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7/28/2019 SigmaXLV5.2 Demonstration
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Introducing SigmaXL
Version 5.2
Powerful.
User-Friendly.
Cost-Effective. Priced at $199, SigmaXL is a fractionof the cost of any major statistical product, yet it hasall the functionality most professionals need.
Quantity, Educational, and Training discounts are
available. Visit www.SigmaXL.com or call
1-888-SigmaXL (1-888-744-6295) for moreinformation.
http://www.sigmaxl.com/http://www.sigmaxl.com/7/28/2019 SigmaXLV5.2 Demonstration
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SigmaXLVersion 5.2
Whats New?
Compatible with Excel 2007 and Windows Vista
Lean and Six Sigma DMAIC Templates: Team/Project Charter
SIPOC Diagram
Data Measurement Plan
Quality Function Deployment (QFD) Pugh Concept Selection Matrix
Control Plan
Lean Templates: Takt Time, Value Analysis and Process
Load Balance Chart
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SigmaXLVersion 5.2
Whats New?
Menu Layout OptionClassical or DMAIC:
Use SigmaXLs Classical
Menu (default). Tools aregrouped by category.
Use the DMAIC Menu.Tools are grouped by theSix Sigma DMAICformat.
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SigmaXLVersion 5.2
Whats New?
Control ChartSelection Tool:
Simplifies theselection ofappropriate controlchart based ondata type
Includes DataTypes andDefinitions helptab.
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Why SigmaXL?
Measure, Analyze, and Control yourManufacturing, Service, or Transactional
Process. An add-in to the already familiar Microsoft
Excel, making it a great tool for Six Sigmatraining. Used by Motorola University and
other leading providers. SigmaXL is rapidly becoming the tool of
choice for Quality and BusinessProfessionals.
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Recall Last Dialog
Recall SigmaXL Dialog
This will activate the last data worksheet and recall
the last dialog, making it very easy to do repetitiveanalysis.
Activate Last Worksheet
This will activate the last data worksheet usedwithout recalling the dialog.
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EZ-Pivot: The power of Excels
Pivot Table and Charts are now
easy to use!
0
10
20
30
40
50
60
70
Dif fi cu lt -to-order Not -ava ilab le Order -takes -too -long Return-ca ll s Wrong-color
3
2
1
Size of Customer (All)
Count of Major-Complaint
Major-Complaint
Customer Type
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Data Manipulation
Subset by Category, Number, or Date
Random Subset
Stack and Unstack Columns
Stack Subgroups Across Rows
Standardize Data Normal Random Number Generator
Box-Cox Transformation
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Templates & Calculators
Sample Size Discrete
Sample Size Continuous
Gage R&R Study (MSA)
Gage R&R: Multi-Vari & X-bar R Charts Attribute Gage R&R (Attribute Agreement Analysis)
Process Sigma Discrete
Process Sigma Continuous
Process Capability
Process Capability & Confidence Intervals Standard Deviation Confidence Interval
1 Proportion Confidence Interval
2 Proportions Test
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Templates & Calculators:
Quality Function
Deployment (QFD)
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Templates & Calculators:
Pugh Concept Selection
Matrix
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Templates & Calculators:
Value Analysis/
Process Load Balance Chart
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Templates & Calculators:
Failure Mode & Effects
Analysis (FMEA)
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Templates & Calculators:
Cause & Effect (XY)
Matrix
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Templates & Calculators:
Sample Size Calculators
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Templates & Calculators:
Process Sigma Level
Discrete & Continuous
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Graphical Tools
Basic and Advanced (Multiple) Pareto Charts
Run Charts (with Nonparametric Runs Test allowingyou to test for Clustering, Mixtures, Lack ofRandomness, Trends and Oscillation.)
