KITCHENER, Ontario, Feb. 25, 2021 (GLOBE NEWSWIRE) -- SigmaXL Inc., a leading provider of user-friendly Excel Add-ins for Statistical and Graphical analysis, announces the release of SigmaXL Version 9 for Mac.
“SigmaXL was designed from the ground up to be a cost-effective, powerful, but easy to use tool that enables users to measure, analyze, improve and control their service, transactional, and manufacturing processes. As an add-in to the already familiar Microsoft Excel, SigmaXL is ideal for Lean Six Sigma training or use in a college statistics course, and is now compatible with Mac Excel 2016, 2019 and 365. Version 9 adds Time Series Forecasting and advanced control charts,” said John Noguera, CTO, SigmaXL.
New features in Version 9 include:
Powerful and Easy-to-Use Time Series Forecasting and Control Charts for Autocorrelated Data
- Run Chart
- Autocorrelation Function (ACF)/Partial Autocorrelation (PACF) Plots
- Cross Correlation (CCF) Plots with Pre-Whiten Data option
- Seasonal Trend Decomposition Plots
- Spectral Density Plot with Detection of Seasonal Frequency
- Exponential Smoothing Forecast. Models include:
- Additive/Multiplicative Error
- Additive/Additive Damped Trend
- Additive/Multiplicative Seasonal
- Multiple Seasonal Decomposition
- Exponential Smoothing Residuals Control Chart for autocorrelated data
- Autoregressive Integrated Moving Average (ARIMA) Forecast with support for:
- Predictors (Continuous and/or Categorical)
- Multiple Seasonal Decomposition
- ARIMA Residuals Control Chart for autocorrelated data
- Utilities: Difference Data, Lag Data, Interpolate Missing Values
- Model Features:
- ARIMA and Exponential Smoothing models are fully automatic or user specified
- Utilizes modern State Space and Kalman Filter models for accurate parameter estimation
- ARIMA estimates missing values with Kalman Filter; Exponential Smoothing uses seasonally adjusted linear interpolation
- Automatic Box-Cox Transformation
- Automatic seasonal frequency detection
- Model Diagnostics:
- ACF/PACF plots, Ljung-Box p-values
- Residual StDev, Log-Likelihood, AIC, AICc, BIC
- Residual plots
- Forecast Accuracy:
- Metrics: RMSE, MAE, MASE, MAPE
- In-Sample (Estimation) one-step-ahead forecast errors
- Out-of-Sample (Withhold) one-step-ahead and multi-step-ahead forecast errors
- SigmaXL’s forecasting capabilities were evaluated using the benchmark standard M4 forecast competition data, a total of 100,000 data sets with Yearly, Quarterly, Monthly, Weekly, Daily and Hourly data. Using a hybrid average of automatic Exponential Smoothing and ARIMA, the Overall Weighted Average forecast accuracy score outperformed three well-known commercial forecast software packages.
New and Improved Control Charts
- New Control Chart Templates
- Rare Events T, G and Probability-Based G
- Trend/Tool Wear
- Exponentially Weighted Moving Average (EWMA)
- Tabular Cumulative Sum (CUSUM)
- Average Run Length (ARL) Calculators:
- Shewhart with Tests for Special Causes
- Attribute C & P
- EWMA & CUSUM
- Markov Chain Approximation - fast and accurate
- Monte Carlo Simulation - additional Run Length statistics: Standard Deviation and Percentiles
- Test robustness to non-normality with specified Skewness & Kurtosis
- Tests for Special Causes now supported for menu-based control charts:
- Varying Subgroup Sizes (Moving Limits)
- Historical Groups
- MR/Range/StDev Charts (Tests 1-4)
A free 30-day trial version is available for download from the SigmaXL website at: www.SigmaXL.com.
About SigmaXL Inc.
SigmaXL is a leading provider of user-friendly Excel Add-ins for Lean Six Sigma tools and Monte Carlo Simulation. SigmaXL customers include market leaders like DHL, FedEx, Hanes, Motorola, NASA, Shell, Sonoco, Southwest Airlines and Tyson Foods. SigmaXL software is also used by numerous colleges, universities and government agencies.
For more information, visit http://www.SigmaXL.com or call 1-888-SigmaXL (888-744-6295).
Press Contact: Diane Tilley (888) 744-6295
A photo accompanying this announcement is available at https://www.globenewswire.com/NewsRoom/AttachmentNg/0be4fd70-39c0-412a-bccd-c4bff4fa29ea