Cleveland Chapter of the ASA Statistical Courses

There are many statistical course offerings in the Cleveland area, either at universities or through private consultants. Meeting announcements and this Web site will contain information on some of these offerings.

If you have course information to include in a monthly meeting announcement, please send this information to Susan Cowling, svc@lubrizol.com , the Chapter secretary. Information received by the 15th of the month will be published in the next month's meeting announcement.

Data Analysis in S-PLUS A StatSci Educational Services Course

This course covers the basics of S-PLUS with an emphasis on using S-PLUS to analyze data. It describes the basics of the language, including user-defined functions. Tools for data analysis in S-PLUS are explored, with extended discussion of the overall data analysis process, graphing data, data management, and the most common statistical procedures.

This course will benefit anyone interested in the basics of the S-PLUS language.

Length: 4 days CEU: 2.8 (28 contact hours) Cost: $1550 per person

Day 1, AM: Essentials of S-PLUS - Introduction - Starting S-PLUS - Data Types - Data Objects: Vectors, Matrices & Data Frames - Subscripting Data Objects - Mathematical and Logical Operators - Using Built-In Functions - Basic Graphics

Day 1, PM: Basic Steps in Data Analysis - Reading and Editing Data - Restructuring and Summarizing Data - Graphing Data - Constructing Models and Testing Hypotheses - Printing and Saving Results

Day 2, AM: More Data Objects & Syntax - Data Objects - Automating Analyses - Writing Functions - Object Orientation

Day 2, PM: Graphics - Overview of Graphics Functions - Univariate Data - Bivariate Data: Scatterplots - Trivariate Data - Higher-Dimensional Data - Multiple Plots Per Page

Day 3, AM: Data Management & Manipulation - Reading Data - Data and Directory Management - Data Manipulation

Day 3, PM: Testing and Modeling - Classical Statistical Tests - Linear Models - Analysis of Variance

Day 4 AM: Tricks and Troubleshooting -Frequently Asked Questions - Increasing Efficiency -Troubleshooting -Debugging Functions

Day 4, PM: Questions & Answer/Work Session

For more information, contact:
Kevin A. Keadle
Statistical Sciences, Division of MathSoft, Inc.
1700 Westlake Avenue North
Suite 500
Seattle, WA 98109
e-mail: kevin@statsci.com
phone (800) 569-0123 x247
(206) 283-8802 x247

Case Western Reserve University - Statistics Offerings, Fall 1996

Graduate / Undergraduate Courses in Statistical Methods and Data Analysis

STAT 312 Basic Statistics for Engineering and Physical Sciences
Balanced approach to statistical methods providing equal emphasis on probability and fundamental concepts of statistics and on point and interval estimation, hypothesis testing, regression modeling, error diagnostics.
Tuesday & Thursday 9:30 - 10:45 3 credit hours

STAT 313 Statistics for Experimenters
Statistical methods focusing on the practicalities of inference from experimental data. Inference for curve and surface fitting to real data sets, designs for experiments and for simulations, regression diagnostics and model critique.
Monday, Wednesday & Friday 12:30 - 1:20 3 credit hours

STAT 332 Statistics for Signal Processing
Emphasis on probabilities as relative frequencies, derivation for memoryless channels. Observations in a series, stationarity, random harmonic signals with noise, random phase and/or amplitude, transmission through filters, power spectra, forecasting.
Tuesday & Thursday 9:30 - 10:20 3 credit hours

STAT 325 Data Analysis I
See STAT 425 below 3 credit hours

Graduate Courses in Statistical Methodology and Statistical Theory

STAT 412 Statistics for Design and Analysis in Engineering and Science
Statistical methods for linear models intended for graduate students involved in research. Content structured around experimental design issues. Descriptive statistics, point and interval estimation, hypothesis testing, curve and surface fitting.
Monday, Wednesday, & Friday TBA 3 credit hours

STAT 414 Industrial Statistics
Statistical problem-solving for researchers and engineers. Analysis and monitoring of processes, both discrete and continuous. Rationale and implementation for simulation and resampling. Special topics* depending on composition of class.
Tuesday & Thursday ** 6:00 - 7:15 pm 3 credit hours
*Statisticians interested in this course are invited to suggest topics of particular interest.

