SPSS Advanced Statistics

Análisis de relaciones complejas con mayor precisión mediante potentes análisis univariados y multivariados

IBM SPSS Advanced Statistics crea análisis más precisos y conclusiones más fiables cuando se trabaja con relaciones complejas.

SPSS Advanced Statistics proporciona técnicas efectivas para resolver problemas reales en una gran variedad de disciplinas, incluidas la investigación médica, la fabricación, los productos farmacéuticos y los estudios de mercado. Escoja entre sofisticados procedimientos de análisis univariados y multivariados, incluidos:

• Modelos lineales generales (GLM) y de modelos mixtos
• Los modelos lineales generalizados (GENLIN), incluidos los modelos estadísticos más utilizados como la regresión lineal para respuestas distribuidas con normalidad, los modelos logísticos para datos binarios y los modelos loglineales para datos de recuento.
• Los procedimientos de las ecuaciones de estimación generalizadas (GEE) amplían los modelos lineales generalizados para acomodar datos longitudinales correlacionados y datos en clúster.
• Los modelos mixtos lineales generalizados (GLMM) para utilizar con datos jerárquicos y una amplia variedad de resultados, incluidos valores ordinales
• Sistemas operativos admitidos: Windows, Mac, Linux

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• General linear models (GLM) – Provide you with more flexibility to describe the relationship between a dependent variable and a set of independent variables. GLM give 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.
  
  

• Generalized Linear Mixed Models (GLMM) – Allow more accurate models when predicting nonlinear outcomes (for example, what product a customer is likely to buy) by taking into account hierarchical data structures (customer nested with an organization).
  
  Mixed-effects models provide a powerful and flexible tool for the analysis of hierarchical/nested data, whether that data is continuous or categorical. Some examples of analyses are: repeated measures data, longitudinal studies and nested designs. This procedure can produce a variety of outcome types.
  
  

• Linear mixed models, also known as hierarchical linear models (HLM)
  • Fixed effect analysis of variance (ANOVA), analysis of covariance (ANCOVA), multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA)
  • Random or mixed ANOVA and ANCOVA
  • Repeated measures ANOVA and MANOVA
  • Variance component estimation (VARCOMP)

  
  The linear mixed models procedure expands the general linear models used in the GLM procedure so that you can analyze data that exhibit correlation and non-constant variability. 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.
  
  

• Generalized linear models (GENLIN) GENLIN covers 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 models of multiway contingency tables (LOGLINEAR)
  
  

• Hierarchical loglinear models for multiway contingency tables (HILOLINEAR)
  
  

• Loglinear and logit models to count data by means of a generalized linear models approach (GENLOG)
  
  

• Survival analysis procedures:
  • Cox regression with time-dependent covariates
  • Kaplan-Meier
  • Life Tables

Operating system	Platform	Hardware
Windows	Microsoft® Windows XP (Professional, 32-bit) or Vista (Home, Business, 32- or 64-bit), Windows 7 (32- or 64-bit)* *Windows 2000 is not a supported platform.	• Intel® or AMD x86 processor running at 1GHz or higher • Memory: 1GB RAM or more recommended • Minimum free drive space: 800MB • DVD drive • XGA (1024x768) or higher-resolution monitor • For connecting with IBM SPSS Statistics Server, a network adapter running the TCP/IP network protocol
Mac	Apple Mac 10.6x (Snow Leopard) or 10.7 (Lion)	• Intel processor (32- and 64-bit) • Memory: 1GB RAM or more recommended • Minimum free drive space: 800MB • DVD drive • XGA (1024x768) or higher-resolution monitor
Linux	SPSS Statistics was tested on and is supported on only Red Hat Enterprise Linux 5 and 6 and Debian 6. We do not expect any problems with distributions derived from Red Hat and Debian, but we do not test or support them.	• Processor: Intel or AMD x86 processor running at 1GHz or higher • Memory: 1GB RAM or more recommended • Minimum free drive space: 800MB • DVD drive • XGA (1024x768) or a higher-resolution monitor