Statgraphics is a comprehensive software for statistical analysis, data visualization and predictive analytics. Statgraphics is designed to make the power of data science available to everyone who collects data and features an easy-to-use user interface that does not require learning a complicated command language. Especially helpful to practitioners is the StatAdvisor, which explains the results of statistical analyses in a manner suitable for presentation to non-statisticians.
What is Statgraphics?
In today’s data-driven world, having the right tools to analyze and present data effectively is more important than ever. That’s where Statgraphics comes in. This powerful software suite offers a range of advanced statistical analysis and visualization tools, making it a must-have for any data professional.
Statgraphics Centurion is a comprehensive Windows desktop product for statistical analysis, data visualization and predictive analytics. It is designed to make the power of data science available to everyone who collects data. The latest version, Statgraphics 19, features an easy-to-use GUI that does not require learning a complicated command language. Especially helpful to practitioners is the StatAdvisor, which explains the results of statistical analyses in a manner suitable for presentation to non-statisticians.
Statgraphics 19 includes over 290 statistical procedures and special features, with many recent additions in the areas of data visualization, predictive analytics, data mining and machine learning.
An important technique for analyzing the effect of categorical factors on a response is to perform an Analysis of Variance. Statgraphics Technologies products provide several procedures for performing an analysis of variance: One-Way ANOVA, Multifactor ANOVA, Variance Components Analysis, General Linear Models, and Repeated Measures ANOVA.
Statgraphics provide a wide range of procedures for accomplishing basic statistical tasks. One Sample Analysis, Outlier Identification, Comparing Two Independent Samples, Comparing Two Paired Samples, Comparing Multiple Samples, Comparing Rates and Proportions, Equivalence and Noninferiority Tests for Means, Equivalence and Noninferiority Tests for Variances, and Power Transformations.
Categorical data classifies an observation as belonging to one or more categories. Tabulation, Donut Chart, Frequency Tables, Crosstabulation, Contingency Tables, Median Polish, Correspondence Analysis, Multiple Correspondence Analysis, Likert Plot, Item Reliability Analysis, Tornado and Butterfly Plots, and Venn and Euler Diagrams.
Data Mining refers to a process by which patterns are extracted from data. Such patterns often provide insights into relationships that can be used to improve business decision-making. Clustering, Classification, Association, Prediction, Classification and Regression Trees, Text Mining, Decision Forests, and K-Means Clustering.
Statgraphics contains extensive capabilities for creating and analysing statistically designed experiments (DOE). Screening Designs, Response Surface Designs, Mixture Experiments, D-Optimal Designs, Robust Parameter Designs, Definitive Screening Designs, Computer Generated Designs, Single Factor Categorical Designs, Multi-Factor Categorical Designs, Variance Component Designs, Design of Experiments Wizard, Multiple Response Optimization, Alias Optimal Designs, and Computer Augmented Designs.
Exploratory Data Analysis refers to a set of techniques originally developed by John Tukey to display data so that interesting features will become apparent. Box-and-Whisker Plots, Stem-and-Leaf Display, Rootogram, Resistant Time Series Smoothing, Scatterplot Smoothing, Median Polish, Bubble Chart, Resistant Curve Fitting, Multi-Vari Chart, Violin Plot, Wind Rose, Diamond Plot, Heat Map, Population Pyramid, and Sunflower Plot.
Determining the reliability of manufactured items often requires performing a life test and analyzing observed times to failure. Life Tables, Distribution Fitting with Censored Data, Weibull Analysis, Arrhenius Plot, Life Data Regression, Cox Proportional Hazards, Analysis of Repairable Systems, and Reliability Demonstration Test Plans.
Measurement Systems Analysis (Gage Studies)
Measurement systems analysis (MSA) assesses variations contributed by the measuring system. When implementing any statistical method that relies on data, it is important to be sure that the systems that collect that data are both accurate and precise.
Monte Carlo simulation estimates the distribution of variables when it is impossible or impractical to determine that distribution theoretically. It is used in many areas, including engineering, finance, and DFSS (Design for Six Sigma). General Simulation Models, ARIMA Time Series Models, Random Number Generation, and Multivariate Normal Random Numbers.
Multivariate statistical methods are used to analyze the joint behaviour of more than one random variable. There is a wide range of multivariate techniques available. Matrix Plot, Correlation Analysis, Spider/Radar Plot, Principal Components and Factor Analysis, Cluster Analysis, Discriminant Analysis, Neural Network Bayesian Classifier, Partial Least Squares, Canonical Correlations, Multivariate Normality Test, Multivariate Tolerance Limits, and Multidimensional Scaling.
Non-parametric methods are used to analyze data when the distributional assumptions of more common procedures are not satisfied. Goodness-of-Fit Tests, Inferences in One Sample or Paired Samples, Comparing Two Samples, Comparing Multiple Samples, Correlation Analysis, Tests for Association, Tests for Randomness, Density Estimation, Curve Fitting, Nonparametric Tolerance Limits, Neural Networks, Median Polish, and Kriging.
Statgraphics contains several procedures for manipulating statistical probability distributions. Each of more than 50 distributions may be plotted, fit to data, and used to calculate critical values or tail areas.
Process capability analysis is an important technique used to determine how well a process meets a set of specification limits.
Regression analysis models the relationship between a response variable and one or more predictor variables. Statgraphics provides many procedures for fitting different types of regression models. Simple Regression, Box-Cox Transformations, Polynomial Regression, Calibration Models, Multiple Regression, Comparison of Regression Lines, Regression Model Selection, Ridge Regression, Nonlinear Regression, Partial Least Squares, General Linear Models, Life Data Regression, Regression Analysis for Proportions, Regression Analysis for Counts, Orthogonal Regression, Classification and Regression Trees, Piecewise Linear Regression, Quantile Regression, Stability Studies, and Zero-Inflated Count Regression.
Before collecting data, it is important to determine how many samples are needed to perform a reliable analysis. This requires finding an adequate sample size n so that the statistics to be estimated have a sufficiently small margin of error and that any statistical tests to be performed have the required power.
Six Sigma analysis is a structured approach to business management that concentrates on improving quality by reducing process variability and eliminating major failure mechanisms.
One of the most important actions that can help maintain the quality of any good or service is to collect relevant data consistently over time, plot it, and examine the plots carefully. All statistical process control charts plot data (or a statistic calculated from data) versus time, with control limits designed to alert the analyst to events beyond normal sampling variability.
Many types of data are collected over time. Stock prices, sales volumes, interest rates, and quality measurements are typical examples. Because of the sequential nature of the data, special statistical techniques that account for the dynamic nature of the data are required. Statgraphics provides several procedures for dealing with time series data.
Many techniques are available to visualize that information, depending on the nature of the data. Statgraphics contains a large set of visualization tools, many with built-in animation to show how data change over time.
What’s new in Statgraphics 19?
STATGRAPHICS Centurion XIX contains over 290 features, including a new link to Python functionality, a modernized graphical user interface with a convenient feature-locating ribbon bar, a procedure dashboard, big data capability, and so much more.
Statgraphics Centurion 19 is the latest version of the flagship Windows desktop product. It is a major upgrade, adding many new features for statistical analysis, data visualization and predictive analytics. Highlights include a new user interface, important enhancements to the Design of Experiments Wizard, new machine learning procedures, and methods for exchanging data and scripts with Python.
Statgraphics 19 Enhancements
Statgraphics requires Windows 7 (SP1 or later), Windows 8, Windows 10 and Windows 11.
Statgraphics can be delivered in these languages:
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