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Böcker av Ronald Christensen

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  • - The Theory of Linear Models
    av Ronald Christensen
    1 206 - 1 680,-

  • - Statistical Learning and Dependent Data
    av Ronald Christensen
    1 050 - 1 436,-

    This book introduces several topics related to linear model theory, including: multivariate linear models, discriminant analysis, principal components, factor analysis, time series in both the frequency and time domains, and spatial data analysis.

  • - Linear Modeling for Unbalanced Data, Second Edition
    av Ronald Christensen
    750,-

    This second edition focuses on modeling unbalanced data. It presents many new topics, including new chapters on logistic regression, log-linear models, and time-to-event data. It shows how to model main-effects and interactions and introduces nonparametric, lasso, and generalized additive regression models. The text carefully analyzes small unba

  • - The Theory of Linear Models
    av Ronald Christensen
    1 286,-

    This textbook provides a wide-ranging introduction to the use and theory of linear models for analyzing data. The authors emphasis is on providing a unified treatment of linear models, including analysis of variance models and regression models, based on projections, orthogonality, and other vector space ideas.

  • - Linear Modeling for Unbalanced Data, Second Edition
    av Ronald Christensen
    1 610,-

    This second edition focuses on modeling unbalanced data. It presents many new topics, including new chapters on logistic regression, log-linear models, and time-to-event data. It shows how to model main-effects and interactions and introduces nonparametric, lasso, and generalized additive regression models. The text carefully analyzes small unbalanced data by using tools that are easily scaled to big data. R, Minitab®, and SAS codes are available on the author¿s website.

  • - An Introduction for Scientists and Statisticians
    av Ronald Christensen
    1 120,-

    Emphasizing the use of WinBUGS and R to analyze real data, thsi book presents statistical tools to address scientific questions. It highlights foundational issues in statistics, the importance of making accurate predictions, and the need for scientists and statisticians to collaborate in analyzing data.

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