Basic Histogram
Multiple Histograms and Descriptive Statistics
(includes Confidence Interval for Mean and StDev.,as well as Anderson-Darling Normality Test)
Multiple Histograms and Process Capability(Pp, Ppk, Cpm, ppm, %)
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Graphical Tools
Multiple Boxplots and Dotplots
Multiple Normal Probability Plots (with 95%
confidence intervals to ease interpretation ofnormality/non-normality)
Multi-Vari Charts
Scatter Plots (with linear regression andoptional 95% confidence intervals andprediction intervals)
Scatter Plot Matrix
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Graphical Tools:
Multiple Pareto Charts
0
2
4
6
8
10
12
14
Return-
calls
Difficult-
to-order
Wrong-
color
Not-
available
Order-
takes-
too-long
Customer Type - Customer Type: # 1 - Size of Customer:
Large
Count
0%
10%
20%30%
40%50%60%
70%80%
90%100%
0
2
4
6
8
10
12
14
Return-
calls
Difficult-
to-order
Wrong-
color
Not-
available
Order-
takes-
too-long
Customer Type - Customer Type: # 2 - Size of Customer:
Large
Count
0%
10%
20%30%
40%50%60%
70%80%
90%100%
0
2
4
6
810
12
14
Return-
calls
Difficult-
to-order
Wrong-
color
Not-
available
Order-
takes-
too-long
Customer Type - Customer Type: # 1 - Size of Customer:
Small
Count
0%10%
20%30%
40%50%60%70%
80%
90%100%
0
2
4
6
810
12
14
Return-
calls
Difficult-
to-order
Wrong-
color
Not-
available
Order-
takes-
too-long
Customer Type - Customer Type: # 2 - Size of Customer:
Small
Count
0%10%
20%30%
40%50%60%70%
80%
90%100%
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Graphical Tools:
Multiple Histograms &
Descriptive Statistics
0
2
4
6
8
10
12
1.
72
1.
99
2.
26
2.
54
2.
81
3.
08
3.
35
3.
62
3.
90
4.
17
4.
44
4.
71
4.
98
Overall Satisfaction - Customer Type: 1
0
2
4
6
8
10
12
1.7
2
1.9
9
2.2
6
2.5
4
2.8
1
3.0
8
3.3
5
3.6
2
3.9
0
4.1
7
4.4
4
4.7
1
4.9
8
Overall Satisfaction - Customer Type: 2
Overall Satisfaction - Customer Type: 1
Count = 31
Mean = 3.3935
Stdev = 0.824680
Range = 3.1
Minimum = 1.7200
25th Percentile (Q1) = 2.8100
50th Percentile (Median) = 3.560075th Percentile (Q3) = 4.0200
Maximum = 4.8
95% CI Mean = 3.09 to 3.7
95% CI Sigma = 0.659012 to 1.102328
Anderson-Darling Normality Test:
A-Squared = 0.312776; P-value = 0.5306
Overall Satisfaction - Customer Type: 2
Count = 42Mean = 4.2052
Stdev = 0.621200
Range = 2.6
Minimum = 2.4200
25th Percentile (Q1) = 3.8275
50th Percentile (Median) = 4.3400
75th Percentile (Q3) = 4.7250
Maximum = 4.98
95% CI Mean = 4.01 to 4.4
95% CI Sigma = 0.511126 to 0.792132
Anderson-Darling Normality Test:
A-Squared = 0.826259; P-value = 0.0302
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Graphical Tools:
Multiple Histograms &
Process Capability
Histogram and Process Capability Report
Room Service D elivery Time: After Improvement
LSL = -10 USL = 10Target = 0
0
20
40
60
80
100
120
140
160
Delivery Time Deviation
Histogram and Process Capability Report
Room Service Delivery Time: Before Improvement (Baseline)
LSL = -10 USL = 10Target = 0
0
20
40
60
80
100
120
140
160
Delivery Time Deviation
Count = 725
Mean = 6.0036
Stdev (Overall) = 7.1616
USL = 10; Target = 0; LSL = -10
Capability Indices using Overall Standard Deviation
