Beyond The Formula

Team Writing Projects with Real Data
for Introductory Statistics

John P. Holcomb, Jr.
Cleveland State University


Monroe Community College
Rochester, NY
August 3, 2001



I. Overview
Movement within statistics education to utilize
  1. Real data
  2. Technology
  3. Collaborative learning
  4. Writing

II. History of Projects
  • Project Book
    Chatterjee, S., Handcock, M. and Simonoff, J. (1995). A Casebook for a First Course in Statistics and Data Analysis, John Wiley & Sons, Inc., New York.
  • Term Long Projects
  • One trick ponies in text book
  • Writing as part of General Education Requirement

III. Goals of the Projects
  1. Students gaining valuable experience with real data
  2. Students acquiring communication skills by writing technical reports that summarize results clearly and concisely
  3. Students learning how to use statistical and word processing software as tools to solve problems and communicate results
  4. Students acquiring skills in working with others
  5. Students learning to apply appropriate methodology
IV. Supporting Information/Documentation
  1. Context of Cleveland State University
  2. Group formation Sheet
  3. Grading Rubric
  4. Software Guide
  5. Sample Project
  6. Group Role idea
    Walker C. and Angelo, T. (1998). A collective effort classroom assessment technique: promoting high performance in student teams, in Classroom assessment and research: an update on uses, approaches, and research findings, Thomas Angelo (ed.), Jossey-Bass Publishers, San Francisco, p. 101-112.
VII. Data Sets
  1. ncbirth1450.mtw
  2. ncbirth1450.xls
  3. ncbirth200.mtw
  4. ncbirth200.xls
VI. Project Topics
  1. Summary Analysis
  2. Probability
  3. Hypothesis Testing
  4. Correlation and Regression
VII. Relative Risk
  1. domviolence.mtw
  2. domviolence.xls
  3. domviolence.doc
  4. domviolence.pdf
  5. Relative_Risk.doc
  6. Relative_Risk.pdf
VIII. Modifications Planned
  1. Checking of assumptions
  2. Linking of graphics to inference
  3. identification of population
  4. implications of inference