Understanding Log-linear Analysis With Ilog: An Interactive Approach
Whenever data are categorical and their frequencies can be arrayed in multidimensional tables, log-linear analysis is appropriate. Like analysis of variance and multiple regression for quantitative data, log-linear analysis lets users ask which main effects and interactions affect an outcome of interest. Until recently, however, log-linear analysis seemed difficult -- accessible only to the statistically motivated and savvy.
Designed for students and researchers who want to know more about this extension of the two-dimensional chi-square, this book introduces basic ideas in clear and straightforward prose and applies them to a core of example studies. ILOG -- a software program that runs on IBM compatible personal computers -- is included with this volume. This interactive program lets readers work through and explore examples provided throughout the book. Because ILOG is capable of serious log-linear analyses, readers gain not only understanding, but the means to put that understanding into practice as well.
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2 Basic Statistics for TwoDimensional Frequency Tables
3 Models for TwoDimensional Frequency Tables
4 Fitting Models for Multidimensional Tables
Specifying a Frequency Table
6 Analyzing Frequency Tables with ILOG
Enough Too Many None at All
Percentages Residuals and TwoWay Tables
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Understanding Log-Linear Analysis with ILOG: An Interactive Approach
Roger Bakeman,Byron F. Robinson
No preview available - 1994
ABCD adjusted residuals alligator survival study analysis of variance associated assuming Bakeman base model categorical chap chapter computed conditional probabilities contingency table Counts command critical value defined degrees of freedom deleted terms described descriptive statistics deterioration in fit digits displayed edit mode effect Equation expected frequencies expected values female infants fits the data fitting model frequency table gender smiling study hierarchical series highlight ILOG infants smiled interaction interpretation likelihood-ratio chi-square listing file log-linear analysis log-linear models male infants marginal model fits multiple regression n-way terms Natural logarithms null hypothesis null model number of cells number of smiles number of tallies Oba Oba observed outcome partial chi-square Pearson chi-square predictor variables raw residuals reﬂect represent row and column sample saturated model selected shown in Table sloth bear smiling behavior standardized residuals statistically significant structural zeros structure table subcommand subtables two-dimensional tables Wickens