SPSS Advanced Statistics

Более точный анализ сложных взаимосвязей с помощью мощного инструмента одномерного и многомерного анализа

Программная платформа IBM SPSS Advanced Statistics позволяет более точно выполнить анализ и сделать выводы более достоверными при работе с комплексными взаимосвязями.

Программная платформа SPSS Advanced Statistics предоставляет мощные инструменты для решения практических задач в различных областях, включая медицинские исследования, промышленное производство, фармацевтику и маркетинговые исследования. Выберите нужную методику из списка процедур одномерного и многомерного анализа, включающего:

• Общие линейные модели (GLM) и смешанные модели
• Обобщенные линейные модели (GENLIN), включая широко применяемые статистические модели, такие как линейная регрессия для нормально распределённых ответов, логистические модели для двоичных данных, и логарифмические линейные модели для расчетных данных.
• Обобщенные процедуры оценочных уравнений (GEE) расширяют сферу применения обобщенных линейных моделей за счет коррелированных данных продольного анализа и кластеризованных данных.
• Обобщенные линейные смешанные модели (GLMM), используемые с иерархическими данными и большим набором результатов, включая порядковые значения
• Поддерживаемые операционные системы: Windows, Mac, Linux.

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Приобретение SPSS Advanced Statistics

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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