Course Title: Statistical Methods for Process Improvement:

Part 2: Using the Normal Distribution

Davis M. Woodruff, PE, CMC

Course Outline

This 1 hour course presents a review and overview of practical and simple statistical methods that can be used for process improvement. Practical how-to information for using the normal distribution in a variety of settings is presented. It is not a theoretical statistics course and is intended as a review/overview type course which is practical and oriented to industry. To get the most benefit from this course, the participant should have completed Part 1: Using Data for Process Improvement or have been introduced to statistical methods in other courses or training.

This course will also provide help organizations that are either ISO 9001, 14001, 13485, TS 16949 or AS 9100 registered or seeking registration to meet the requirements for process and product monitoring and measurement as well as data analysis and continual improvement found in the standards.

This course discusses the following topics:

• Introduction to the Normal Distribution
• Characteristics of the Normal Distribution
• Significance of the Mean and Standard Deviation
• Areas Under the Curve
• Z-Scores: Using the Standard Normal Curve
• Examples of Using Z-scores

Practical  examples and illustrations are used and these process analysis tools may be adapted to any business or business model.

This course includes a multiple choice quiz at the end, which is designed to enhance the understanding of the course materials.

Learning Objective

At the conclusion of this course, the participant will:

• Understand the characteristics of the normal distribution;
• Know the significance of the mean and standard deviation;
• Understand how the Z-scores are calculated and interpreted; and
• Be able to use Z-scores for process analysis.

Intended Audience

Any professional who is involved in process or product monitoring or measurement  or who needs to understand how to apply statistical methods to sustain effective continual improvements.  Engineers, consultants and managers interested in understanding process or product monitoring and measurement as a part of the CI process and to more effectively manage your business using facts will benefit from this course.

Benefit to Attendees

Course participants will learn how to use simple, powerful and practical statistical methods for process improvement that can guide fact based decision making.

Course Introduction

The primary distribution utilized in Statistical Process Control and in using statistical methods for process analysis is the normal distribution.  This distribution occupies a prominent place in statistics for the following reasons:

• The distribution is applicable to many situations in which it is necessary to make inferences from samples taken from a population.
• A normal distribution comes close to fitting the actual observed frequency distribution of many phenomena (weights, heights, times, etc.) and it adequately describes outputs from many processes (dimensions, yields, etc.).

The distribution, a probability density function, resembles a bell-shaped curve as shown below.

Normal Distribution Curve

This One hour course presents a practical and common sense approach to using statistics for process analysis as the second of a five part series.

Course Content

In this lesson, you are required to download and study the following course content in PDF format:

Course Summary

This five part series of courses will provide the information necessary to apply fundamental statistical concepts and methods for process improvement. Statistics can be theoretical and boring. In fact, many engineers dreaded taking “stats” in college, but now find that practical statistics are essential in today’s work place. This course, part 2, as well as the other 4 parts will provide an understanding of how to really use statistics for process improvement. This is not a course in probability theory or theoretical statistics, but rather in how to apply these methods to improve your processes.