SigmaXL - Powerful Statistical and Graphical Analysis
SigmaXL is a leading provider of user friendly Excel add-in tools for statistical and graphical analysis. Our flagship product, SigmaXL was designed from the ground up to be cost-effective, powerful, and easy to use. It allows users to measure, analyze, improve, and control their service, transactional and manufacturing process. As an add-in to the already familiar Microsoft Excel, SigmaXL is ideal for Six Sigma training and is used by leading consultants. It is rapidly becoming the tool of choice for Six Sigma professionals.
SigmaXL is a fraction of the cost of any major statistical product, yet it has all the statistical and graphical functionality most people need.
SigmaXL includes Lean and DMAIC Templates and a Control Chart Selection tool to simplify the selection of SPC Charts.
An example of user friendliness is the Two Sample Comparison Test, which automatically tests for normality, equal variance, means, and medians. It also provides rule-based yellow highlighting to aid the user's interpretation of the output. Low p-values are highlighted in red indicating that the results are significant.
You can design experiments and "view power analysis" as you design! This allows you to readily see the trade off between the cost of experimental runs/replicates and the corresponding statistical power - before you finalize the design.
SigmaXL is the perfect tool for Six Sigma® Green Belts, Black Belts, Quality and Business Professionals, Engineers, and Managers.
SigmaXL V7 is compatible with Excel 2007-2013 and can accommodate more than 1 million rows of data. Included are additional Six Sigma DMAIC templates, a DMAIC Menu option and a Control Chart Selection Tool to simplify the selection of SPC charts
SigmaXL Version 7.0
SigmaXL Version 7.0 Feature List Summary
Compatible with Excel 2010/2013/2016: 32 & 64 bit
Menu Layout: Classical or DMAIC
Recall Last Dialog & Worksheet Manager
Data Manipulation:
Subset by Category, Random, Number, or Date
Transpose Data
Stack Subgroups Across Rows
Stack and Unstack Columns
Standardize Data
Random Number Generator
Normal
Uniform (Continuous & Integer)
Lognormal
Weibull
Triangular
Data Preparation
Remove Blank Rows and Columns
Change Text Data Format to Numeric
Box-Cox Transformation
Templates & Calculators:
DMAIC & DFSS Templates
Team/Project Charter
SIPOC Diagram
Flowchart Toolbar
Data Measurement Plan
Cause & Effect (Fishbone) Diagram and Quick Template
Cause & Effect (XY) Matrix with Pareto
Failure Mode & Effects Analysis (FMEA) with RPN Sort
Quality Function Deployment (QFD)
Pugh Concept Selection Matrix
Control Plan
Lean Templates
Takt Time Calculator
Value Analysis/Process Load Balance
Value Stream Mapping
Graphical Templates
Pareto Chart
Histogram
Run Chart
Statistical Templates
Sample Size – Discrete and Continuous
Minimum Sample Size for Robust t-Tests and ANOVA*
1 Sample t-Test and Confidence Interval for Mean
2 Sample t-Test and Confidence Interval (Compare 2 Means)
1 Sample Chi-Square Test and CI for Standard Deviation*
2 Sample F-Test and CI (Compare 2 Standard Deviations) *
1 Proportion Test and Confidence Interval
2 Proportion Tests and Confidence Interval
1 Poisson Rate Test and Confidence Interval
2 Poisson Rates Test and Confidence Interval
One-Way Chi-Square and Goodness of Fit Test*
One-Way Chi-Square and Goodness of Fit Test - Exact**
Probability Distribution Calculators
Normal, Lognormal, Exponential, Weibull
Binomial, Poisson, Hypergeometric
MSA Templates
Gage R&R Study – with Multi-Vari Analysis
Attribute Gage R&R (Attribute Agreement Analysis)
Process Sigma Level – Discrete and Continuous
Process Capability & Confidence Intervals
DOE Templates
2 to 5 Factors
2-Level Full and Fractional-Factorial designs
Main Effects & Interaction Plots
Control Chart Templates
Individuals
C-Chart
Graphical Tools:
Basic and Advanced (Multiple) Pareto Charts
EZ-Pivot/Pivot Charts: Easily create Pivot Tables and Charts
Basic Histogram
