The following e-learning materials are available free of charge. They provide helpful hints and guidance to navigate the software more efficiently.
Examples & Templates
Statistica ships with workspace examples & templates to help get started on a project. Examples contain data that connects with a workflow to complete various analytics; data cleaning to modeling building. Templates do not include example data.
To open an example start Statistica. Select Home menu ► pull down arrow on Open menu ► Open Examples menu. Browser to the Workspaces folder.
Open Basic_DM_Example to see a simple example. Select the Run All button to execute the workflow and explore the different steps. This executes model building for three different algorithms. The training and testing workflows will decide which model is the best. Delete the CreditScoring dataset and connect to your data.
Data Cleaning, Transformation, Feature Selection
Here is a short list of data related examples.
- Anonymize Variable Names
- Automated Data Cleaning
- Create Design Matrix
- Feature Selection with Spark
- Feature Selection with Statistica
- Lasso Regression
- Spotfire Data Science Import; call Spotfire Data Science workflow and return results to Statistica for metadata analysis
Here are the examples that build multiple models and select the best performing model.
- Advanced Comprehensive Classifiers; General Discriminant Analysis (GDA) Models vs C&RT Standard Trees vs Standard CHAID vs Exhaustive CHAID vs Neural Network
- Advanced Comprehensive Regression Models; General Regression Models (GRM) vs C&RT Standard Trees vs Standard CHAID vs Exhaustive CHAID vs Neural Network
- Customer Churn Analysis; C&RT Standard Trees vs Boosted Trees vs Generalized Linear/Nonlinear Models (GLZ)
- General Forecaster Neural Network Time Series; Simple Exponential Smoothing and Forecasting vs Exponential Smoothing and Forecasting vs ARIMA Models and Forecasting vs Neural Network
- Spark Model Comparison; Statistica Random Forest vs Spark Random Forest
- Spark Regresion; Linear Regression vs Logistic Regression vs Generalized Linear Model (GLM)
- Spark Trees; Decision Tree vs Random Forest
Horizontal or Vertical Solutions
- Credit Scoring WoE
- Customer Churn Analysis
- Consumer Preferences
- MNIST Digit Recognition with Microsoft CNTK
- MNIST Digit Recognition with H2O Deep Learning
- Non Linear Time Series for R - integration with R
- Optimization - Bayesian Optimization for Binary Response
- Optimization - Bayesian Optimization for Continuous Response
- Transportation / logistic delay prediction with H2O Deep Learning
- Website visitors association
- H2O DRF Distributed Random Forest
- H2O K-Means
- H2O PCA Principal Component Analysis
- General Classification Quick Linear Models
- General Quick Linear Models
- SPC Charts With Alarming
- SPC Charts
Statistica also ships with around 70 step-by-step examples detailed within the product help. Start Statistica and select F1. You can search by keyword Example or look at the table of contents. Select Statistics - Analyzing Data ► Data Mining or Statistics.
Each algorithm will have a set of examples.
Are you actively trialing Statistica? Check out the TIBCO Statistica Trial Toolkit for all the tools you'll need to ensure a successful trial and enjoyable trial experience!
Looking for videos to get started?
- Get to know the UI
- Build a workspace (workflow)
- Bring in your data
- Prepare and clean your data
- Other videos (Statistica Playlist)
Some helpful webcast videos (with some walk-throughs) include:
- What's New in 13.4
- What's New in 13.3
- Statistica 13.2 Demo: The Game-Changer of Advanced Analytics
- Statistica Enterprise Solutions Summary
- Overview Video Playlist
Electronic Statistics Textbook is recommended by Encyclopedia Britannica for its "Quality, Accuracy, Presentation, and Usability."This on-line textbook covers Statistical theory and how to interpret results.
Product Help Documents - Additional examples and information can be found in the help documents that come with your software and are available online.
Statistica Object Model Documentation - This was previously found in the Statistica Developer Network site and is installed with your software. Virtually every detail of data importing, visualization, cleaning, data analysis, predictive analytics, and reporting can be controlled via a scripting language. Macros can be recorded within Statistica and saved for re-running later. Business logic can be stored within Statistica Enterprise Manager. These objects can be called directly from many other applications or COM-compliant programming languages (e.g., C#, C++, Java, VB.NET).
Are you an existing user? Try to access TIBCO Support portal where you can find another useful information. From there, you can reach Knowledge base articles. Apart from solutions to frequent asked questions, you might find useful articles showing you tips and tricks with the software functionalities such as: How to embed document in Workspace, How to access help for specific dialog, How to connect to Aster database, What are the cases and variables, etc.
If you are not able to find information you are looking for, please use Answers Section, insert your questions and tag product as TIBCO Statistica.
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