Pp = 0.47
Ppu = 0.19; Ppl = 0.74
Ppk = 0.19
Cpm = 0.36
Sigma Level = 2.02
Expected Overall Performance
ppm > USL = 288409.3
ppm < LSL = 12720.5
ppm Total = 301129.8% > USL = 28.84%
% < LSL = 1.27%
% Total = 30.11%
Actual (Empirical) Performance
% > USL = 26.90%
% < LSL = 1.38%
% Total = 28.28%
Anderson-Darling Normality Test
A-Squared = 0.708616; P-value = 0.0641
Count = 725
Mean = 0.09732
Stdev (Overall) = 2.3856
USL = 10; Target = 0; LSL = -10
Capability Indices using Overall Standard DeviationPp = 1.40
Ppu = 1.38; Ppl = 1.41
Ppk = 1.38
Cpm = 1.40
Sigma Level = 5.53
Expected Overall Performance
ppm > USL = 16.5
ppm < LSL = 11.5
ppm Total = 28.1
% > USL = 0.00%
% < LSL = 0.00%
% Total = 0.00%
Actual (Empirical) Performance
% > USL = 0.00%
% < LSL = 0.00%
% Total = 0.00%
Anderson-Darling Normality Test
A-Squared = 0.189932; P-value = 0.8991
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Graphical Tools:
Multiple Boxplots
1
2
3
4
5
1 2 3
Customer Type - Size of Customer: Large
Overa
llSa
tis
fac
tion
1
2
3
4
5
1 2 3
Customer Type - Size of Customer: Small
Overa
llSa
tis
fac
tion
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Graphical Tools:
Run Charts with
Nonparametric Runs Test
Median: 49.00
32.40
37.40
42.40
47.40
52.40
57.40
62.40
67.40
1 2 3 4 5 6 7 8 9101112131415161718192021222324252627282930313233343536373839404142434445464748495051525354555657585960616263646566676869707172737475767778798081828384858687888990919293949596979899100
Run
Chart-
Avgd
ays
Order
tode
livery
time
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Graphical Tools:
Multiple Normal Probability
Plots
-3
-2
-1
0
1
2
3
1 2 3 4 5 6
Overall Satisfaction - Customer Type: 1
NSCORE
-3
-2
-1
0
1
2
3
2.1 2.6 3.1 3.6 4.1 4.6 5.1 5.6 6.1
Overall Satisfaction - Customer Type: 2
NSCORE
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Graphical Tools:
Multi-Vari Charts
1.634
2.134
2.634
3.134
3.634
4.134
4.634
# 1 # 2 # 3
Customer Type - S ize of Customer:
Large - Product Type: Consumer
OverallSatisfaction
(Mean
Options)
0.00
0.20
0.40
0.60
0.80
1.00
# 1 # 2 # 3
Customer Type - S ize of Customer:
Large - Product Type: Consumer
Standard
Deviation
1.634
2.134
2.634
3.134
3.634
4.134
4.634
# 1 # 2 # 3
Customer Type - Size of Customer: Small -
Product Type: Consumer
0.00
0.20
0.40
0.60
0.80
1.00
# 1 # 2 # 3
Customer Type - Size of Customer: Small -
Product Type: Consumer
1.634
2.134
2.634
3.134
3.634
4.134
4.634
# 1 # 2 # 3
Customer Type - Size of Customer: Large -
Product Type: Manufacturer
0.00
0.20
0.40
0.60
0.80
1.00
# 1 # 2 # 3
Customer Type - Size of Customer: Large -
Product Type: Manufacturer
1.634
2.134
2.634
3.134
3.634
4.134
4.634
# 1 # 2 # 3
Customer Type - Size of Customer: Small -
Product Type: Manufacturer
0.00
0.20
0.40
0.60
0.80
1.00
# 1 # 2 # 3
Customer Type - Size of Customer: Small -
Product Type: Manufacturer
G hi l T l
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Graphical Tools:
Multiple Scatterplots with
Linear Regression
y = 0.5238x + 1.6066
R2
= 0.6864
1.1
1.6
2.1
2.6
3.1
3.6
4.1
4.6
5.1
1.01 1.51 2.01 2.51 3.01 3.51 4.01 4.51
Responsive to Calls - Customer Type: 1
Overa
llSa
