Business Strategy and Operations
Six Sigma Black Belt: The Analyze Phase
The Analyze phase of Six Sigma's® DMAIC (Define, Measure, Analyze, Improve, and Control) roadmap includes what is traditionally referred to as "crunching the numbers." After you have accurately
defined the problem and measured the correct data in earlier phases, the Analyze phase looks at that data from all angles in an effort to precisely determine the relationships among variables. Making the data useful
is the job of the Analyze phase..
Six Sigma is a registered Trademark of Motorola Corporation, and all right, title, and interest in Six Sigma belongs to Motorola.
Target Audience:
Candidates for black belt certification; managers/executives overseeing personnel involved in the implementation of Six Sigma in their organization; consultants involved in implementing a
Six Sigma proposal; and organizations implementing a Six Sigma project.
Curriculum Includes:
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Six Sigma Black Belt - Control Phase Training Curriculum Online
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sk6siganlyz
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$169.00
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Exploratory Data Analysis
By organizing, quantifying, visualizing, and testing the relationships between variables in a process, the Analyze phase narrows the focus to the few key causes of error or inefficiency. In the Improve phase that
follows, Six Sigma teams then have the formulas, tested hypotheses, and target areas for the changes that are needed--a statistical understanding of the problem and likely solutions. This course, Exploratory Data
Analysis, discusses the initial methods for understanding the collected data, using various tools and techniques that present the data in ways that reveal both simple and complex relationships among the
variables. Using statistical analysis techniques--some unique to Six Sigma, but most standard to all statistical evaluation--relationships are displayed and correlations are established. Using regression
analysis, formulas can be developed to model relationships, allowing prediction and estimation. This "number crunching" can later be used to formulate and mathematically test hypotheses before actually
implementing solutions.
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Hypothesis Testing
What if you proposed a solution to a problem at your company based only on a strong hunch and casual review of data? What if your company bought into your solution and invested millions of dollars executing it?
If the solution didn't work as you expected or the end results were insignificant, chances are you'd be
looking for a new job. When millions of dollars and, perhaps, your company's future success are on the line, your hypotheses must be based on more than hunches or strong feelings. Six Sigma® uses complex
statistical tools to analyze data from multiple angles to be sure proposed solutions or hypotheses are truly the right ones for your company. Hypothesis testing refers to the process of using statistical analysis to
determine whether the observed differences between two or more samples are due to random chance or to true differences in the samples. This course, Hypothesis Testing, explores the statistical techniques used
to test hypotheses in Six Sigma projects. It covers how data can be viewed in a variety of ways and how sample size impacts data. Additionally, this course explores confidence levels and the statistical
techniques used to confirm that a hypothesis is indeed valid and the end results statistically significant.
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Common Tests
This course, Common Tests, discusses the common hypothesis testing methods, how to perform them, and their uses in Six Sigma analysis. Common hypothesis tests fall into three basic categories--those that
compare two or more groups based on averages, on variability, and on proportion. Hypothesis testing using means is explored, including tests of single means, two independent means, and multiple means. Analysis
of variance and tests of proportions--including binomial proportions--are also discussed. Paired-comparison
tests, which are commonly used in "before and after" testing, are covered--as is goodness-of-fit testing, which compares how well your hypothesis fits your observed data.
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Variance, Contingency Tables, and Nonparametric Tests
This course, Variance, Contingency Tables, and Nonparametric Tests, discusses the common tests of variance, how to perform them, and their uses in Six Sigma analysis. You'll learn about the benefits and
general characteristics of the Analysis of Variance (ANOVA) technique. Step-by-step instruction on performing both single-factor (one-way) ANOVA and two-factor (two-way) ANOVA is provided. You'll also
learn how to construct and use contingency tables. Finally, you'll explore the application of common nonparametric tests such as the Mann Whitney U, Kruskal-Wallis, Mood's Median, and Levene's tests.
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