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

     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

     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

     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.