tis
fac
tion
y = 0.5639x + 1.822
R2
= 0.6994
2.1
2.6
3.1
3.6
4.1
4.6
5.1
1.88 2.38 2.88 3.38 3.88 4.38 4.88
Responsive to Calls - Customer Type: 2
OverallSatisfaction
Linear Regression with 95%
Confidence Interval and Prediction Interval
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Graphical Tools:
Scatterplot Matrix
y = 1.2041x - 0.7127
R2
= 0.6827
1.0000
2.0000
3.0000
4.0000
5.0000
1.7200 2.2200 2.7200 3.2200 3.7200 4.2200 4.7200
Overall Satisfaction
ResponsivetoCal
ls
y = 0.8682x + 0.4478
R2
= 0.5556
1.4000
2.4000
3.4000
4.4000
1.7200 2.2200 2.7200 3.2200 3.7200 4.2200 4.7200
Overall Satisfaction
EaseofCo
mmunications
y = 0.1055x + 2.8965
R2
= 0.0059
0.9600
1.9600
2.9600
3.9600
4.9600
1.7200 2.2200 2.7200 3.2200 3.7200 4.2200 4.7200
Overall Satisfaction
StaffKnowledge
y = 0.567x + 1.6103
R2
= 0.6827
1.7200
2.7200
3.7200
4.7200
1.0000 2.0000 3.0000 4.0000 5.0000
Responsive to Calls
OverallSatisfaction
y = 0.303x + 2.5773
R2
= 0.1437
1.4000
2.4000
3.4000
4.4000
1.0000 2.0000 3.0000 4.0000 5.0000
Responsive to Calls
EaseofCo
mmunications
y = 0.0799x + 2.9889
R2
= 0.0071
0.9600
1.9600
2.9600
3.9600
4.9600
1.0000 2.0000 3.0000 4.0000 5.0000
Responsive to Calls
StaffKnowledge
y = 0.64x + 1.4026
R2
= 0.5556
1.7200
2.7200
3.7200
4.7200
1.4000 2.4000 3.4000 4.4000
Ease of Communications
OverallSatisfaction
y = 0.4743x + 2.0867
R2 = 0.1437
1.0000
2.0000
3.0000
4.0000
5.0000
1.4000 2.4000 3.4000 4.4000
Ease of Communications
ResponsivetoCal
ls
y = 0.0599x + 3.0732
R2
= 0.0026
0.9600
1.9600
2.9600
3.9600
4.9600
1.4000 2.4000 3.4000 4.4000
Ease of Communications
StaffKnowledge
y = 0.0555x + 3.6181
R2
= 0.0059
1.7200
2.7200
3.7200
4.7200
0.9600 1.9600 2.9600 3.9600 4.9600
Staff Knowledge
OverallSatisfaction
y = 0.0893x + 3.57
R2
= 0.0071
1.0000
2.0000
3.0000
4.0000
5.0000
0.9600 1.9600 2.9600 3.9600 4.9600
Staff Knowledge
ResponsivetoCal
ls
y = 0.0428x + 3.6071
R2
= 0.0026
1.4000
2.4000
3.4000
4.4000
0.9600 1.9600 2.9600 3.9600 4.9600
Staff Knowledge
EaseofCo
mmunications
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Statistical Tools
P-values turn red when results are significant (p-value < alpha)
Descriptive Statistics including Anderson-DarlingNormality test, Skewness and Kurtosis with p-values
1 Sample t-test and confidence intervals
Paired t-test, 2 Sample t-test 2 Sample Comparison Tests
Normality, Mean, Variance, Median
Yellow Highlight to aid Interpretation
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Statistical Tools
One-Way ANOVA and Means Matrix
Two-Way ANOVA Balanced and Unbalanced
Equal Variance Tests: Bartlett
Levene
Welchs ANOVA Correlation Matrix
Pearsons Correlation Coefficient
Spearmans Rank
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Statistical Tools
Multiple Linear Regression
Binary and Ordinal Logistic Regression
Chi-Square Test (Stacked Column data andTwo-Way Table data)
Nonparametric Tests
Power and Sample Size Calculators Power and Sample Size Charts
St ti ti l T l
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Statistical Tools:
Two-Sample Comparison
Tests
P-values turn red
when results are
significant!Rules based
yellow highlight to
aid interpretation!
