Advanced Linear Nonlinear Models
- Generalized Linear Model (GLM) Overview
- Generalized Linear/Nonlinear Models (GLZ) Overview
- Stepwise Model Builder Overview
- General Regression Models (GRM) Overview
- Partial Least Squares (PLS) Overview
- Multivariate Statistical Process Control (MSPC) and Nonlinear Iterative Partial Least Squares (NIPALS) Overview
Statistica Multivariate Statistical Process Control (MSPC) and Nonlinear Iterative Partial Least Squares (NIPALS) are implementations of a number of techniques used in statistical multivariate data analysis known as Principal Component Analysis (PCA) and Partial Least Squares (PLS). MSPC also includes the application of these methods to industrial batch processing for quality control and process monitoring. In Statistica, PCA and PLS are implemented using the state-of-the-art algorithm known as NIPALS. - Variance Components Overview
- Survival Analysis Overview
- Cox Proportional Hazards Model Overview
The main characteristic of survival analysis that differentiates itself from other statistical or data-mining domains is that, methods in survival analysis are specifically designed to handle censored data. - Nonlinear Estimation Overview
- Fixed Nonlinear Regression Overview
- Log-Linear Analysis Overview
One basic and straightforward method for analyzing data is via crosstabulation. - Time Series Analysis Overview
In the following topics, we will first review techniques used to identify patterns in time series data (such as smoothing and curve fitting techniques and autocorrelations), then we will introduce a general class of models that can be used to represent time series data and generate predictions (autoregressive and moving average models). Finally, we will review some simple but commonly used modeling and forecasting techniques based on linear regression. For more information on these topics, click on the topic name below. - Structural Equation Modeling Overview
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