Advanced Statistics 17.0
More Accurately Analyze Complex Relationships Using Powerful Univariate and Multivariate Analysis
Make your analysis more accurate and reach more dependable conclusions with procedures designed to fit the inherent characteristics of data describing complex relationships. SPSS Advanced Statistics (formerly called Advanced Models), provides a powerful set of sophisticated univariate and multivariate analysis techniques for real-world problems, such as:
- Medical research: Analyze patient survival rates
- Manufacturing: Assess production processes
- Pharmaceutical: Report test results to the FDA
- Market research: Determine product interest levels
Access a Range of Powerful Models
In addition to the general linear models (GLM) and mixed models procedures, SPSS Advanced Models now offers the generalized linear models (GENLIN) and generalized estimating equations (GEE) procedures.
- GENLIN include widely used statistical models, such as linear regression for normally distributed responses, logistic models for binary data, and loglinear models for count data. This procedure also offers many useful statistical models through its very general model formulation.
- GEE extend generalized linear models to accommodate correlated longitudinal data and clustered data
SPSS Advanced Statistics continues to offer the following procedures:
- General linear models (GLM) procedure:
Provides you with more flexibility to describe the relationship between a dependent variable and a set of independent variables
- Linear mixed models, also known as hierarchical linear models (HLM) procedure:
Expands the general linear models used in the GLM procedure so that you can analyze data that exhibit correlation and non-constant variability. Create more accurate models when working with nested-structure data, and model the mean, variance, and covariance in your data
SPSS Advanced Statistics is available in English, Japanese, French, German, Italian, Spanish, Chinese, Polish, Korean, and Russian.
What’s New in SPSS Advanced Statistics 17.0?
When you upgrade to SPSS Advanced Statistics 17.0, you gain two enhancements to two statistical procedures—generalized linear models (GENLIN) and generalized estimating equations (GEE). Together, they enable you to predict more types of outcomes than ever.
Specifically, with SPSS Advanced Statistics 17.0, you can predict:
- Ordinal outcomes such as customer satisfaction
- Outcomes that are a combination of discrete and continuous outcomes, such as claim amount, with a Tweedie distribution
More Statistics for Data Analysis
Using SPSS Advanced Statistics with SPSS Statistics Base gives you an even wider range of statistics so you can reach the most accurate response for specific data types. You can seamlessly work in the SPSS environment.
Highlights for SPSS Advanced Statistics
Generalized linear models (GENLIN): GENLIN cover not only widely used statistical models, such as linear regression for normally distributed responses, logistic models for binary data, and loglinear model for count data, but also many useful statistical models via its very general model formulation. The independence assumption, however, prohibits generalized linear models from being applied to correlated data.
Generalized estimating equations (GEE): GEE extend generalized linear models to accommodate correlated longitudinal data and clustered data.
General linear model (GLM): The GLM gives you flexible design and contrast options to estimate means and variances and to test and predict means. You can also mix and match categorical and continuous predictors to build models. Because GLM doesn't limit you to one data type, you have options that provide you with a wealth of model-building possibilities.
Linear mixed models, also known as hierarchical linear models (HLM): If you work with data that display correlation and non-constant variability, such as data that represent students nested within classrooms or consumers nested within families, use the linear mixed models procedure to model means, variances, and covariances in your data. Its flexibility means you can formulate dozens of models, including split-plot design, multi-level models with fixed-effects covariance, and randomized complete blocks design. You can also select from 11 non-spatial covariance types, including first-order ante-dependence, heterogeneous, and first-order autoregressive. You'll reach more accurate predictive models because it takes the hierarchical structure of your data into account.
You can also use linear mixed models if you're working with repeated measures data, including situations in which there are different numbers of repeated measurements, different intervals for different cases, or both. Unlike standard methods, linear mixed models use all your data and give you a more accurate analysis.
System requirements
For SPSS Statistics Base 17.0 for Windows
Operating System
MicrosoftWindows XP (32-bit versions) or Vista(32-bit or 64-bit versions)
Hardware
Intelor AMD x86 processor running at 1GHz or higher
Memory: 512MB RAM; 1GB recommended
Minimum free drive space: 450MB
CD-ROM drive
Super VGA (800x600) or a higher-resolution monitor
For connecting with SPSS Statistics Base Server, a network adapter running the TCP/IP network protocol
Software
Web browser: Internet Explorer 6 or above
For SPSS Statistics Base 17.0 for Mac OS X
Operating system: Apple Mac OS X 10.4 (Tiger) or Mac OSX 10.5 (Leopard)
Hardware
PowerPC or Intel processor
Memory: 512MB RAM; 1GB recommended
Minimum free drive space: 800MB
CD-ROM drive
Super VGA (800x600) or a higher-resolution monitor
Software
Safari 1.3.1, MozillaFirefox1.5 or higher, or Netscape7.2 or higher
Java Standard Edition 5.0 (J2SE 5.0)
SPSS Statistics Base 17.0 for Linux
Operating system*
Any Linux OS that meets the following requirements:
Kernel 2.6.9.42 or higher
glibc 2.3.4 or higher
XFree86-4.0 or higher
libstdc++5
Hardware
Processor: Intel or AMD x86 processor running at 1 GHz or higher
RAM: 512MB RAM; 1GB recommended
450 MB of available hard-disk space
CD-ROM drive
Super VGA (800x600) or a higher-resolution monitor
Software
Web browser: Konqueror 3.4.1 or higher, or Firefox 1.0.6 or higher, or Netscape 7.2 or higher
*Note: SPSS Statistics 17.0 was tested on and is supported only on Red HatEnterprise Linux 4 Desktop and Debian4.0
SPSS Statistics add-on modules
All SPSS Statistics 17.0 add-on modules require SPSS Statistics Base 17.0.
No other system requirements are necessary.
Amos 17.0
Operating system: Windows XP or Windows Vista
Hardware:
Memory: 256MB RAM minimum
125MB or more available hard-drive space
Web browser: Internet Explorer 6.0
SPSS Statistics Server 17.0
Operating system: Windows Server 2003 or Windows Server 2008 (32-bit or 64-bit) or Windows Server 2008 (32-bit or 64-bit); Sun Solaris (SPARC) 9 and later (64-bit only); IBMAIX5.3 and later; or Red Hat Enterprise Linux ES4 and later (64-bit); HP-UX 11i (64-bit Itanium)
Hardware
Minimum CPU: Two CPUs recommended, running at 1GHz or higher
Memory: 512MB RAM per expected concurrent user
Minimum free drive space: 300MB
Required temporary disk space: Calculate by multiplying 2.5 x number of users x expected size of dataset in megabytes
SPSS Statistics Adapter for SPSS Predictive Enterprise Services
Requires Statistics Base 17.0 and SPSS Predictive Enterprise Services
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