Can you comprehend all of the questions related to process performance that require comparisons between two or more sets of data? (For example: How much difference is there between the cholesterol levels for patients on the new drug compared to those on the current drug?)  In this course we introduce methods by which two or more sets of data can be compared to determine whether or not there are significant differences between them. 

Hypothesis testing is imperative if you're going to use data to improve process performance.  This course explains hypothesis testing, its tools, and how to employ it in your process assessments and measurements.  

Course Objectives

• Understand hypothesis testing and how it works.
• Learn how to interpret the P-value.
• Distinguish between Type I and Type II errors as they relate to hypothesis testing.
• Learn how to run various tests of hypothesis:
  • T-test and paired t-test.
  • ANOVA.
  • Test of equal variances.
  • Chi-square.
• Understand the assumptions related to hypothesis testing.
• Recognize where hypothesis testing can apply to your own organization. 

Main Topics

• Making comparisons between two groups (What is hypothesis testing?)
• Interpreting the P-value
• Type I and Type II errors in hypothesis testing
• T-test and paired t-test
• Making comparisons between several groups: ANOVA
• Test of equal variances
• Chi-square test
• Using MinitabTM in support of statistical tools

Who Should Attend

Managers and professionals who are involved in process improvement activities, as well as associated quality, engineering, manufacturing, process, and industrial personnel.   Also valuable for anyone involved in a Lean and/or Six Sigma deployment.

This course requires the use of Minitab^TM statistical software; attendees are responsible for bringing laptops with Minitab^TM  to the class.

CEUs: 2.5
Number of Days: 3