Multiple Histograms and Descriptive Statistics (includes Confidence Interval for Mean and StDev., and Anderson-Darling Normality Test)
Multiple Histograms and Process Capability
(Pp, Ppk, Cpm, ppm, %)
Multiple Boxplots, Dotplots
Run Charts (with Nonparametric Runs Test allowing you to test for Clustering, Mixtures, Lack of Randomness, Trends and Oscillation)
Overlay Run Chart
Multiple Normal Probability Plots (with 95% confidence intervals to ease interpretation of normality/non-normality)
Multi-Vari Charts
Scatter Plots (with linear regression and optional 95% confidence intervals and prediction intervals)
Scatter Plot Matrix
Measurement Systems Analysis:
Create Gage R&R (Crossed) Worksheet:
Generate worksheet with user specified number of parts, operators, replicates
Analyze Gage R&R (Crossed)
ANOVA, %Total, %Tolerance (with upper and/or lower specifications), %Process, Variance Components, Number of Distinct Categories
Gage R&R Multi-Vari and X-bar R Charts
Confidence Intervals for %Total, %Tolerance, %Process and Standard Deviations Handles unbalanced data
Attribute MSA (Binary)*
Any number of samples, appraisers and replicates
Within Appraiser Agreement, Each Appraiser vs Standard Agreement, Each Appraiser vs Standard
Attribute MSA (Ordinal)**
Confidence Interval Graphs for Percent Agreement and Kendall's Coefficients (Concordance and Correlation)
Attribute MSA (Nominal)**
Confidence Interval Graphs for Percent Agreement and Fleiss' Kappa Coefficient
Kappa color highlight to aid interpretation: Green (>.9), Yellow (.7-.9) and Red (<.7)
Process Capability:
Multiple Histograms and Process Capability
Capability Combination Report for Individuals/Subgroups:
Histogram, Normal Probability Plot and Normality Test
Capability Report (Cp, Cpk, Pp, Ppk, Cpm, ppm, %)
Control Charts
Capability Combination Report for Nonnormal Data (Individuals)
Box-Cox Transformation (includes an automatic threshold option so that data with negative values can be transformed)
Johnson Transformation
Distributions supported: Half-Normal, Lognormal (2 & 3 parameter), Exponential (1 & 2), Weibull (2 & 3), Beta (2 & 4), Gamma (2 & 3), Logistic, Loglogistic (2 & 3), Largest Extreme Value, Smallest Extreme Value
Nonnormal Process Capability Indices: Z-Score (Cp, Cpk, Pp, Ppk) and Percentile (ISO) Method (Pp, Ppk)
Distribution Fitting Report
All valid distributions and transformations reported with histograms, curve fit and probability plots
Within Appraiser Agreement, Each Appraiser vs Standard Agreement, Each Appraiser vs Standard Disagreement, Between Appraiser Agreement, All Appraisers vs Standard Agreement; Fleiss' kappa
Statistical Tools:
P-Values turn red when results are significant (P-Value < alpha)
Descriptive Statistics including Anderson-Darling Normality test, Skewness and Kurtosis with P-Values
Confidence Intervals
1 Sample t-test*
2 Sample t-test, Paired t-test*
2 Sample comparison tests*:
Reports AD Normality, F-test and Levene’s for variance, t-test assuming equal and unequal variance, Mann-Whitney test for medians.
Recommended tests are highlighted based on sample size, normality, and variance
One-Way ANOVA and Means Matrix
Two-Way ANOVA (Balanced and Unbalanced)
Equal Variance Tests (Bartlett, Levene and Welch’s ANOVA)*
Correlation Matrix (Pearson and Spearman’s Rank Correlation)*
Multiple Linear Regression:
Accepts continuous and/or categorical (discrete) predictors
Interactive Predicted Response Calculator with 95% Confidence Interval and 95% Prediction Interval
Residual Plots: histogram, normal probability plot, 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 and DFITS
Highlight of significant outliers in residuals
Durbin-Watson Test for Autocorrelation in Residuals with P-Value
ANOVA report for categorical predictors
Pure Error and Lack-of-Fit report
Collinearity Variance Inflation Factor (VIF) and Tolerance report
Fit Intercept is optional
Binary and Ordinal Logistic Regression
Powerful and user-friendly logistic regression.