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Statistical Tools: One-Way
ANOVA & Means Matrix
3.08
3.28
3.48
3.68
3.88
4.08
4.28
4.48
1 2 3
Customer Type
Mean
/C
I-
Overa
llSa
tis
fac
tion
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Statistical Tools:
Correlation Matrix
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Statistical Tools:
Multiple Linear Regression
Accepts continuous and/or categorical (discrete)predictors.
Categorical Predictors are coded with a 0,1 schememaking the interpretation easier than the -1,0,1scheme used by competitive products.
Interactive Predicted Response Calculator with95% Confidence Interval and 95% PredictionInterval.
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Statistical Tools:
Multiple Regression
Multiple Regression accepts Continuous and/or
Categorical Predictors!
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Statistical Tools:
Multiple Regression
Durbin-Watson Test with p-values
for positive and negative
autocorrelation!
Statistical Tools: Multiple
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Statistical Tools: Multiple
Regression Predicted
Response Calculator with
Confidence Intervals
Easy-to-use Calculator with
Confidence Intervals and Prediction Intervals!
St ti ti l T l
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Statistical Tools:
Multiple Regression with
Residual Plots
0
10
20
30
40
50
60
-0.88
-0.71
-0.54
-0.37
-0.19
-0.02 0.1
50.3
20.5
00.6
70.8
41.0
11.1
9
Regular Residuals
Frequency
-3
-2
-1
0
1
2
3
-0.90
-0.40 0.1
00.6
01.1
0
Residuals
NSCORE
-1
-0.5
0
0.5
1
1.5
0.00
20
.00
40
.00
60
.00
80
.00
100
.00
120
.00
Fitted Values
RegularResiduals
-1.00
-0.50
0.00
0.50
1.00
1.50
0 20 40 60 80 100 120Observation Order
RegularResiduals
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Statistical Tools:
Nonparametric Tests
1 Sample Sign
1 Sample Wilcoxon
2 Sample Mann-Whitney Kruskal-Wallis Median Test
Moods Median Test
Kruskal-Wallis and Moods include a graph ofGroup Medians and 95% Median ConfidenceIntervals
Runs Test
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Statistical Tools:
Chi-Square Test
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Statistical Tools: Power &
Sample Size Calculators
1 Sample t-Test
2 Sample t-Test
One-Way ANOVA 1 Proportion Test
2 Proportions Test
The Power and Sample Size Calculatorsallow you to solve for Power (1 Beta),Sample Size, or Difference (specify two, solve
for the third).
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Statistical Tools: Power &
Sample Size Charts
Power & Sample Size: 1 Sample t-Test
0
0.2
0.4
0.6
0.8
1
1.2
0 10 20 30 40 50 60
Sample Size (N)
Power
(1-
Be
ta)
Difference = 0.5
Difference = 1
Difference = 1.5
Difference = 2
Difference = 2.5
Difference = 3
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Measurement Systems
Analysis
Basic MSA Templates
Create Gage R&R (Crossed) Worksheet
Generate worksheet with user specifiednumber of parts, operators, replicates
Analyze Gage R&R (Crossed)
Attribute MSA (Binary)
Measurement Systems
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Measurement Systems
Analysis: Gage R&R
Template
Measurement Systems
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Measurement Systems
Analysis: Create Gage R&R
(Crossed) Worksheet
Meas rement S stems
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Measurement Systems
Analysis: Analyze Gage
R&R (Crossed)
ANOVA, %Total, %Tolerance (2-Sided or 1-Sided), %Process, Variance Components,
Number of Distinct Categories
Gage R&R Multi-Vari and X-bar R Charts
Confidence Intervals on %Total, %Tolerance,
%Process and Standard Deviations
Handles unbalanced data (confidenceintervals not reported in this case)
Measurement Systems
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Measurement Systems
Analysis: Analyze Gage
R&R (Crossed)
Measurement Systems
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Measurement Systems
Analysis:
Analyze Gage R&R with
Confidence Intervals
Confidence Intervals are calculated for Gage R&R Metrics!
