SigmaXL was meticulously crafted to serve as a budgetfriendly, robust, yet userfriendly solution that empowers individuals to assess, dissect, enhance, and manage their service, transactional, and manufacturing processes. Operating as an addin within the wellknown Microsoft Excel environment, SigmaXL® is exceptionally suited for both Lean Six Sigma training and practical implementation, as well as for integration into collegelevel statistics courses. Contact us for the SigmaXL software at an affordable price.
SigmaXL Price and Licensing Options
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SigmaXL Software Features
SigmaXL provides a huge number of features as follows:

Data Manipulation
 Subset by Category, Number, Date or Random
 Transpose Data
 Stack Subgroups Across Rows
 Stack and Unstack Columns
 Random Number Generator
 Normal
 Uniform (Continuous & Integer)
 Lognormal
 Weibull
 Triangular
 BoxCox Transformation
 Standardize Data
 Convert to Discrete
 Data Preparation
 Remove Blank Rows and Columns
 Change Text Data Format to Numeric
 Convert Raw Data to Frequency (Tally)
 Convert Frequency to Raw Data

Templates and Calculators
 DMAIC & DFSS Templates
 Team/Project Charter
 SIPOC Diagram
 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
 Probability Distribution Calculators
 Normal, Lognormal, Exponential, Weibull
 Binomial, Poisson, Hypergeometric
 Statistical Templates
 Sample Size  Discrete and Continuous
 Minimum Sample Size for Robust tTests and ANOVA
 1 Sample tTest and Confidence Interval for Mean
 1 Sample ZTest and Confidence Interval for Mean
 2 Sample tTest and Confidence Interval (Compare 2 Means)
 1 Sample Equivalence Test For Mean
 2 Sample Equivalence Test (Compare 2 Means)
 1 Sample ChiSquare Test and CI for Standard Deviation
 2 Sample FTest and CI (Compare 2 Standard Deviations)
 1 Proportion Test and Confidence Interval
 2 Proportions Tests and Confidence Interval
 2 Proportions Equivalence Test
 1 Poisson Rate Test and Confidence Interval
 2 Poisson Rates Test and Confidence Interval
 2 Poisson Rates Equivalence Test
 OneWay ChiSquare and Goodness of Fit Test (With Exact and Monte Carlo PValue)
 OneWay ChiSquare and Goodness of Fit Test  Exact
 Measurement System Analysis (MSA) Templates
 Type 1 Gage Study
 Gage Bias and Linearity Study
 Gage R&R Study  with MultiVari Analysis
 Attribute Gage R&R (Attribute Agreement Analysis)
 GLM GageRR (Crossed) Metrics with/without Interaction; GLM GageRR (Nested) Metrics; GLM GageRR (Expanded) Metrics
 Orthogonal (Deming) Regression
 Process Sigma Level  Discrete and Continuous
 Process Capability & Confidence Intervals
 Tolerance Interval Calculator (Normal Exact)
 DOE Templates
 2 to 5 Factors
 2Level Full and FractionalFactorial designs
 Main Effects & Interaction Plots
 Taguchi DOE Templates
 Taguchi L8 (2 Level) Three Factor  Robust Cake Example
 Taguchi L8 (2 Level) Four Factor  Catapult Example
 Taguchi L9 (3 Level) Four Factor  Paper Airplane Example
 L4, L8, L9, L12, L16, L18, L27
 SignaltoNoise Ratios: Nominal is Best, Nominal is Best (Variance Only), Nominal is Best (Mean Square Deviation with Target), Larger is
 Better, Smaller is Better
 Pareto of Deltas (Effects) and ANOVA SS (SumofSquares) % Contribution (for Main Effects and TwoWay Interactions)
 Main Effects and Interaction Plots
 Control Chart Templates
 Individuals, CChart
 DMAIC & DFSS Templates

