The IBM SPSS Statistics is a comprehensive system for analyzing data. SPSS Statistics can take data from almost any type of file and use them to generate tabulated reports, charts, and plots of distributions and trends, descriptive statistics, and complex statistical analyses. It makes statistical analysis more accessible for the beginner and more convenient for experienced users. Simple menus and dialog box selections make it possible to perform complex analyses without typing a single line of command syntax.
The IBM SPSS Statistics Base offers a wide range of statistical procedures for basic analyses and reports, including counts, crosstabs and descriptive statistics, OLAP Cubes and codebook reports. It also provides a variety of dimension reduction, classification and segmentation techniques such as factor analysis, cluster analysis, nearest neighbor analysis and discriminant function analysis includes the following key capabilities:
Linear models offer a variety of regression and advanced statistical procedures. They are
designed to fit the inherent characteristics of data describing complex relationships.
Nonlinear models provide the ability to apply more sophisticated models to data.
Simulation capabilities help analysts automatically model many possible outcomes when
inputs are uncertain, improving risk analysis and decision making.
Customized tables enable users to easily understand their data and quickly summarize
results in different styles.
The IBM SPSS Campus Wide license also includes the following modules:
Advanced Statistics focuses on techniques often used in experimental and biomedical
research. It includes procedures for general linear models (GLM), linear mixed models,
variance components analysis, loglinear analysis, ordinal regression, actuarial life tables,
Kaplan-Meier survival analysis, and basic and extended Cox regression.
Custom Tables creates a variety of presentation-quality tabular reports, including complex
stub-and-banner tables and displays of multiple response data.
Regression provides techniques for analyzing data that do not fit traditional linear statistical
models. It includes procedures for prohibit analysis, logistic regression, weight estimation,
two-stage least-squares regression, and general nonlinear regression.