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Measurement Systems
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Measurement Systems
Analysis: Analyze Gage
R&R X-bar & R ChartsGage R&R - X-Bar by Operator
1.4213
1.3812
1.4615
1.1930
1.2430
1.2930
1.3430
1.3930
1.4430
1.4930
1.5430
Part
01_O
pera
torA
Part
01_O
pera
torB
Part
01_O
pera
torC
Part
02_O
pera
torA
Part
02_O
pera
torB
Part
02_O
pera
torC
Part
03_O
pera
torA
Part
03_O
pera
torB
Part
03_O
pera
torC
Part
04_O
pera
torA
Part
04_O
pera
torB
Part
04_O
pera
torC
Part
05_O
pera
torA
Part
05_O
pera
torB
Part
05_O
pera
torC
Part
06_O
pera
torA
Part
06_O
pera
torB
Part
06_O
pera
torC
Part
07_O
pera
torA
Part
07_O
pera
torB
Part
07_O
pera
torC
Part
08_O
pera
torA
Part
08_O
pera
torB
Part
08_O
pera
torC
Part
09_O
pera
torA
Part
09_O
pera
torB
Part
09_O
pera
torC
Part
10_O
pera
torA
Part
10_O
pera
torB
Part
10_O
pera
torC
X-Bar-
Pa
rt/Opera
tor-
Measuremen
t
Gage R&R - R-Chart by Operator
0.021
0.000
0.070
-0.003
0.007
0.017
0.027
0.037
0.047
0.057
0.067
Part01_
Oper
ator
A
Part01_
Oper
ator
B
Part01_
Oper
ator
C
Part02_
Oper
ator
A
Part02_
Oper
ator
B
Part02_
Oper
ator
C
Part03_
Oper
ator
A
Part03_
Oper
ator
B
Part03_
Oper
ator
C
Part04_
Oper
ator
A
Part04_
Oper
ator
B
Part04_
Oper
ator
C
Part05_
Oper
ator
A
Part05_
Oper
ator
B
Part05_
Oper
ator
C
Part06_
Oper
ator
A
Part06_
Oper
ator
B
Part06_
Oper
ator
C
Part07_
Oper
ator
A
Part07_
Oper
ator
B
Part07_
Oper
ator
C
Part08_
Oper
ator
A
Part08_
Oper
ator
B
Part08_
Oper
ator
C
Part09_
Oper
ator
A
Part09_
Oper
ator
B
Part09_
Oper
ator
C
Part10_
Oper
ator
A
Part10_
Oper
ator
B
Part10_
Oper
ator
C
R-
Part
/Opera
tor-
Measureme
nt
Measurement Systems
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Measurement Systems
Analysis: Analyze Gage
R&R Multi-Vari Charts
Gage R&R Multi-Vari
1.20879
1.25879
1.30879
1.35879
1.40879
1.45879
1.50879
Operator A Operator B Operator C
Operator - Part 01
Mean
Op
tions-
To
tal
Gage R&R Multi-Vari
1.20879
1.25879
1.30879
1.35879
1.40879
1.45879
1.50879
Operator A Operator B Operator C
Operator - Part 02
Measurement Systems
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Measurement Systems
Analysis: Attribute MSA
(Binary)Any number of samples, appraisers and
replicates
Within Appraiser Agreement, EachAppraiser vs Standard Agreement, EachAppraiser vs Standard Disagreement,
Between Appraiser Agreement, AllAppraisers vs Standard Agreement
Fleiss' kappa
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Process Capability
Process Capability/Sigma Level Templates
Multiple Histograms and Process Capability
Capability Combination Report for
Individuals/Subgroups:
Histogram
Capability Report (Cp, Cpk, Pp, Ppk, Cpm, ppm, %)
Normal Probability Plot Anderson-Darling Normality Test
Control Charts
Box-Cox Transformation
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Process Capability:
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Process Capability:
Box-Cox Power
Transformation
Normality Test is
automatically applied
to transformed data!