Graphical Tools
 Graphical Tool Selection Guide
 Basic and Advanced (Multiple) Pareto Charts
 EZPivot/Pivot Charts: Easily create Pivot Tables and Charts
 Heatmap
 Basic Histogram
 Multiple Histograms and Descriptive Statistics (includes Confidence Interval for Mean and StDev., and AndersonDarling Normality Test)
 Multiple Histograms and Process Capability (Pp, Ppk, Cpm, ppm, %)
 Multiple Dotplots, Multiple Boxplots, Multiple X Boxplots
 Interval Plots and Multiple X Interval Plots
 Multiple Normal Probability Plots (with 95% confidence intervals to ease interpretation of normality/nonnormality)
 Empirical/Normal CDF Plots
 Run Charts (with Nonparametric Runs Test allowing you to test for Clustering, Mixtures, Lack of Randomness, Trends and Oscillation)
 Overlay Run Chart
 MultiVari Charts
 Scatter Plots (with linear regression and optional 95% confidence intervals and prediction intervals)
 Scatter Plot Matrix
 XYZ Contour/Surface Plot**
 Automatic Interpolation Method Selection and XY Standardization using CrossValidation
 Inverse Distance, Akima’s Polynomial and Biharmonic Spline Interpolation
 Analysis of Means (ANOM) Charts
 ANOM Normal OneWay
 ANOM Normal TwoWay (with Main Effects and Slice Charts)
 ANOM Binomial Proportions OneWay
 ANOM Binomial Proportions TwoWay
 ANOM Poisson Rate OneWay
 ANOM Poisson Rate TwoWay
 Nonparametric Transformed Ranks
 Variances & Levene Robust Variances

