Quality Seminars - Just the way You Want Them
Presented
By Elizabeth Clarkson
The web is a terrific place to find all sorts of free help and
information on Quality and Statistics. Sometimes, though, especially
at the beginning of projects, you someone on-site to offer a
more intensive, more interactive training, tailored specifically
to the needs of your organization.
My
seminars are offeced in modules, each focusing on a specific
aspect of Statistics and Quality.
Each
module is available in two hour or four-hour formats. The two-hour
format allows you more subjects in a given time. The four-hour
format provides more depth, as well as additional hands-on experience
for participants.
Select the modules that will benefit you the most, in the format
you prefer. There are over 1,000 possible one-day seminars.
And
you're not limited to one-day seminars. Different modules can
be presented on different days, allowing more time for material
to sink in and questions to surface.
It's
your choice.
Seminar
Modules
Module
1 Probability Theory
Probability theory is the basis for all statistical calculation.
This module covers all the basics.
• Computing probability • Conditional probability •
Permutations and combinations • Spotting statistical fallacies
•
Module
2 Probability Distributions
If you know what distribution your data fit, you can make more
accurate predictions about the future output of your process.
These common distributions underlie statistical process control.
• Normal distribution • Binomial distribution •
Poisson distribution •
Module
3 Process Control
To improve a process, you must first control it. This module
includes an introduction to statistical process control: philosophy,
basic statistics and control charts.
• Process control planning • Process monitoring •
Process improvement • Process capability • Control
charts •
Module
4 Control Charts
There are a variety of control charts. Which should you use,
and when? This module covers the different types of control charts
and when each is appropriate.
• X-bar and R charts • p and np charts • c and
u charts • Probability plotting •
Module 5 Confidence Limits
Any time you compute statistics based on a sample, you are making
a guess about how well that sample represents your process as
a whole. Without determining confidence limits, you have no idea
how reliable that the guess might be.
• Confidence limits for means and variance •
Module
6 Significance Testing
Has a change actually occurred or are you just seeing random
fluctuations in the data? Is there a difference in the outputs
of machine #1 and machine #2. Does my data fit a particular distribution?
Significance testing tells you how closely two distributions
match.
• Chi-Squared test • t-test • F-test •
Module
7 Design of Experiments
Explore the limits of your processes. Variables often interact
within a process in unforeseen ways. Design of Experiments is
a statistically rigorous approach to extract the most information
possible about your processes from the fewest experimental runs.
• Classical Orthogonal Designs • Latin Square Design
• D-optimal Designs •
Module
8 Advanced Methods
How do you determine which of four vendors is the best, when
they each excel in different areas? How do you handle a large
number of variables when you're unsure which are important and
which aren't? Analyze vendors - categorical data - to determine
which will best suit your needs. Determine which factors have
the greatest influence on the quality of your process.
• Analysis of Variance • Regression/Correlation •
Module
9 Acceptance Sampling
Sampling incoming material is very different from sampling the
output of your process. This module shows how to develop a system
for random selection of parts from incoming lots, and how to
set the Acceptable Quality Level for incoming material.
• Mil-Std-105 • Acceptable Quality Level • Operating
Characteristic Curves • Choosing a Random Sample •
Module
10 Graphical Analysis
Have you ever had data you didn't know what to make of? This
module covers using graphics to spot trends and interpret patterns
in your data. Includes traditional and innovative ways to view
data graphically.
• Principles of graph construction • Graphical methods
• Graphical perception • Graphing multiple variables
•
Module
11 Data Presentation
The best part of graphical analysis is that it is ideally suited
to be a communication tool as well, allowing you to guide others
to the same understanding of the data. This module covers using
basic statistics and graphics to present data so that others
can understand it.
• Choosing the right chart for the job • Histograms
• Pareto and control charts • Pies, lines, bars and
more •
Module
12 Survey Development and Analysis
Surveys are a great way to gather the opinions of your customers,
your employees, or any other group of people you need to know
about.
• Designing a survey • Determining target population
• Drawing random samples • Writing good survey questions
•
Module
13 Non-Mathematical Methods
Statistics alone are not enough. Non- mathematical, qualitative
methods help understand what data need to be collected in the
first place, how to organize it, and how it can be used to best
advantage. Discuss and study problems. Organize thinking. Discover
issues.
• Brainstorming • Cause and effect diagrams •
Fault tree analysis • Focus groups • |
