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. Virtually 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 MS Windows®. 

Eight hours class time and a maximum of 24 participants. It includes a certificate of completion and all materials are provided, including a CD 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