STAT 425 Data Analysis I
Basic exploratory data analysis for univariate response observations. Graphical methods, data projection onto low dimensional subspaces, model building, model selection, robust/resistant methods, case studies in data analysis.
Tuesday & Thursday** 1:15 - 2:30 3 credit hours

STAT 445 Theoretical Statistics I
Statistical theory (without measure theory) for entering graduate students. Random variables, common distributions, distributions of sample quantities, methods of inference. Mathematical derivations and proofs.
Tuesday & Thursday** 9:30 - 10:45 3 credit hours

STAT 455 Linear Models
Theory of least squares estimation. Interval estimation and tests for models with normally distributed errors. Regression, analysis of variance, analysis of covariance, variance components models, mixed models.
Tuesday & Thursday** 2:45 - 4:00 3 credit hours

STAT 545 Advanced Theory of Statistics
Statistical theory for advanced graduate students. Development of advanced statistical theory beginning with background concepts. Characterization of distribution functions, asymptotic theory. Distribution theory for functions of asymptotically normal variables.
Tuesday & Thursday** 11:30 - 12:20 3 credit hours

** Scheduled class time is tentative and could be changed (e.g., to late day) to allow registration of interested students.

RealWorld Quality Systems' Training Courses in Statistical Foundations

Topic 1: Statistical Fundamentals is a 16-hour course that provides many simple yet powerful tools for fully exploring and analyzing a single set of data. Many simple statistics and a wide variety of graphs are presented. Also the key concepts of reproducibility, significance, confidence interval and normality are covered. Throughout Topic 1 (as well as Topics 2-7), emphasis is placed on drawing and summarizing conclusions complete with reliability / probability statements.

Topic 2: Analysis of Variance is a 12-hour course that provides a set of tools for comparing at first two sets of data and then three or more sets of data. All comparisons of data sets are made with respect to shape of the data distributions, their centers and dispersion of data around these centers.

Topic 3: Design and Analysis of Experiments is a 24-hour course that covers the critical tools for solving problems involving 1 or 2 independent variables. It begins with the simplest of cases, 1 independent variable at only two levels (i.e. settings) and progresses sequentially through 1 independent variable at 2 levels, 2 independent variables at 2 levels each and, lastly, 2 independent variables at 2 levels each. Both quantitative and qualitative variables (with and without blocking) are addressed in each scenario. Hence, both the ANOVA-style problems of comparison of discrete populations as well as the regression-style problems of building mathematical models are tackled.

Topic 4: Regression Techniques is a 12-hour course that provides the fundamental background, concepts, tools and methods on developing mathematical models / equations that reliably and succinctly describe simple relationships in data. By using the simplest case of a single independent variable, critical issues can be mastered easily and potential pitfalls demonstrated. Course graduates will be empowered with a set of tools to construct appropriate models, test them for significance, ascertain the adequacy of the resultant fit to the data and predict future values of Y complete with quantitative statements of confidence in their conclusions.

Topic 5: Statistical Methods Using RS/1 & RS/EXPLORE Software is a 16-hour course whose objective is to further illustrate, expand and automate the statistical techniques presented in Topics 1 to 4. Hence, Topic 5 focuses on the "how to" aspect of statistical analyses by teaching both the mechanics and the interpretation of output from the RS/1 and RS/EXPLORE software modules.

Topic 6: Advanced Design of Experiments, Regression and Optimization is a 24-hour course that provides the students with a wide variety of tools that are essential for efficiently and effectively tackling complex real world problems. Efficient designs for quantifying the linear and synergistic effects of several independent variables simultaneously are covered first. This is followed by an in-depth study of designs that permit the quantification of curvature in the cause and effect relationships. Next, mixture designs are presented for handling the scenario where the independent variables must conform to the constraint that their values sum to a constant, like 100%. Finally, robust product and process design, pioneered by Dr. Taguchi, is covered. For all classes of designs, appropriate analysis is thoroughly discussed along with practical applications and examples.

Topic 7: Implementation of Total Quality in a Research and Development Environment is an 8-hour course whose objective is to introduce a generic TQ implementation scheme specifically designed for the unique needs of R&D. This scheme is based on a hierarchical set of systems: The Equipment System, The Project / Problem Solving System, the Researcher's Personal System, The Group's System and, lastly, The Global TQ System. Each of these systems employs tools to discover critical cause and effect relationships that are the cornerstone of continuous improvement.

For more information, contact Dennis J. Keller at RealWorld Quality Systems, Inc. at (216) 333-1010.

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