SigmaXL

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.