Statistical Tools
 PValues turn red when results are significant (PValue < alpha)
 Descriptive Statistics including AndersonDarling Normality test, Skewness and Kurtosis with PValues
 Descriptive Statistics Options:
 Percentile Report and Percentile Ranges
 Percentile Confidence and Tolerance Intervals
 Additional Descriptive Statistics and Normality Tests (ShapiroWilk and DoomikHansen Normality)
 Outlier (Boxplot and Grubbs) and Randomness (Nonparametric Runs) Tests
 Hypothesis Test Selection Guide**
 1 Sample ttest and Confidence Intervals
 Paired ttest, 2 Sample ttest
 2 Sample comparison tests:
 Reports AD Normality, Ftest and Levene's for variance, ttest assuming equal and unequal variance, MannWhitney test for medians
 Recommended tests are highlighted based on sample size, normality, and variance
 OneWay ANOVA and Means Matrix
 Multiple Comparison of Means Probability Methods (PostHoc): Fisher, Tukey, Dunnett With Control
 Automatic Assumptions Check for One Sample, TwoSample, Paired Ttests and OneWay ANOVA
 Test report with color highlight: Green (OK), Yellow (Warning) and Red (Serious Violation)
 Test each sample for Normality. If not, check minimum sample size of robustness of test
 Check each sample for Outliers: Potential (Tukey's Boxplot 1.5*IQR); Likely (2.2*IQR); Extreme (3*IQR)
 Randomness (Nonparametric Runs Test)
 Equal Variance (for 2 or more samples)
 TwoWay ANOVA (Balanced and Unbalanced)
 Equal Variance Tests (Bartlett, Levene and Welch's ANOVA)
 Multiple Comparison of Variances Probability Methods (PostHoc): FTest (with Bonferroni Correction), Levene, Tukey ADM (Absolute Deviation for Medians)
 Welch Multiple Comparison of Means Probability Methods (PostHoc): Welch Pairwise, Games Howell
 Correlation Matrix (Pearson and Spearman's Rank Correlation)
 Automatic normality check for correlation utilizing the powerful DoornikHansen bivariate normality test
 Yellow highlight to recommend significant Pearson or Spearman correlation  Pearson is highlighted if the data are bivariate normal, otherwise Spearman is highlighted
 General Linear Model: Extends Advanced Multiple Regression to include:
 Fixed and Random Factors
 Nested Factors
 Covariates (can be Nested)
 For Random or Mixed Random/Fixed Factors with a balanced design, the ANOVA and Variance Components (VC) report is given based on Expected Mean Squares. VC confidence intervals using Restricted Maximum Likelihood (REML) are included.
 If the design is unbalanced or model is nonhierarchical, REML is used to compute the VC values and confidence intervals. Fixed Effects Tests are based on Satterthwaite approximation degrees of freedom.
 Main Effects with Confidence Intervals and Interaction Plots of Fitted Means for NonNested Fixed Factors
 Tukey and Fisher Pairwise Comparison of Means for NonNested Fixed Factors
 Predicted Response Calculator
 Multiple Response Optimization for Nested or NonNested Fixed Factors
 Advanced Multiple Regression:
 Standardization and coding of continuous predictors
 Option to display regression equation with unstandardized coefficients
 (1, 0) or (1,0,+1) coding of categorical predictors
 BoxCox Transformation
 Specify confidence level
 Residual Plots (Regular, Standardized, Studentized – Deleted t)
 Main Effects and Interaction Plots (Fitted Means)
 Contour and Surface Plots
 Optimization with optional constraints
 Automatic removal of extreme VIF or collinear terms (with alias and removal report)
 Specify interactions, quadratic and higher orders (all interactions or up to 3Way)
 ANOVA Type I and/or Type III SumofSquares with Pareto of Percent Contribution and Standardized Effects
 Lenth Pseudo Standard Error for Saturated Models (Orthogonal or NonOrthogonal) with Monte Carlo or Student T PValues
 Specify Test/Withhold Sample for Rsquare Test & StDev Test Validation
 RSquare Predicted (LeaveOneOut Cross Validation)
 RSquare KFold & StDev KFold (KFold Cross Validation)
 Test for Constant Variance: BreuschPagan. AndersonDarling Normality test is applied to residuals in order to automatically select
 Normal or Koenker (Robust) version. Report includes the Overall test and Individual predictors as well.
 White robust standard errors for nonconstant variance (HeteroskedasticityConsistent)
 DurbinWatson test for autocorrelation in residuals with PValues
 NeweyWest robust standard errors for nonconstant variance with autocorrelation (Heteroskedasticity and AutocorrelationConsistent)
 White or NeweyWest automatically selected based on DurbinWatson PValues
 Stepwise/Best Subsets Regression:
 Forward/Backward with alphatoenter, alphatoremove
 Forward Selection with alphatoenter
 Backward Elimination with alphatoremove
 Forward, Backward Criterion: Minimize AICc, BIC; Maximize RSquare Adjusted, RSquare Predicted, RSquare KFold
 Best Subsets utilizes the powerful MIDACO Solver (Mixed Integer Distributed Ant Colony Optimization) to solve best subsets with upto hundreds of continuous or categorical variables, including interactions and higher order terms. This feature gives SigmaXL a significant advantage over competitors with Best Subsets limited to 30 continuous variables.
 Best Subsets Criterion: Minimize AICc, BIC; Maximize RSquare Adjusted
 Hierarchical option
 Detailed report with additional statistics such as Condition Number and Mallows’ Cp.
 Multiple Response Optimization:
 Multiple Response Optimization with Desirability
 Multistart NelderMead Simplex
 MIDACO
 Multiple Response Optimization with Desirability
 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
 DurbinWatson Test for Autocorrelation in Residuals with pvalue
 ANOVA report for categorical predictors
 Pure Error and LackofFit report
 Collinearity Variance Inflation Factor (VIF) and Tolerance report
 Fit Intercept is optional
 Binary and Ordinal Logistic Regression:
 Powerful and userfriendly 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 ChiSquare, Pseudo RSquare, Pearson Residuals ChiSquare, Deviance. Residuals ChiSquare, Observed and Predicted Outcomes  Percent Correctly Predicted
 Stored data includes Event Probabilities, Predicted Outcome, ObservedPredicted, Pearson Residuals, Standardized Pearson Residuals, and Deviance Residuals
 ChiSquare Test (Stacked Column data and TwoWay Table data)
 With Fisher Exact (utilizing permutations and fast networks algorithms) and Monte Carlo PValues
 Options: Advanced Tests and Measures of Association for Nominal & Ordinal Categories
 Nonparametric Tests:
 1 Sample Sign and 1 Sample Wilcoxon
 2 Sample MannWhitney
 KruskalWallis and Mood's Median Test (With graph of Group Medians and 95% Median Confidence Intervals)
 Runs Test
 With Exact and Monte Carlo PValue
 Nonparametric Tests  Exact:
 1 Sample Wilcoxon  Exact
 2 Sample MannWhitney  Exact
 KruskalWallis Median Test  Test
 Mood's Median Test  Test
 Runs Test  Exact
 Power and Sample Size Calculators for:
 1 and 2 Sample tTest
 OneWay 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

