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Introduction -- Contingency tables -- Generalized linear models -- Logistic regression -- Building and applying logistic regression models -- Multicategory logit models -- Loglinear models for contingency tables -- Models for matched pairs -- Modeling correlated, clustered responses -- Random effects : generalized linear mixed models -- A historical tour of categorical data analysis -- Appendix A. Software for categorical data analysis -- Appendix B. Chi-squared distribution values.

Vol. 1: Socio-economic and demographic trends / ed. by K. A. Twum-Bah ... . - 2005. - XIV,237 S. : zahlr. graph. Darst., zahlr. Tab., Lit

Missing data have long plagued those conducting applied research in the social, behavioral, and health sciences. Good missing data analysis solutions are available, but practical information about implementation of these solutions has been lacking. The objective of Missing Data: Analysis and Design is to enable investigators who are non-statisticians to implement modern missing data procedures properly in their research, and reap the benefits in terms of improved accuracy and statistical power. Missing Data: Analysis and Design contains essential information for both beginners and advanced r

1 Descriptive Statistical Techniques -- 2 Environmetric Methods of Nonstationary Time-Series Analysis: Univaraute Methods -- 3 Regression and Correlation -- 4 Factor and Correlation Analysis of Multivariate Environmental Data -- 5 Errors and Detection Limits -- 6 Visual Representation of Data Including Graphical Exploratory Data Analysis -- 7 Quality Assurance for Environmental Assessment Activities.