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Histograms

In this module Dr. Bill Bailey will introduce you to the different types of quality tools as well as important Six Sigma metrics that can be used throughout the DMAIC process. Quality tools and metrics are a critical tool for process improvement. This module will introduce you to some of the most common quality tools as well as the most important Six Sigma metrics.

Six Sigma Principles

乔治亚大学系统

课程 1（共 4 门，Six Sigma Yellow Belt 专项课程）

This course is for you if you are looking to learn more about Six Sigma or refresh your knowledge of the basic components of Six Sigma and Lean. Six Sigma skills are widely sought by employers both nationally and internationally. These skills have been proven to help improve business processes and performance. This course will introduce you to the purpose of Six Sigma and its value to an organization. You will learn about the basic principles of Six Sigma and Lean. Your instructors will introduce you to, and have you apply, some of the tools and metrics that are critical components of Six Sigma. This course will provide you with the basic knowledge of the principles, roles, and responsibilities of Six Sigma and Lean. Every module will include readings, videos, and a quiz to help make sure you understand the material and concepts that are studied. You will also have the opportunity to participate in discussions and peer review exercises to give you the opportunity to apply the material to your daily life. Our applied curriculum is built around the latest handbook The Certified Six Sigma Handbook (2nd edition) and students will develop /learn the fundamentals of Six Sigma. Registration includes online access to course content, projects, and resources but does not include the companion text The Certified Six Sigma Handbook (2nd edition). The companion text is not required to complete the assignments. However, the text is a recognized handbook used by professionals in the field. Also, it is a highly recommended text for those wishing to move forward in Six Sigma and eventually gain certification from professional agencies such as American Society for Quality (ASQ).

查看授课大纲

您将学习的技能

Lean Six Sigma, Process Improvement, Six Sigma, Lean Methods

教学方

Bill Bailey, PhD
Assistant Professor of Industrial Engineering
Gregory Wiles, PhD
Interim Chair and Assistant Professor
David Cook, PhD
Assistant Professor of Mechanical Engineering Technology
Christina Scherrer, PhD
Professor of Industrial Engineering

脚本

A useful tool to help us to understand our data is a histogram.

Remember with check sheets we counted how many times different things occurred.

That number is also called a frequency.

The histogram is a graphic representation or

a picture of what that frequency distribution looks like.

Typically, it's a special type of bar graph.

The vertical axis represents the frequency or how many times something happened.

And the horizontal axis represents the various events or the things that happen.

We can use our frequency table to construct a histogram.

Again, this might be simple counts of things or

events, this is also called the discrete data.

Or if we have measurements, which may be continuous data, we will need to group

those measurements and count how many times our measures fell in those groups.

The intervals are shown on the X-Axis across the bottom.

The number of scores or frequency is shown by the height of

the rectangle located above the interval on the Y-Axis.

In constructing a frequency table, and a histogram for

measurement data, you need to determine how to group your measures.

It's necessary to establish how many groups you need and how wide they will be.

The number of groups is approximately equal to the square root of N,

where N is the number of data points.

For the width, just take the smallest value in your data set and

subtract it from the largest.

This is the range or the spread of your data.

Divide that number by the number of groups that you determined in the first step.

If the result is a fraction, just round up.

All groups should be the same size if possible and there should be no overlap.

Each value should fall into one and only one of the groups.

For this example, let's look at some student grades on an exam.

First, we might use a check sheet like we did before.

The grades can be any number from 0 to 100,

and any fraction in-between, so this is continuous data.

We'll need to construct groups on our check sheet to reflect that.

Then we'll create a tally by placing an x in the row

next to each group for every student whose score falls into that group.

When all of the grades have been tallied, we can add a totals column and

count how many times each grade occurred.

This is what the histogram of our grade data would look like.

Notice we can also turn our check sheets sideways and

get the same visual representation.

What does this tell us?

The first thing that we look for is whether or

not the data is normally distributed.

You've probably seen representations of the normal curve or the bell curve.

Here we've placed a rough sketch of the normal curve over our histogram.

We're not looking for an exact fit.

Often, we may not have a lot of data.

Small samples will not look exactly normal.

For small amounts of data, like fewer than 50 data points,

is the distribution higher near the middle and smaller as you move right to left?

There are statistical tests for normality, but

those will be covered in a future course.

Is the data normally distributed?

Where's the approximate center of the data?

What's the spread of the data?

That is, what are the differences between the lowest and highest values?

Are there any outliers or unusual values?

The answers to these questions

can help you to determine what type of analysis you can do with the data.

And you can get an idea of those values just by glancing at the histogram.

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