods, estimation of parameters, testing of hypotheses.
STAT401
Applied Probability and Statistics II
Credits: 3
Prerequisite: 1 course with a minimum grade of C- from (STAT400, STAT410).
Point estimation - unbiased and consistent estimators. Interval estimation. Minimum variance and maximum likelihood estimators. Testing of hypotheses. Regression, correlation and analysis of variance. Sampling distributions. Elements of non-parametric methods.
STAT410
Introduction to Probability Theory
Credits: 3
Prerequisite: 1 course with a minimum grade of C- from (MATH240, MATH461, MATH341); and 1 course with a minimum grade of C- from (MATH340, MATH241).
Cross-listed with: SURV410.
Credit only granted for: STAT410 or SURV410.
Probability and its properties. Random variables and distribution functions in one and several dimensions. Moments. Characteristic functions. Limit theorems.
STAT420
Theory and Methods of Statistics
Credits: 3
Prerequisite: 1 course with a minimum grade of C- from (SURV410, STAT410).
Cross-listed with: SURV420.
Credit only granted for: STAT420 or SURV420.
Point estimation, sufficiency, completeness, Cramer-Rao inequality, maximum likelihood. Confidence intervals for parameters of normal distribution. Hypothesis testing, most powerful tests, likelihood ratio tests. Chi-square tests, analysis of variance, regression, correlation. Nonparametric methods.
STAT426
Introduction to Data Science and Machine Learning
Credits: 3
Prerequisite: Minimum grade of C- in MATH241 or MATH340; and minimum grade of C- in MATH240, MATH461 or MATH341; and minimum grade of C- in STAT400 or STAT410; students who have taken courses with content comparable to STAT400/410 may request permission of the instructor.
Credit only granted for: STAT426 or CMSC320.
An introductory course to the recent developments in the fields of data science and machine learning. Emphasis will be given to mathematical and statistical understanding of commonly used methods and processes.
STAT430
Introduction to Statistical Computing with SAS
Credits: 3
Prerequisite: 1 course with a minimum grade of C- from (STAT400, STAT410); and must have completed or be concurrently enrolled in STAT401 or STAT420; students who do not meet the STAT401 or STAT420 requirement but who have taken a statistics course may contact the math department to confirm eligibility.
Descriptive and inferential statistics. SAS software: numerical and graphical data summaries; merging, sorting and splitting data sets. Least squares, regression, graphics and informal diagnostics, interpreting results. Categorical data, lifetime data, time series. Applications to engineering, life science, business and social science.
STAT440
Sampling Theory
Credits: 3
Prerequisite: 1 course with a minimum grade of C- from (STAT401, STAT420).
Credit only granted for: STAT440 or SURV440.
Simple random sampling. Sampling for proportions. Estimation of sample size. Sampling with varying probabilities. Sampling: stratified, systematic, cluster, double, sequential, incomplete.
STAT464
Introduction to Biostatistics
Credits: 3
Prerequisite: Must have completed one semester of calculus.
Restriction: Junior standing or higher.
Credit only granted for: BIOE372 or STAT464.
Additional information: Not acceptable toward degrees in MATH/STAT.
Probabilistic models. Sampling. Some applications of probability in genetics. Experimental designs. Estimation of effects of treatments. Comparative experiments. Fisher-Irwin test. Wilcoxon tests for paired comparisons.
STAT470
Actuarial Mathematics
Credits: 3
Prerequisite: 1 course with a minimum grade of C- from (MATH240, MATH461, MATH341); and 1 course with a minimum grade of C- from (MATH340, MATH241).
Recommended: STAT400.
Major mathematical ideas involved in calculation of life insurance premiums, including compound interest and present valuation of future income streams; probability distribution and expected values derived from life tables; the interpolation of probability distributions from values estimated at one-year multiples; the 'Law of Large Numbers' describing the regular probabilistic behavior of large populations of independent individuals; and the detailed calculation of expected present values arising in insurance problems.
STAT498A
(Perm Req)
Selected Topics in Statistics
Credits: 1 - 6
Contact department for information to register for this course.
STAT601
Probability Theory II
Credits: 3
Prerequisite: STAT600.
Weak convergence of measures; characteristic functions; Central Limit Theorem and local limit theorem; stable laws; Kolmogorov consistency theorem (without proof); conditional expectations and martingales; optimal stopping theorem; convergence of martingales; Brownian motion; Markov processes and families; stochastic integral and Ito formula.
Offered Spring only.
STAT650
Applied Stochastic Processes
Credits: 3
Prerequisite: STAT410; or students who have taken courses with comparable content may contact the department.
Basic concepts of stochastic processes. Markov processes (discrete and continuous parameters), Random walks, Poisson processes, Birth and death processes. Renewal processes and basic limit theorems. Discrete time martingales, stopping times, optional sampling theorem. Applications from theories of stochastic epidemics, survival analysis and others.
STAT689
(Perm Req)
Research Interactions in Statistics
Credits: 1 - 3
Contact department for information to register for this course.
STAT701
Mathematical Statistics II
Credits: 3
Prerequisite: STAT700; or students who have taken courses with comparable content may contact the department.
Testing hypotheses: parametric methods. Neyman-Pearson lemma. Uniformly most powerful tests. Unbiased tests. Locally optimal tests. Large sample theory, asymptotically best procedures. Nonparametric methods, Wilcoxon, Fisher-Yates, median tests. Linear models, analysis of variance, regression and correlation. Sequential analysis.
Offered Spring only.
STAT707
Bayesian Statistics
Credits: 3
Prerequisite: STAT700; or permission of instructor .
Credit only granted for: STAT700 or STAT818B.
Formerly: STAT818B.
The essentials of Bayesian statistics with some advanced topics. Basic statistical decision theory. Bayesian paradigm. Prior and posterior distributions. Conjugate family. Hierarchical models. Bayesian linear regression. Bayes factors. Markov chain Monte Carlo. Metropolis-Hastings algorithm. Gibbs sampler. Bernstein von-Mise theorem. Posterior consistency. Potential advanced topics include variational Bayes, empirical Bayes, Bayesian inference of high-dimensional data and Bayesian non-parametric inference.
STAT741
Linear Statistical Models II
Credits: 3
Prerequisite: STAT740.
Continuation of STAT 740. Multiway layouts, incomplete designs, Latin squares, complete and fractional factorial designs, crossed and nested models. Balanced random effects models, mixed models, repeated measures.General mixed model, computational algorithms, ML and REML estimates. Generalized linear models, logistic and loglinear regression.
Offered Spring only.
STAT770
Analysis of Categorical Data
Credits: 3
Prerequisite: STAT430 and STAT420; or permission of CMNS-Mathematics department.
Loglinear and logistic models. Single classification, two-way classification; contingency tables; tests of homogeneity and independence models, measures of association, distribution theory. Bayesian methods. Incomplete contingency tables. Square contingency tables - symmetry. Extensions to higher dimensional contingency tables.
STAT799
Master's Thesis Research
Credits: 1 - 6
Contact department for information to register for this course.
STAT808A
Selected Topics in Probability
Credits: 1 - 3
Contact department for information to register for this course.
STAT818A
Selected Topics in Statistics
Credits: 1 - 3
Contact department for information to register for this course.
STAT818O
Selected Topics in Statistics; Introduction to Stochastic Partial Differential Equations
Credits: 1 - 3