Mathews Malnar and Bailey, Inc. Quality engineering, applied statistical consulting, and training services for R&D, product, process, and manufacturing engineering organizations.

Sample Size Calculations for Process Improvement

Course Description: As companies have discovered the value of data-based decision making methods during their adoption of Six Sigma and Lean, they’ve become more conscious of the need to balance the costs associated with taking too much data with the risks associated with taking too few. The purpose of this course is to teach students how to right-size their data collection activities.

Students of this course will learn how to calculate sample sizes to test and quantify means, standard deviations, proportions, and counts. They will learn to apply these methods to data that will be analyzed by linear regression, correlation, designed experiments, reliability, statistical process control, acceptance sampling, process capability analysis, and gage error studies. Students will use manual calculation methods and software such as Russ Lenth's piface program (www.stat.uiowa.edu/~rlenth/Power/), PASS, MINITAB, and/or R to solve practical sample size problems.

Who Should Take This Course: This course is intended for process improvement specialists who work in product or process engineering, manufacturing, research, service industry, and business administration operations. The course does not cover applications to medical clinical trials or behavioral sciences, although many of the basic methods are the same.

Prerequisite: Students should have strong math and statistics skills including an excellent understanding of the calculation, use, and interpretation of confidence intervals and hypothesis tests. They should also have extensive experience in the use of these methods in quality and process improvement activities.

Textbook: Paul Mathews, Sample Size Calculations for Process Improvement.

Course Format: The preferred course format is to present the material in four four-hour sessions with one session per week. This gives the students a chance to read and study the methods of the week, to complete homework assignments, to consider personal applications of the methods, and to practice solving problems with the software of their choice.