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Actionable Information From Soft Data
Engineers, Six Sigma practitioners, and other researchers often work with "hard" data - discrete data that can be counted and legitimately expressed as ratios. But what of "soft" data, things like opinions, attitudes, satisfaction? Can statistical process controls (SPC) be applied here? Can process variation in customer satisfaction, for example, be measured and then reported to management in a meaningful way? Can we leverage "appeal", "responsiveness", or "value for money spent"? The process of turning soft data into information (assuming the data are valid) is two-fold: knowing what to extract and knowing how to display. From George H. Chynoweth, Ph.D. and iSixSigma.
Analytical Treatment of Discrete Ordered Category Data
Black Belts learning to apply the Six Sigma methodology to ordered category data should know that there are alternate methods for analyzing discrete ordered category data that are specifically useful when structuring voice of the client processes. From Nilakantasrinivasan.
Boxplots in Excel
Examples of how to create boxplots using Microsoft Excel 5/95 and Excel 97. From Neville Hunt, Coventry University.
Building a Sound Data Collection Plan
Six Sigma project leaders should develop a sound data collection plan to gather reliable and statistically valid data in the DMAIC measurement phase. Several crucial steps need to be addressed to ensure that the data collection process and measurement systems are stable and reliable -- Learn more here. From Patrick Waddick and iSixSigma.
Comparing Apples to Apples
Make sure you attribute your Six Sigma results to the correct factor -- don't compare apples to oranges. From Quality Digest, written by Thomas Pyzdek.
Data Analysis
Information and questions on data origination, collection, peer review, comparisons and context. From RobertNiles.com.
Determining Sample Size
Microsoft Excel spreadsheet identifying the sample size for given Alpha and Beta Risks. From Mike Carnell.
Determining Sample Size
Determining sample size is a very important issue because samples that are too large may waste time, resources and money, while samples that are too small may lead to inaccurate results. Learn how to determine the sample size necessary for correctly representing your population. From iSixSigma.
Estimating Sample Size for Process Capability with Special Causes
No valid statistical calculation exists to set a sample size for establishing a baseline for an unstable process. But here is a way to judge if the samples taken are likely to give a reliable result for Process Capability. An Excel template is included. From David Hampton of Rath & Strong.
Explaining Confidence Interval
Not sure how to put forward the concepts of confidence interval and degree of confidence to someone who is not comfortable with math? Try this article for a quick and thorough explanation for confidence interval. From the iSixSigma Discussion Forum.
Fast Start Collecting Data on Financial Service Process
In any financial service process that is being studied for the first time, it's common for Six Sigma teams to spend one-third to one-half of their project time on data collection alone. Here are three tips that may help teams get a fast started. From Kevin Simonin.
Finding Data On The Internet
Look in the right places and you'll find the information you need. If it's out there, it might be here. From RobertNiles.com.
GE's Six Sigma Focus On Span
Span is a metric used to understand process dispersion, as well as help a business become focused on customer requirements. From Kerri Simon and iSixSigma.
Getting Close to the Customer: Quantitative vs. Qualitative Approaches
After adapting information technology to develop ever more sophisticated quantitative research methods, marketers are taking a second look at more human, qualitative approaches to tapping into the hearts and minds of consumers. From Wharton School of the University of Pennsylvania.
How To Turn Process Data Into Information
When we turn our counts and measures into accurate statistical pictures, patterns emerge. Learning to recognize these patterns is an indispensable Six Sigma skill. Valuing the information conveyed by these patterns is one of the most important contributions executive leaders can make to Six Sigma projects. From Daniel Sloan and iSixSigma.
Is There Bias In Your Random Sample?
Learn how to randomly sample your population to ensure no bias. From iSixSigma.
Language Data: The 'Other Data' of Six Sigma
There is a kind of data that gets less press than the numbers so visible in almost all Six Sigma work – language data. The fact that it receives less exposure doesn’t mean its less worthy of exploration. From David L. Hallowell and Six Sigma Advantage.
Language Data: The 'Other Data' of Six Sigma - Part 1 of 2 – Nature of Language Data
There is a kind of data that gets less press than the numbers so visible in almost all Six Sigma work – language data. The fact that it receives less exposure doesn’t mean it is less worthy of exploration. From David Hallowell.
Moms Know . . . All About Clear Operational Definitions
An operational definition can be defined as a clear and understandable description of what is to be observed and measured, such that different people collecting, using and interpreting data will do so consistently. It's something moms know about... From Kathy Parker.
Quick Guide to Boxplots in Excel 97+
Step-by-step example of how to create boxplots using Microsoft Excel 97+. [PDF file] From The University of Sheffield.
Rounding and Round-Off Rules
Simple rules for appropriately rounding your statistical data. From iSixSigma.
Sample Size Calculation for Non Statisticians
'So how come a survey of 1,600 people can tell me what 250 million are thinking?' From RobertNiles.com.
Sampling by Attributes
This application gives the single and double sampling plans for attributes, according to the Military Standard 105E tables, for a given lot size and AQL. From The Technion.
Selecting Appropriate Metrics
Process metrics need to serve a purpose, be understood, and not be too cumbersome or complex to use. Fashioning appropriate metrics requires knowledge, experience and a lot of common sense. These examples are intended to help the inexperienced Six Sigma practitioner recognize the issues involved and the criticality of ensuring that the metrics selected and used at all levels of the organization are the appropriate ones. From Niraj Goyal and iSixSigma.
Six Sigma Special Topics: Z Shift, Statistics & Non-Standard Data Analysis
Understanding z shifts, how it applies to your data (which may not be standard), and how to explain it to non-Six Sigma quality professionals. (PDF file: 2.9MB). From C.L. Stanard and GE CRD.
Small (<30) Sample Size Calculation
Learn how the Student t Distribution can help you represent your population with a small (less than 30) sample size. From iSixSigma.
Stability and Linearity
A couple of key points to an effective measurement system. From PQ Systems.
Table of the Standard Normal (z) Distribution
A table of the Standard Normal (z) Distribution for calculating the area under a normal distribution and the critical value. From iSixSigma.
Tips On How To Explain Normal Distribution
Tips and various examples on understanding and expaining a normal distribution. From iSixSigma.
Turning Customer Data into Critical-to-Satisfaction Data
In Six Sigma, a successful business knows its customers by identifying the voice of the customer (VOC). And, the key to success in that process is gathering customer data and converting it into measurable critical-to-satisfaction elements. From Debra Thomas.
Use Cases and Measures: Strengthening the Six Sigma Link
As the functionality described by many use cases begs measurability, creating an early clear link with measures facilitates better understanding of requirements, measurement and test system planning, Y-to-x flowdown and performance prediction. From David L. Hallowell.
Using Vector Analysis for Turbo-Charged Data Mining
Data mining via vector analysis is a powerful, flexible process observation tool. With due regard for the possibility of correlation/causation fallacies, data mining can be used by almost anyone. From M. Daniel Sloan.
Why You Cannot Depend Totally on Statistical Software
The proliferation of do-it-yourself statistical software is giving some Six Sigma practitioners, who are not strong in statistics, a false sense of confidence. Here are some tips about what needs to be done even before collecting and analyzing data. From Tzippy Shochat.
Yield the Right Way
Yields, rolled throughput yields, and other mysteries explained. From Thomas Pyzdek.
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