List of Courses: ================ For over forty years Professor Romeu has taught statistics, operations research, and computer science courses at five institutions: Syracuse University, SUNY Cortland, SUNYIT, the Fulbright Organization and RIAC. Courses at Syracuse University: =============================== MAS261: Introduction to Statistics for Business Students (SU). Descriptive statistics. Probability. One/two sample hypothesis testing and estimation. Z, t tests. Correlation and Regression. Textbook: Statistical Methods, by Anderson, Sweeney and Williams. IOR313: Statistical Lab. (Syracuse University). Hypothesis testing and estimation for one and two sample cases. Simulation, regression, analysis of variance, correlation and nonparametric methods. Textbook: Probability and Statistics for Engineers and Scientists, by Walpole and Myers. MFE326: Statistics for Engineers. (Syracuse University). Calculus based statistics course: regression, analysis of variance, nonparametrics, life testing, reliability, quality control and simulation principles. Textbook: Introduction to Probability and Statistics for Engineers and Scientists, by Sheldon Ross. CIS326: Probability and Statistics for computer scientists. (SU) Calculus based statistics course: intro probability, discrete and continuous distributions: Hypergeometric, Binomial, Poisson, Uniform, Normal, Chi Square, Student t, Fisher's F, and regression and ANOVA. Textbook: Introduction to Probability and Statistics for Engineers and Scientists, by Sheldon Ross. MFE429: Modelling and Optimization Techniques. (Syracuse University). Introduction to major deterministic and stochastic techniques in O.R., including linear programming and its extensions, queueing models and its implementations and simulation. Computer applications. Textbook: Introduction to O.R. by Hillier and Lieberman. ECS526: Statistics for Engineers (Syracuse University). Probabil- ity, events, random variables, discrete and continuous distributions, variable transformations, moment generation, sampling distributions, point and interval estimation for means, proportions and variances, hypothesis tests for one and two populations, regression, analysis of variance, factorial experiements. Textbooks: Statistics for Engineers by Walpole and Myers and Practical Guide to Statistical Analysis of Materials Data by Romeu and Grethlein. IOR529: Regression and Data Analysis. (Syracuse University). Simple Linear Regression. Residual Analysis. Model Violations and transformations. Multiple regression. Multicollinearity and variable selection methods. ANOVA. Factorial experiments. Response surface methodology. Textbooks: Regression Analysis by Example by Chatterjee and Price and Statistics for Experimenters by Box, Hunter and Hunter. MFE634: Quality and Productivity Engineering (SU). Overview of all quality activities (technical and managerial) from the perspective of a quality certified engineer (American Society for Quality). Juran's quality principles and methodology; Six Sigma; Acceptance Sampling; Design of Experiments; Process Control and Reliability tools. Courses Taught at SUNY Cortland: ================================ CAP100: Introduction to Computer Applications. (4-cred-hrs). Personal Computers: operating systems, word processing, data base management, spread sheets, graphics and integrated software systems. Time sharing systems (VAX 11/750): operating systems, communications, runoff, Minitab and programming in BASIC. Textbook: Computers by Sumner. MAT101: Introduction to Mathematics. Sets, logic, number systems, properties, operations; abstract systems (groups). Textbook: Mathematical Ideas, by Heeren. MAT111: Algebra for College Students. Algebra of Functions. Graphing Functions. Compositions and Inverses. Exponential and Log Functions. Textbook: Algebra and Trigonometry by Sobel and Lerner. MAT115: Pre-Calculus Mathematics. Conics. Functions. Extremes. Systems of Equations. Trigonometric Equations. Sequences and Series. Graphing. Applications. Textbook: Algebra and Trigonometry by Sobel and Lerner. MAT125: Calculus I (for math majors). Mathematics based calculus course covering limits, continuity, differenciation, integration and applications. Textbook: Calculus, by Berkey. MCS186: Computer Programming I. (two versions: FORTRAN and Pascal). Sequential, branching and looping constructs, procedures, functions, arrays, sorting and searching and text file processing. Textbooks: FORTRAN for Humans, by Diday and Page. Pascal: Programming and Problem Solving, by Leestma and Nyhoff. MAT201: Introduction to Statistics (for non-math majors) Descriptive statistics. Probability. One/two sample hypothesis testing and estimation. Z, t tests. Correlation and Regression. Textbook: Statistical Methods, by Anderson, Sweeney and Williams. MAT224: Discrete Mathematics. Fundamentals of Logic. Proofs of Theorems. Mathematical Induction. Set Theory. Relations and Functions. Injective, Surjective, Bijective Functions. Composition and Inverses. Equivalence Relations. Groups and their Properties. Textbook: Discrete And Combinatorial Mathematics by R. P. Grimaldi. MCS287: Computer Programming II. (Two versions: FORTRAN and Pascal). Multidimensional arrays, records, sets, recursion, files other than