Moustaki irini (119 resultados)

Paperback. Condición: Very Good. 2. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

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Condición: Very Good. 2 Edition. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good. …

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Condición: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

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Condición: Good. Your purchase helps support Sri Lankan Children's Charity 'The Rainbow Centre'. Ex-library, so some stamps and wear, but in good overall condition. Our donations to The Rainbow Centre have helped provide an education and a safe haven to hundreds of children who live in appalling conditions.…

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paperback. Condición: New. In shrink wrap. Looks like an interesting title.

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PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

PAP. Condición: New. New Book. Shipped from UK. Established seller since 2000.

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Condición: As New. Unread book in perfect condition.

Condición: As New. Unread book in perfect condition.

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Hardback. Condición: New. Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related techniques for investigating dependency. This book: Provides a unified approach showing how such apparently diverse methods as Latent Class Analysis and Factor Analysis are actually members of the same family.Presents new material on ordered manifest variables, MCMC methods, non-linear models as well as a new chapter on related techniques for investigating dependency.Includes new sections on structural equation models (SEM) and Markov Chain Monte Carlo methods for parameter estimation, along with new illustrative examples.Looks at recent developments on goodness-of-fit test statistics and on non-linear models and models with mixed latent variables, both categorical and continuous. No prior acquaintance with latent variable modelling is pre-supposed but a broad understanding of statistical theory will make it easier to see the approach in its proper perspective. Applied statisticians, psychometricians, medical statisticians, biostatisticians, economists and social science researchers will benefit from this book.…

Paperback. Condición: New. Drawing on the authors' varied experiences researching and teaching in the field, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. With minimal mathematical and statistical knowledge required, this third edition expands its topics to include graphical modelling, models for longitudinal data, structural equation models for categorical variables, and latent class analysis for ordinal, nominal, and continuous variables. It also connects the topics to terminology and principles of machine learning, intended to help readers grasp the links between methods of multivariate analysis and advancements in the field of data science.After describing methods for the summarisation of data in the first part of the book, the authors consider regression analysis. This chapter provides a link between the two halves of the book, signalling the move from descriptive to inferential methods. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.Relying heavily on numerical examples from a range of disciplines, the authors provide insight into the purpose and working of the methods as well as the interpretation of results from analyses. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional practice, encouraging readers to explore new ground in social science research.FeaturesContains new chapters on undirected graphical modelling and models for longitudinal data, as well as new material such as K-means, cross-validation, structural equation models for categorical variables, latent class analysis for categorical, nominal and continuous variables, and treatment of missing data.Connects topics with terminology and principles of machine learning.Presents numerous examples of real-world applications, including voting preferences, social attitudes, educational assessment, recidivism, and health.Covers methods that summarise, describe, and explore multivariate datasets, including longitudinal data.Establishes a unified approach to latent variable modelling by providing detailed coverage of methods such as item response theory, factor analysis for continuous and categorical data, and models for categorical latent variables.Covers models for hierarchical and longitudinal data and their connections to latent variable models.Offers a full version of the data sets in the text or the book's website, with software code for implementing the analyses on the website.The book offers a balanced and accessible resource for students and researchers with limited mathematical and statistical training. It serves as a practical resource for courses in multivariate analysis and as a guide for applying these techniques in applied research. …

Condición: New.

Paperback. Condición: New. Drawing on the authors' varied experiences researching and teaching in the field, Analysis of Multivariate Social Science Data: Statistical Machine Learning Methods, Third Edition enables a basic understanding of how to use key multivariate methods in the social sciences. With minimal mathematical and statistical knowledge required, this third edition expands its topics to include graphical modelling, models for longitudinal data, structural equation models for categorical variables, and latent class analysis for ordinal, nominal, and continuous variables. It also connects the topics to terminology and principles of machine learning, intended to help readers grasp the links between methods of multivariate analysis and advancements in the field of data science.After describing methods for the summarisation of data in the first part of the book, the authors consider regression analysis. This chapter provides a link between the two halves of the book, signalling the move from descriptive to inferential methods. The remainder of the text deals with model-based methods that primarily make inferences about processes that generate data.Relying heavily on numerical examples from a range of disciplines, the authors provide insight into the purpose and working of the methods as well as the interpretation of results from analyses. Many of the same examples are used throughout to illustrate connections between the methods. In most chapters, the authors present suggestions for further work that go beyond conventional practice, encouraging readers to explore new ground in social science research.FeaturesContains new chapters on undirected graphical modelling and models for longitudinal data, as well as new material such as K-means, cross-validation, structural equation models for categorical variables, latent class analysis for categorical, nominal and continuous variables, and treatment of missing data.Connects topics with terminology and principles of machine learning.Presents numerous examples of real-world applications, including voting preferences, social attitudes, educational assessment, recidivism, and health.Covers methods that summarise, describe, and explore multivariate datasets, including longitudinal data.Establishes a unified approach to latent variable modelling by providing detailed coverage of methods such as item response theory, factor analysis for continuous and categorical data, and models for categorical latent variables.Covers models for hierarchical and longitudinal data and their connections to latent variable models.Offers a full version of the data sets in the text or the book's website, with software code for implementing the analyses on the website.The book offers a balanced and accessible resource for students and researchers with limited mathematical and statistical training. It serves as a practical resource for courses in multivariate analysis and as a guide for applying these techniques in applied research. …

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