Six Sigma Green Belt Certification
Six Sigma Green Belt - Six Sigma Measure
During the Measure stage of the Six Sigma methodology, you need to identify and map processes and procedures for problem areas identified during the Define stage, and present them to the Six Sigma team for a closer
look. This 11 hour program is aligned to the ASQ Certified Six Sigma Green Belt certification exam and is designed to assist learners as part of their exam preparation.
Six Sigma is a registered Trademark of Motorola Corporation, and all right, title, and interest in Six Sigma belongs to Motorola
Target Audience
Candidates seeking Six Sigma Green Belt certification; quality professionals, engineers, production managers, and frontline supervisors; process owners and champions charged with the
responsibility of improving quality and processes at the organizational or departmental level
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Six Sigma Green Belt - Measure Training Curriculum Online
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sk6sigmeasgb
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$199.00
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Modeling and Analyzing Processes in Six Sigma
"If you can't describe what you are doing as a process, you don't know what you are doing," says W.
Edwards Deming, a well-known American quality advocate, statistician, and educator. During the Measure stage of the Six Sigma methodology, you need to identify and map processes and procedures for problem
areas identified during the Define stage, and present them to the Six Sigma team for a closer look. As you start uncovering and analyzing these processes, the likely causes of problems become clearer. This
course will examine the tools and techniques used to model and analyze existing processes. From a process modeling perspective, the course looks at techniques such as process mapping, written
procedures, and work instructions. From a process analysis point of view, the course examines the use of SIPOC analysis to identify process input and output variables, and explores how cause-and-effect
diagrams and relational matrices are used to establish relationships between problems and potential causes.
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Statistics and Probability in Six Sigma
Six Sigma bases its analysis and findings on the facts and figures at hand. Statistical studies and probability are the key tools that Six Sigma teams use to measure and analyze issues that are identified in
the early stages of Six Sigma projects. This course explores basic statistical concepts that apply to Six Sigma. It distinguishes between enumerative and analytical statistics and population and sample
characteristics, and describes the Central Limit Theorem. It also examines basic probability concepts and looks at dependent, independent, and mutually exclusive events, and multiplication and addition rules.
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Data Classification and Collection in Six Sigma
"Measure what is measurable, and make measurable what is not so" said Galileo Galilei, the famous Italian physicist, mathematician, astronomer, and philosopher. Measuring the key characteristics in your
current processes is a very significant step in any Six Sigma improvement journey. As such, sample data from existing processes needs to be identified, collected, presented, and analyzed. Collecting data that is
correct and useful is one of the first steps in the measurement process. Various types of data exist, and they all need appropriate treatment during the collection, presentation, and analysis stages. You also need
to be careful when applying sampling techniques to ensure data accuracy and integrity. This course will explore continuous and discrete types of data, and nominal, ordinal, interval, and ratio measurement
scales. It will also introduce methods for data collection, such as check sheets and coded data, and deals with the issue of data accuracy and integrity, focusing particularly on sampling techniques such as random
sampling and stratified sampling.
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Summarizing and Presenting Data in Six Sigma
Six Sigma teams use measures of central tendency and dispersion to reveal key facts about process data and the existing processes. They summarize data and put forth the relationships between various data
components for further analysis. The teams then present these relationships in easy-to-understand graphical forms that facilitate comparison and help to identify possible trends. This course deals with the
basic concepts of descriptive statistics, such as measures of central tendency and dispersion, and their significance in Six Sigma data analysis. The course also shows how to apply graphical methods, such as
stem-and-leaf plots, box-and-whisker plots, run charts, and scatter diagrams, for illustrating relationships among various components of a given dataset. In addition, it examines how to depict distributions using
histogram and normal probability plots.
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Probability Distributions and Measurement Systems Analysis
Probability distributions are an essential part of descriptive statistics that Six Sigma teams can use to
assist in fitting collected data into various types of distributions. Probability distributions help to ascertain
specific probability values in the distribution and lead the Six Sigma teams down the hypothesis testing roadmap to the next stage of the Six Sigma DMAIC process. Of course, all this is meaningless if the data
you have gathered and used is not accurate or precise, which is where measurement systems analysis (MSA) comes into play. MSA is a task in the Measure stage of the Six Sigma DMAIC process and is used
to identify the variability caused by the measurement system itself. This course will examine how to calculate normal, binomial, Poisson, chi-square, Student's t-distributions, and F distributions. It will also
look at how to assess the precision and accuracy of an organization's current measurement system using Gauge Repeatability and Reproducibility (GR&R), bias, linearity, percent agreement, and
Precision/tolerance (P/T) studies.
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Measuring Process Capability and Performance in Six Sigma
Businesses exist to meet the needs of the customers they serve. As such, you must listen to the voice of your customers and build processes that deliver products and services to them. It is also necessary to
review processes to ensure they remain within the targets and specifications set by your customers. Measuring the capability and performance of a process is an important activity in Six Sigma DMAIC
methodology. Six Sigma teams use process capability and performance measurements, such as process capability (Cp), process capability index (Cpk), process performance (Pp), and process performance index
(Ppk), to indicate the current state of a process and its sigma levels. This course will examine the key concepts of process capability and performance, and the methods of measuring and interpreting them in a
process capability study. It covers the calculation and interpretation of process capability and performance measurements. It also identifies key considerations for measuring process capability, such as short-term
and long-term capability and process capability for discrete data.
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