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Design of Experiments
Basic DOE Templates
Automatic update to Pareto of Coefficients
Easy to use, ideal for training Generate 2-Level Factorial and Plackett-
Burman Screening Designs
Main Effects & Interaction Plots
Analyze 2-Level Factorial and Plackett-Burman Screening Designs
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Basic DOE Templates
Design of Experiments:
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Design of Experiments:
Generate 2-Level Factorial
and Plackett-Burman
Screening Designs
User-friendly dialog box
2 to 19 Factors 4,8,12,16,20 Runs
Unique view power analysis as you design
Randomization, Replication, Blocking andCenter Points
Design of Experiments:
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g p
Generate 2-Level Factorial
and Plackett-Burman
Screening Designs
View Power Information
as you design!
Design of Experiments
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Design of Experiments
Example: 3-Factor, 2-Level
Full-Factorial Catapult DOEObjective: Hit a target at exactly 100 inches!
Design of Experiments:
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Design of Experiments:
Main Effects and
Interaction Plots
Design of Experiments:
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g
Analyze 2-Level Factorial
and Plackett-Burman
Screening Designs
Used in conjunction with Recall Last Dialog, itis very easy to iteratively remove terms from
the model Interactive Predicted Response Calculator
with 95% Confidence Interval and 95%Prediction Interval.
ANOVA report for Blocks, Pure Error, Lack-of-fit and Curvature
Collinearity Variance Inflation Factor (VIF)
and Tolerance report
Design of Experiments:
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Analyze 2-Level Factorial
and Plackett-Burman
Screening Designs
Residual plots: histogram, normal probabilityplot, residuals vs. time, residuals vs. predicted
and residuals vs. X factors Residual types include Regular,
Standardized, Studentized (Deleted t) and
Cook's Distance (Influence), Leverage andDFITS
Highlight of significant outliers in residuals
Durbin-Watson Test for Autocorrelation in
Residuals with p-value
Design of Experiments
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Design of Experiments
Example: Analyze Catapult
DOE
Pareto Chart of Coefficients for Distance
0
5
10
15
20
25
A:PullB
ack
C:PinH
eigh
t
B:Sto
pPin AC AB AB
C BC
Abs(Coefficient)
Design of Experiments:
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Design of Experiments:
Predicted Response
Calculator
Excels Solver is used with the
Predicted Response Calculator to
determine optimal X factorsettings to hit a target distance of
100 inches.
95% Confidence Interval and
Prediction Interval
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Control Charts
Individuals
Individuals & Moving Range
X-bar & R X-bar & S
P, NP, C, U
P and U (Laney) to handle overdispersion I-MR-R (Between/Within)
I-MR-S (Between/Within)
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Control Charts
Tests for Special Causes Special causes are also labeled on the control
chart data point.
Set defaults to apply any or all of Tests 1-8 Control Chart Selection Tool Simplifies the selection of appropriate control chart
based on data type
Process Capability report Pp, Ppk, Cp, Cpk
Available for I, I-MR, X-Bar & R, X-bar & S charts.
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Control Charts
Add data to existing charts ideal foroperator ease of use!
