This Slideshare presentation is a partial preview of the full business document. To view and download the full document, please go here: http://flevy.com/browse/business-document/035introduction-to-graphical-analysis-1131 DESCRIPTION In real estate the rule used to guide success is location, location, location! In other words, buy in the right location and the rest will take care of itself. In data analysis the rule used to guide success is plot, plot, plot! In other words, before any other analysis is undertaken, plot the data. There is truth in the saying ?a picture paints a thousand words?. This is a very powerful concept and surprisingly . . . often overlooked. The objectives of this module are to: understand the basic graphical tools used to analyze project data, review the use of Minitab and a variety of 6ixsigma<dot>org tools to generate graphical analysis. The material is suitable for independent study or formal classroom training and includes several exercises, a list of tools and quiz questions.

1. © 2006 Textron Inc. 1
Introduction to
Graphical Analysis

2. 4
What Graphical Tool Do I Use?
•This is a difficult question to answer. There are no
roadmaps, rules of thumb or meaningful guidelines.
It may be more art than science.
•For a given data set, try plotting the data with as
many different tools as you can find. Determine the
ones that provide the most insight. Experience is
the best teacher.
•To start, a few commonly used tools are reviewed:
Run Chart Box Plot
Histogram Time Series Plot
Pareto Multi-Vari Plot
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3. 7
Anatomy of the Run Chart
A process in control produces random data. The following table illustrates
what the two tests for randomness can tell you:
Test for Randomness Condition Indicates
Number of runs about the
median
More runs observed
than expected
Mixed data from two
population
Fewer runs observed
than expected
Clustering of data
Number of runs up or down
More runs observed
than expected
Oscillation - data varies up and
down rapidly
Fewer runs observed
than expected
Trending of data
With both tests, the null hypothesis is that the data have a random sequence
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4. 10
Histogram using “Basic Statistics.xlsx”
Data Input (Max 1,000 data points)
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5. 13
Pareto Chart in Minitab®
1. Select Stat > Quality Tools > Pareto Chart
2. Enter defect data and labels as appropriate
3. Select OK
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6. 16
Box Plot
Sample 3Sample 2Sample 1
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Data
Boxplot of Sample 1, Sample 2, Sample 3 • Use the Box Plot to
illustrate variation
between data groups
• The Box Plot shows these differences between groups:
Location Variation
Median Inter-Quartile Range
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7. 19
Time Series Plot
Q3Q2Q1Q4Q3Q2Q1Q4Q3Q2Q1Q4Q3Q2Q1Q4Q3Q2Q1
250
200
150
100
50
Quarter
Sales
Time Series Plot of Sales
• Use the Time Series
Plot to show how
one variable is
affected by time.
• Use the Time Series Plot to:
Evaluate patterns in the data over time
Detect seasonal effects on data
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8. 22
Scatter Plot
1412108642
60
50
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30
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10
Variable
Response
Response vs Variable
• Use the Scatter Plot
to show the
relationship between
two continuous
variables
• In the Scatter Plot, the independent variable is plotted on the
x axis and the dependent variable is plotted on the y axis.
• The result will indicate the presence or absence of a relation
ship between the two.
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9. 1412108642
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50
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30
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10
Variable
Response
Response vs Variable
25
Anatomy of the Scatter Plot
Dependent
Variable
Independent
Variable
Assess whether a
relationship is present
between the
dependent and
independent variables
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10. Anatomy of the Multi-Vari Chart
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50
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1
Unit
Response
2
1
2
Measurement
Multi-Vari Chart for Response by Measurement - Treatment
Panel variable: Treatment
Black line:
Measurement to
Measurement
Variation
Red line:
Part to Part
Variation
Green line:
Treatment to
Treatment
Variation
28
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11. Questions
1. The Run Chart is used in conjunction with the statistical Run’s Test.
2. There are many Histogram bin size methodologies.
3. Different Histogram assumptions will result in different plots.
4. Very small sample sizes will be normal according to Minitab.
5. The Pareto Chart is named after Vilfredo Pareto.
6. Distribution outliers are identified using Z values.
7. Scatter plots can be used to isolate root cause.
8. Multi Vari Charts are the visual equivalent of ANOVA.
9. Some versions of the Box Plot include a confidence interval.
10. Multi Vari Charts show factor interaction.
T F
T F
T F
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T F
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T F
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T F
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12. 1
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