SigmaXLV5.2 Demonstration

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/


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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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Pugh Concept Selection

Matrix


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Value Analysis/

Process Load Balance Chart


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Failure Mode & Effects

Analysis (FMEA)


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Cause & Effect (XY)

Matrix


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Sample Size Calculators


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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


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


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


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



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


Count = 42Mean = 4.2052

Stdev = 0.621200

Range = 2.6

Minimum = 2.4200


50th Percentile (Median) = 4.3400


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


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


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


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 -


1.634

2.134

2.634

3.134

3.634

4.134

4.634

# 1 # 2 # 3



0.00

0.20

0.40

0.60

0.80

1.00

# 1 # 2 # 3



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


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


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


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


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


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


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


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



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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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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



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g p

Generate 2-Level Factorial

and Plackett-Burman

Screening Designs

View Power Information

as you design!



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Example: 3-Factor, 2-Level

Full-Factorial Catapult DOEObjective: Hit a target at exactly 100 inches!



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Main Effects and

Interaction Plots



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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



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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



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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)



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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)


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

SigmaXLV5.2 Demonstration

Documents