One common statistical technique, especially useful in manufacturing is the hypothesis test. While it is common, it is not always well understood. In addition, some instances can involve complicated calculations.
Hypothesis testing is a statistical procedure that helps determine if a set of data, typically from a sample, is compatible with a given hypothesis. This presentation focuses on hypothesis tests involving the means of a distribution, one of the most common applications.
The presentation explains the underlying concept and provides a set of steps to help implement the procedure. A variety of examples illustrates how to use the procedure. In addition, the examples show how to perform the calculations in Excel, laying out the problems and illustrating the use of built in functions. In the more complicated cases, the examples use the Data Analysis ToolPak provided by Excel.
Learning Objectives:
- Understand the basic concepts of hypothesis tests for means
- Know when to perform a one-tailed or two-tailed test
- Understand the difference between large sample and small sample tests
- Learn applications of the z-test
- Learn applications of the t-test and when it should be used
- Use the Data Analysis ToolPak in Excel for hypothesis tests
Why Should You Attend:
Hypothesis testing for means is a powerful statistical tool that can help in process improvement, design verification and validation and in corrective and preventive action. The method uses statistical analysis, but Excel handles the calculations allowing you to focus on the information and understanding your need.
Instructor
Daniel O'Leary is the President of Ombu Enterprises, LLC, a company offering training and execution in Operational Excellence, focused on analytic skills and a systems approach to operations management.
Dan has more than 30 years experience in quality, operations, and program management in regulated industries including aviation, defense, medical devices, and clinical labs.
He has a Masters Degree in Mathematics; is an ASQ certified Biomedical Auditor, Quality Auditor, Quality Engineer, Reliability Engineer, and Six Sigma Black Belt; and is certified by APICS in Resource Management.