Utvidet returrett til 31. januar 2025

Bøker av Ronald (University of New Mexico Christensen

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  • - Linear Modeling for Unbalanced Data, Second Edition
    av Ronald (University of New Mexico Christensen
    660,-

    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

  • - An Introduction for Scientists and Statisticians, Second Edition
    av Ronald (University of New Mexico Christensen
    889,-

    This textbook on Bayesian Statistics can be used to teach an upper division statistics course, a master's level course, a Ph.D. level course or used independently by researchers. It emphasizes the art of data modeling and analysis over technicalities and is rich with technical demonstrations available to Ph.D. students who need them and to others who are interested in them. The new edition features new material and new exercises and shifts the software focus from WinBUGS to OpenBUGS.

  • - Linear Modeling for Unbalanced Data, Second Edition
    av Ronald (University of New Mexico Christensen
    1 744,-

    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 (University of New Mexico Christensen
    1 228,-

    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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