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Home > Mathematics and Science books > Mathematics > Probability and statistics > Ciencia de Datos Con Variables Categóricas a Través de IBM SPSS Statistics
Ciencia de Datos Con Variables Categóricas a Través de IBM SPSS Statistics

Ciencia de Datos Con Variables Categóricas a Través de IBM SPSS Statistics


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About the Book

Las técnicas de ciencia de datos tienen como finalidad esencial el descubrimiento automático del conocimiento contenido en la información almacenada de modo ordenado en grandes bases de datos. La ciencia de datos constituye un proceso de descubrimiento de relaciones, patrones, asociaciones, segmentos, clasificaciones y tendencias al examinar cantidades de información de gran tamaño. La informatización de las organizaciones y la aparición de aplicaciones de software operacionales sobre el sistema de información hace que sea posible un análisis potente y ordenado de los datos de acuerdo a los métodos estadísticos univariantes y multivariantes. Estos métodos, hasta ahora difíciles de utilizar por su complejidad matemática, se implementan ahora ordenadamente sobre software de minería de datos para dotar a las organizaciones de nuevas prestaciones de los sistemas de información derivadas de la inteligencia de datos. Es habitual clasificar las técnicas de ciencia de datos en técnicas predictivas (técnicas de aprendizaje supervisado), en las que las variables pueden clasificarse inicialmente en dependientes e independientes (similares a las técnicas del análisis de la dependencia o métodos explicativos del análisis multivariante) y técnicas descriptivas (técnicas de aprendizaje no supervisado), en las que todas las variables tienen inicialmente el mismo estatus (similares a las técnicas del análisis de la interdependencia o métodos descriptivos del análisis multivariante). En este libro se tratan técnicas de análisis supervisado y no supervisado cuta característica fundamental es que involucran variables categóricas. El texto comienza tratando las distribuciones de variables categóricas a través de las tablas de contingencia, contrastes de independencia y medidas de asociación. A continuación se presentan las técnicas de reducción de la dimensión con variables cualitativas entre las que destacan las técnicas de escalamiento óptimo que incluyen componentes principales con variables categóricas, análisis no lineal de correlación canónica y regresión categórica. En los capítulos siguientes se aborda el análisis de correspondencias simples y múltiples y los modelos logartmo lineales. También se dedica una parte importante del contenido al análisis conjunto y a las técnicas de escalamiento multidimensional no métrico. Finalmente se profundiza e los árboles de decisión. Las técnicas se ilustran con ejemplos prácticos y ejercicios resueltos con el software IBM SPSS STATISTICS.


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Product Details
  • ISBN-13: 9798344665412
  • Publisher: Independently Published
  • Publisher Imprint: Independently Published
  • Height: 254 mm
  • No of Pages: 332
  • Spine Width: 18 mm
  • Width: 178 mm
  • ISBN-10: 8344665418
  • Publisher Date: 27 Oct 2024
  • Binding: Paperback
  • Language: Spanish
  • Returnable: N
  • Weight: 630 gr


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