Learn fundamental concepts and methods needed to organize and analyze data to make objective decisions. The course instructs in the development of mathematical models to predict outcomes and understand key factors affecting processes. Knowledge of basic algebra is helpful. Note: computers will be used in class to apply learned techniques. Please bring a laptop if possible.

Seminar Content
Characterizing a Single Process

• Estimation of a Mean, Variance and Proportion
• Detecting Outliers
• Confidence Intervals for the Estimates
• Type I and II Errors
• Hypothesis Testing for the Mean, Variance and Proportion
• Paired Data and the Paired t Test

Comparison of Two Processes

• Testing for Equal Means
• Testing for Equal Variances
• Testing for Equal Proportions

Comparison of Multiple Processes

• ANOVA for Comparing Means
• Confidence Intervals for Multiple Comparisons

Describing the Relationship between Two Variables

• Correlation
• Simple Linear Regression
• R Squared
• Residual Analysis
• Confidence and Prediction Intervals

Predicting a Response Using Several Variables

• Multiple Regression
• Testing for Significant Explanatory Variables
• Model Selection

Who Should Attend
Individuals involved with analysis of process data underlying quality and productivity improvement.

Prerequisites
A knowledge of basic algebra is highly recommended. Computer analysis using Microsoft ExcelTM and StatgraphicsTM will be emphasized.

Related Seminars
Statistical Process Control II
Design of Experiments II

Each participant will receive a comprehensive manual and a Certificate of Completion at the close of the seminar.