Report includes a calculator to predict the response event probability for a given set of input X values.
Categorical (discrete) predictors can be included in the model in addition to continuous predictors.
Model summary and goodness of fit tests include Likelihood Ratio Chi-Square, Pseudo R-Square, Pearson Residuals Chi-Square, Deviance Residuals Chi-Square, Observed and Predicted Outcomes – Percent Correctly Predicted.
Stored data includes Event Probabilities, Predicted Outcome, Observed-Predicted, Pearson Residuals, Standardized Pearson Residuals, and Deviance Residuals.
Chi-Square Test* (Stacked Column data and Two-Way Table data)
One Way Chi-Square Goodness-of-Fit Test
Chi-Square Test Two-Way Table Data
Chi-Square Test - Exact**
Chi-Square Test - Fisher's Exact**
Chi-Square Test Two-Way Table Data - Fisher's Exact**
One-Way Chi-Square Goodness-of-Fit Exact Template
Nonparametric Tests*:
1 Sample Sign and 1 Sample Wilcoxon
2 Sample Mann-Whitney
Kruskal-Wallis and Mood’s Median Test
Kruskal-Wallis and Mood’s include a graph of Group Medians and 95% Median Confidence Intervals
Runs Test
Nonparametric Tests - Exact**:
1 Sample Wilcoxon Signed Ranked Test- Exact
2 Sample Mann-Whitney - Exact
Kruskal-Wallis - Exact
Mood's Median Test- Exact
Runs Test - Exact
Power and Sample Size Calculators for:
1 Sample t-Test, 2 Sample t-Test*
One-Way ANOVA*
1 Proportion Test, 2 Proportions Test*
The Power and Sample Size Calculators allow you to solve for Power (1 – Beta), Sample Size, or Difference (specify two, solve for the third).
Power and Sample Size Chart. Quickly create a graph showing the relationship between Power, Sample Size and Difference.
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 and Center Points
Basic DOE Templates
2 to 5 Factors, 2-Level Full and Fractional-Factorial designs
Automatic update to Pareto of Coefficients
Easy to use, ideal for training part
Main Effects & Interaction Plots
Analyze 2-Level Factorial and Plackett-Burman Screening Designs
Used in conjunction with Recall Last Dialog, it is 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
Residual plots: histogram, normal probability plot, residuals vs. time, residuals vs. predicted and residuals vs. X factors
Highlight of significant outliers in residuals
Durbin-Watson Test for Autocorrelation
Contour & 3D Surface Plots
Response Surface Designs
2 to 5 Factors
Central Composite and Box-Behnken Designs
Easy to use design selection sorted by number of runs
Control Charts:
Control Chart Selection Tool
Individuals
X-Bar & R, X-Bar & S
I-MR-R, I-MR-S (Between/Within)
P, NP, C, U
P’ and U’ (Laney) to handle overdispersion
Control charts include a report on 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.
Process Capability report (Pp, Ppk, Cp, Cpk) is available for I, I-MR, X-Bar & R, X-bar & S charts.
Add data to existing charts for operator ease of use!
Scroll through charts with user defined window size
Advanced Control Limit options: Subgroup Start and End; Historical Groups (e.g. split control limits to demonstrate before and after improvement)
Exclude data points for control limit calculation
Add comment to data point for assignable cause
± 1, 2 Sigma Zone Lines
Control charts for Nonnormal data (Individuals)
Box-Cox and Johnson Transformations
16 Nonnormal distributions supported (see Process Capability)
Individuals chart of original data with percentile based control limits
Individuals/Moving Range chart for normalized data with optional tests for special causes
Reliability/Weibull Analysis:
Weibull Analysis
Complete and Right Censored data
Least Squares and Maximum Likelihood
Output includes percentiles with confidence intervals, survival probabilities, and Weibull probability plot.
