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Statistical Analysis with Missing Data

Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current;

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Statistical Methods for Handling Incomplete Data

in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and;

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Missing Data

Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables;

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Missing Data

research, and reap the benefits in terms of improved accuracy and statistical power. Missing Data: Analysis and Design contains essential;

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Missing Data Analysis in Practice

Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for;

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Missing Data Analysis in Practice

Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for;

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Analysis of Incomplete Multivariate Data

routine basis. Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools;

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Analysis of Incomplete Multivariate Data

routine basis. Analysis of Incomplete Multivariate Data helps bridge the gap between theory and practice, making these missing-data tools;

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Handbook of Missing Data Methodology

Missing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have;

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Preventing and Treating Missing Data in Longitudinal Clinical Trials

Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have;

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Preventing and Treating Missing Data in Longitudinal Clinical Trials

Recent decades have brought advances in statistical theory for missing data, which, combined with advances in computing ability, have;

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Multiple Imputation of Missing Data in Practice

Multiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple;

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Statistics and Data Analysis for Nursing Research

The second edition of Statistics and Data Analysis for Nursing, uses a conversational style to teach students how to use statistical;

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Computational Reconstruction of Missing Data in Biological Research

follows. In Chapter 2, we introduce the statistical recovery of missing data features; in Chapter 3, we introduce the statistical recovery;

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Statistical Power Analysis with Missing Data

analysis of incomplete (missing) data are gaining more widespread applications. This volume brings statistical power and incomplete data together;

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Analyzing Longitudinal Clinical Trial Data: A Practical Guide

theory on analysis of longitudinal clinical trial data into routine practice.The book, with its example-oriented approach that includes numerous;

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Analyzing Longitudinal Clinical Trial Data: A Practical Guide

theory on analysis of longitudinal clinical trial data into routine practice.The book, with its example-oriented approach that includes numerous;

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Discovering Knowledge In Data

of missing data, and multivariate statistical analysis, Discovering Knowledge in Data, Second Edition remains the eminent reference on data mining;

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Analysis Of Longitudinal Data

The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis;

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Analysis of Survey Data

This book is concerned with statistical methods for the analysis of data collected from a survey. A survey could consist of data collected;

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Statistics And Data Analysis For Nursing Research

The second edition of Statistics and Data Analysis for Nursing, uses a conversational style to teach students how to use statistical;

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Statistical Foundations, Reasoning and Inference

of multivariate distributions, missing data analysis, causality as well as principles of experimental design. The textbook includes sufficient material;

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Advanced R Statistical Programming and Data Models

, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes;

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Analysis Of Longitudinal Data 2nd

The first edition of Analysis for Longitudinal Data has become a classic. Describing the statistical models and methods for the analysis;

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Analysis Of Questionnaire Data With R

disadvantages. Analysis of Questionnaire Data with R translates certain classic research questions into statistical formulations. As indicated in the;

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How to Manage, Analyze, and Interpret Survey Data

steps in a content analysis of qualitative data * How to recognize and deal with missing data and outliers for recoding * How to enter;

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Multiple Imputation in Practice

sets, incorporate complex sample design features, and be used for other statistical analyses framed as missing data problems. IVEware can be;

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