Spotfire Statistica® Analyst

Customers can purchase add-ons to Spotfire Statistica® Analyst for a metadata store, job server, versioning/approval, monitoring & alerting server, live scoring, manual data entry & analytics, and interactive dashboards. 

Spotfire Statistica® Analyst contains the following features:

• automation for data cleaning; dirty data is the most common analytics problem
• business rules builder
• exploratory analysis & visualizations; learn about the problem space
• Spotfire Statistica® PI Connector
• descriptive statistics, nonparametrics; learn and share factoids about the problem to build situational awareness
• linear regression models, nonlinear regression models; estimate the relationships among your variables and create predictive models (machine learning); also use simulated data to create linear regression models and learn something new
• multivariate exploratory techniques; organize data into meaningful clusters, classify variables (reduce/relate variables), principal components & classification analysis
• process analysis, quality control, multivariate statistical process control; understand critical process parameters which impact critical quality attributes
• design of experiments, power analysis, and interval estimation; experiment and discover; also use simulated data to execute virtual experiments
• tabulation options; everyone needs a summary table for their presentation to management

There are two modes of interaction with analytics: spreadsheet and workspace. For ad-hoc analysis that does not need to be duplicated, users can import data into a spreadsheet and interact with menus, variables, and rows of data. The workspace is a visual analytic workflow management tool and is recommended. This allows work to be saved and reused. No coding is needed to complete a workspace. And for the users who need to manage their code, the workspace has a "code node" which can execute C#, Python, or R code.

Data Profiling, Cleaning, Transformation

The Data Health Check node (data profiling) explores values, value ranges, discrete text labels, missing data, outliers, etc.. on every variable. The result of this analysis is a diagnostic report. This node can also be configured to automate and fix the data problems uncovered by the analyses. 

Additional options to transform and clean are available; remove duplicates, recode, rank, merge, process invariant variables, recode outliers, missing data imputation, recode missing data, subset, sample, etc.

Box-Cox is available to transform variables so that they have a distribution as close to normality as possible (Box and Cox, 1964). This allows the use of algorithms, like regression analysis, that only work with a normal distribution. 

Workspace Features

A workspace is a no-code tool that:

• documents the analytic steps
• imports excel, csv, fixed width (mainframe) data, and data from PI Servers
• embed sdata within the workspace as a lookup table; transform "m" to Monday for readability
• imports Spotfire SBDF data file and configures analytics (see options below)
• retrieves data from the database with ODBC driver and configure analytics (see options below)
• creates data mashup 
• creates visualizations
• formats output for reporting
• exports results to excel, csv, Spotfire SBDF, etc.
• writes results into a database; SQL Server, Oracle, Teradata, SQL Server PDW, PostgreSQL, DB2
• workspace calls another workspace

The workspace can also be extended with R, C#, or Python coding.

Visualizations

2D and 3D visualizations are available with the product; histogram, line, scatterplot, means with error, bag plots, quantile-quantile (beta, exponential, extreme, gamma, lognormal, normal, Rayleigh, Weibull), variability, contour, wafer, normal probability, etc.. 

Analytics 

• ANOVA MANOVA
• Calculators: Distributions, Pearson Product Moment Correlation Coefficient, Six Sigma 
• Canonical Analysis
• Classification Trees
• Cluster Analysis
• Correlation
• Correspondence Analysis
• Cox Proportional Hazards Models
• Descriptive Statistics
• Design of Experiments
• Discriminant Function Analysis
• Distribution Fitting
• Distributions & Simulation
• Dynamic Time Warping
• Extract, Transform, and Load (analytics are used to align time based data)
• Factor Analysis
• Fixed Nonlinear Regression
• General Discriminant Analysis 
• General Linear Models
• General Partial Least Squares Models
• General Regression Models
• Generalized Linear Nonlinear Models
• Log-Linear Analysis of Frequency Tables
• Multiple Regression
• Multidimensional Scaling
• Multivariate Statistical Process Control
• Nonlinear Estimation
• Nonparametric Statistics
• Power Analysis and Interval Estimation
• Principal Components & Classification Analysis
• Process Analysis
• Quality Control Charts
• Reliability and Item Analysis
• Stepwise Model Builder
• Structural Equation Modeling and Path Analysis
• Survival & Failure Time Analysis
• Time Series Forecasting
• T-Tests And Other Tests Of Group Differences
• Tabulate
• Variance Components & Mixed Model ANOVA ANCOVA
• Variance Estimation and Precision