Scroll through charts with user definedwindow size
Advanced Control Limit options: SubgroupStart and End; Historical Groups (e.g. splitcontrol limits to demonstrate before and afterimprovement)
Box-Cox Transformation
Control Charts:
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Individuals &
Moving Range Charts
32.58
Mean CL: 49.02
65.46
29.32
34.32
39.32
44.32
49.32
54.32
59.32
64.32
69.32
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95 97 99
Individua
ls-
Avg
days
Order
tode
livery
time
0.00000
6.18182
20.19600
0.00
5.00
10.00
15.00
20.00
25.00
1
MR-
Avg
days
Order
tode
livery
time
Control Charts:
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Control Charts:
X-bar & R/S Charts
93.92
100.37
106.81
84.52921561
89.52921561
94.52921561
99.52921561
104.5292156
109.5292156
114.5292156
John
Moe
Sally Su
eDa
vidJo
hnM
oeSa
lly Sue
David
John
Moe
Sally Su
eDa
vidJo
hnM
oeSa
lly Sue
David
X-Bar-
Sho
t1-
Sho
t3
0.00000
6.30000
16.21776
0
2
4
6
8
10
12
14
16
John
Moe
Sally
Sue
David
John
Moe
Sally
Sue
David
John
Moe
Sally
Sue
David
John
Moe
Sally
Sue
David
R-
Sho
t1-
Sho
t3
C t l Ch t I MR R/S
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Control Charts: I-MR-R/S
Charts (Between/Within)
91.50
100.37
109.23
82.35
87.35
92.35
97.35
102.35
107.35
112.35
117.35
Joh
n
M
oe
Sall
ySu
e
Dav
id
Joh
n
M
oe
Sall
ySu
e
Dav
id
Joh
n
M
oe
Sall
ySu
e
Dav
id
Joh
n
M
oe
Sall
ySu
e
Dav
id
Individuals-Shot1-Shot3
0.00000
3.33333
10.89000
0.00
2.00
4.00
6.00
8.00
10.00
John Moe Sally Sue Da
vid John Moe Sally Sue Da
vid John Moe Sally Sue Da
vid John Moe Sally Sue
MR-Shot1-Shot3
0.00000
6.30000
16.21776
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
John
Moe
Sally Su
eDa
vidJo
hnM
oeSa
lly Sue
David
John
Moe
Sally Su
eDa
vidJo
hnM
oeSa
lly
R-Shot1-Shot3
Control Chart Selection
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Control Chart Selection
Tool
Simplifies theselection of
appropriate controlchart based ondata type
Includes DataTypes andDefinitions helptab.
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Control Charts:
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Summary Report on
Tests for Special Causes
Control Charts:
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Use Historical Groups to
Display Before Versus
After Improvement
Mean CL: 0.10
-6.80
7.00
-19
-14
-9
-4
1
6
11
16
21
26
31
Individ
uals-DeliveryTimeDevia
tion
Before Improvement After Improvement
Control Charts:
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Scroll Through Charts With
User Defined Window Size
Control Charts:P C bilit R t
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Process Capability Report
(Long Term/Short Term)
Control Charts:
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Box-Cox Power
Transformation
Normality Test is
automatically applied
to transformed data!
Reliability/Weibull
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Reliability/Weibull
Analysis
Weibull Analysis
Complete and Right Censored data
Least Squares and Maximum Likelihoodmethods
Output includes percentiles with confidence
intervals, survival probabilities, and Weibullprobability plot.
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SigmaXLTraining
We now offer On-Site and Public Training inSigmaXL.
Course Duration: 4.5 Days. Tuition is $1500 per participant, 20% discount for
groups of 3 or more from the same company.
Tuition includes a perpetual license of SigmaXL!
Instructor is John Noguera, SigmaXL co-founder,Six Sigma Master Black Belt, Motorola UniversitySenior Instructor.
Hands-on exercises with catapult.
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SigmaXLTraining
Course Contents:
Day 1: Introduction to SigmaXL, BasicGraphical Tools and Descriptive Statistics
Day 2: Measurement Systems Analysis,Process Capability
Day 3: Comparative Methods, Multi-Vari
Analysis Day 4: Correlation, Regression and
Introduction to DOE