text, pointers. List processing with data structures: stacks, queues and singly linked lists. Textbooks: FORTRAN for Humans, by Diday and Page. Pascal: Programming and Problem Solving, by Leestma and Nyhoff. MAT248: Mathematics for Management/Economics. Introduction to mathematical applications to economics and management science: linear systems, matrices, linear programming, simplex, dual and game theory. Textbook: Mathematics with Applications, by Harcourt, Brace and Jovanovich. CAP330: Simulation Modeling With GPSS. Introduction to Systems Analysis. Queues and inventories. Performance measures. GPSS Language. Simulation Validation and Output Analysis. Comparison and optimization of strategies. Experimental Design. Textbook: Simulation for Decision Making, by Thesen and Travis. MAT383: Statistics II (for non-math majors). Two Smaple t-tests, F tests for one/two variances, ANOVA, Linear Regression, Chi Square, Goodness-of-Fit, non-parametric methods, model building. Textbook: Statistical Methods, by Anderson, Sweeney and Williams. MCS365: Computer Programming III. Advanced Programming With Data Structures, including stacks, queues, linked lists, trees. Applications in simulation modeling/graph theory. Principles of software engineering. Textbook: Advanced Pascal With Data Structures by Kruse, Brown and Meade. MCS496: Introduction to Simulation Modeling. Simulation principles: clock, future events list, time flow mechanism, pseudo random number and variate generators, derivation and modeling of M/M/1 queue and related performance measures. Output analysis and comparison of methods. Simple experimentation. Programming in GPSS simulation language. Textbook: Introduction to Simulation Modeling by Banks and Carson. MAT558: Mathematical Statistics. Calculus based (graduate level) statistics course. Includes testing hypothesis and confidence interval derivation for one and two population means, proportions and variances, goodness-of-fit, contingency tables, ANOVA and simple and multiple regression analysis. MINITAB. Textbook: Statistics for Engineering and Computer Science. Also, Mathematical Statistics. MAT610: Introduction to Statistics. (for non-math graduates). Descriptive statistics. Intro to Probability. Inferential: one and two sample hypothesis testing and estimation. Z, t and F tests. Correlation and Regression. Nonparametric tests. Textbook: Introduction to Statistical Analysis by Dixon and Massey. Courses Taught at SUNY Polytechnich Institute ============================================= MGS411: Optimization Models (for Business Undergrads): A broad range of quantitative techniques and their applications in business, including: graphical & analytical approaches to linear programming, transportation, trans-shipment, networks (shortest path, maximum flow), integer and binary programming, multiple criteria optimzation methods. MGS511: Optimiation Models (for Business Graduate Students): Broad range of Management Science quantitative techniques and their applications in business: probability, decision theory, graphical/analytical linear programming methods, transportation, trans-shipment, networks (shortest path, maximum flow) and integer and binary programming. Graduate Level course. Courses at IITRI and Reliability Analysis Center (RAC): ======================================================= Reliability and Statistics for Engineers. (one-week-intensive practical course for professional engineers and managers). Covers: Normal, exponential and Weibull distributions. Hypothesis testing and estimation. Regression, ANOVA with reliability applications. Sequential testing. Computer packages in statistics. Textbook: Statistical Data Analysis for Material Science (Romeu and Grethlein). Lean-Six Sigma Green Belt. Two days, intensive introduction to Lean Manufacturing and Six Sigma methodology, combined. Targeted to managers, analysts and practicing engineers who want to learn what are the principles and objectives of this approach. Fulbright Senior Speaker Specialist Courses: ============================================ Simulation Modeling and Analysis (for mathematics majors). Systems Analysis. Mathematical description of queues and inventories. Basic results. GPSS Language. Validation, comparison and optimization of strategies. Output analysis. Methods of Replication, Batch means. Variance Reduction. Design of Factorial Experiments. Response Surfaces. Texts: Simulation for Decision Making, by Thesen and Travis and Simulation by Banks and Carson. Design of Experiments (for statistics graduate students): two sample problem; ANOVA statement for the t-test; one-way ANOVA, random blocks, Latin Squares, two and three way ANOVAS with/wo interaction; full factorial designs; blocking; confounding; fractional factorials; screening designs; Plackett-Burnam designs; response surface methods; evolutionary operation (EVOP) and Taguchi methods. Statistics for Business and Economics. Calculus based probability and statistics course. Descriptive. Probability. Distributions. Expected Value. Variance. Covariance. Correlation. Problems. Several texts included. Statistical analysis using Minitab and Excel software. Applications oriented course in the use of SW as classroom support. Especially for statistics faculty.