six sigma green belt training part 19
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Six Sigma – Green Belt Training
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Welcome
Part 19
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Histogram Output
Histogram is the graphical representation of the distribution of population plotted. It helpsunderstanding following for any given population:
• Shape: Look for the distribution of bars. Are there multiple modes in thedistribution?. This might give us idea if the given data is mix of two or more differentpopulation sets.
• Center: Where is the distribution centered?. Is it off the process target?
• Spread: How is the spread of bars in histogram?. Is the spread large compare torequirement?(look against the specification limits). Note that spread has got nothingto do with Normality of data, as you might have normal data with very large spreadwhen compared against specification limits from customer.The options at the bottom of histogram input window gives option to put specification limits.
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Dot Plot
Tool Utility: Another tool to study variation in process. Also used for quick comparison oftwo or more groups for variation. As a part of descriptive statistical analysis used in initialstages.How: Use Minitab GRAPH > DOTPLOTClick on ‘Simple’, Click OkDouble Click on C1, Click ok
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Dot Plot -Minitab
MINITAB FILE: DescriptiveStats.mtw
Dot plot can be plotted from GRAPHS> DOTPLOTIn Minitab Release 14 the Dotplot from Graphs menu gives multiple option for Single Y, Multiple Y,Single group, Multiple group and there combination.
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Dot Plot Output
When Dotplot plotted for multiple operators (groups) gives us comparative visual representation oftheir:•Variation•Center• and spread
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Box And Whisker Plot
Tool Utility: Useful tool to compare two or more groups on variation and median. Thetool plots:- Quartiles- Median- Outliers, if anyThe box tells about the spread ofdata and is a visual used forcomparing two or more data groups.How: Minitab > Graph > Boxplot
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Box plot - How to Read ?
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Box plot -Minitab
MINITAB FILE: BoxPlots.mtwMinitab > Graph > Boxplot
• Choose ‘One Y’ ‘With Groups’, Click Ok.• Use C6 for Data,• C7 for Subscripts• Click ok
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Statistical Hypothesis Testing
Statistical Hypothesis
Testing
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The Nature Of Statistical Problem
While working on Six Sigma Projects we come across two types of problems with data.•Variation (Spread)•Centering
Though working on one problem has some impact on other also, more often than not we wouldtarget only one problem at a time.In Analyze phase we run statistical tests to determine which X’s have an effect on the Y. These testsmay be testing for variance or for mean depending on what is our Y metric.
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A Statistical Hypothesis
Definition of Hypothesis: Statement about the parameters of the PopulationIn hypothesis testing there are two hypotheses of interest.– The null hypothesis (H0)– The alternative hypothesis (HA)
Concept Of Hypothesis Testing
1. All processes have variation.2. Different Samples from the same process may vary3. Hypothesis testing differentiates between sampling error(Precision) and true process differences.
Are these Samples from the same distribution or are they truly from different processes.
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Why Do Hypothesis Testing?
Identification and validation of factors which impact process performance. Validate improvement in process Statistically validate our statement about the characteristics of population Helps in decision making process. Enabler for data driven approach.
Hypothesis Testing
In Hypothesis Testing one has to make a decision:– to either to accept null hypothesis or– to reject null hypothesisThe hypotheses are always statements about the population parameters. To define a statistical Test we– Choose a statistic (called the test statistic)– State your null and alternate hypothesis– Divide the range of possible values for the test statistic into two parts• The Acceptance Region• The Critical Region
•The Acceptance Region (The range of values of the test statistic that indicate the Null Hypothesis is true.)• The Critical Region (The range of values of the test statistic that indicate the Null Hypothesis is false.)
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Six Sigma – Green Belt Training