Measurement System 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 MultiVari and Xbar R Charts
 Confidence Intervals for %Total, %Tolerance, %Process and Standard Deviations
 Handles unbalanced data
 Attribute MSA (Binary, Ordinal, Nominal)
 Attribute MSA (Binary)
 Attribute MSA (Ordinal)
 Attribute MSA (Nominal)
 Create Gage R&R (Crossed) Worksheet:

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
 Distribution Fitting Report
 All valid distributions and transformations reported with histograms, curve fit and probability plots
 Sorted by AD Pvalue
 Capability Combination Report for Nonnormal Data (Individuals)
 BoxCox Transformation (includes an automatic threshold option so that data with negative values can be transformed)
 Johnson Transformation
 Distributions supported: HalfNormal, 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
 Automatic Best Fit based on AD pvalue
 Nonnormal Process Capability indices: ZScore (Cp, Cpk, Pp, Ppk) and Percentile (ISO) Method (Pp, Ppk)

Design of Experiments
 Generate 2Level Factorial and PlackettBurman Screening Designs
 Userfriendly 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, 2Level Full and FractionalFactorial designs
 Automatic update to Pareto of Coefficients
 Easy to use, ideal for training
 Main Effects & Interaction Plots
 Contour & 3D Surface Plots
 Response Surface Designs
 2 to 5 Factors
 Central Composite and BoxBehnken Designs
 Easy to use design selection sorted by number of runs
 Analyze 2Level Factorial and PlackettBurman 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, LackofFit 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
 DurbinWatson Test for Autocorrelation
 Generate 2Level Factorial and PlackettBurman Screening Designs

Control Charts
 Control Chart Selection Guide
 Individuals, Individuals & Moving Range
 XBar & R, XBar & S
 IMRR, IMRS (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 18
 Process Capability report (Pp, Ppk, Cp, Cpk) is available for I, IMR, XBar & R, Xbar & 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)
 BoxCox 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
 Control Chart Templates: Rare Events
 Rare Events T Chart
 Rare Events G Chart
 Rare Events Prob G Chart
 Control Chart Templates: TimeWeighted
 Exponentially Weighted Moving Average (EWMA) Chart
 Tabular Cumulative Sum (CUSUM) Chart
 Control Chart Templates: Trend
 Trend Chart
 Control Chart Templates: Average Run Length (ARL) Calculators
 Average Run Length (ARL)
 Shewhart ARL
 Attribute P ARL
 Attribute C ARL
 EWMA ARL
 CUSUM ARL

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
 Weibull Analysis

Time Series Forecasting and Control Charts for Autocorrelated Data
 Autocorrelation (ACF/PACF) Plots
 Cross Correlation (CCF) Plots
 Spectral Density Plot
 Seasonal Trend Decomposition Plots
 Seasonal Interaction Plots
 Exponential Smoothing Forecast
 Exponential Smoothing – Multiple Seasonal Decomposition (MSD) Forecast
 Exponential Smoothing Control Chart
 Exponential Smoothing Multiple Seasonal Decomposition (MSD) Control Chart
 ARIMA Forecast
 ARIMA Forecast with Predictors
 ARIMA – Multiple Seasonal Decomposition (MSD) Forecast
 ARIMA Control Chart
 ARIMA Control Chart with Predictors
 ARIMA Multiple Seasonal Decomposition (MSD) Control Chart
 Utilities – Difference Data
 Utilities – Lag Data
 Utilities – Interpolate Missing Values