Excel

ADVANCED QUALITY ENGINEERING (AQE)

HOW TO USE MICROSOFT® EXCEL™

FOR STATISTICAL DATA ANALYSIS

Statistical analysis made easy

There was a time when performing statistical analysis of data was laborious and required a talent for mathematics. With the prevalence of spreadsheet programs, such as Microsoft® Excel™, data analysis has become simple and routine. Therefore, nearly anyone who can point a mouse can utilize the power of statistical methods for quality improvement.

Having knowledge of easy-to-use statistical tools to understand variation quantitatively and act to reduce it is a powerful advantage. This is particularly true in the design stages of a product.

All too often, decisions are made based more on opinion than fact, and our processes remain more of an art than a science. This seminar will enable participants with basic statistical skills to become adept with the Data Analysis, Charting, and Function Wizard aspects of Excel.

Who should attend this seminar?

Managers, supervisors, and engineers in manufacturing companies. It is particularly well suited for design, manufacturing and quality engineers. All participants should have a basic understanding of statistics and Windows® 95 or 98. Eight hours class time and a maximum of 24 participants. It includes a certificate of completion and all materials are provided, including a diskette with sample Microsoft® Excel™ data files for the techniques.

On completion of this seminar, you will be able to:

	Use Microsoft® Excel™ for statistical data analysis and to make better decisions.
	Analyze data you bring with you from your own work.
	Achieve process and design improvements through the use of statistical methods.
	Use Microsoft® Excel™ more productively

Topics covered:

	Data entry and manipulation on spreadsheets
	Descriptive statistics
	Probability distributions
	Histograms and other charting methods
	Process capability
	Gauge Repeatability and reproducibility studies (GR&R)
	Correlation and regression analysis
	Control limit calculations for control charts
	Hypothesis testing (tests of significance): t-Test, f-Test, z-Test, ANOVA
	Exponential smoothing
	Randomization and sampling