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This book considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response.
This book considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response.
Through the use of specific examples to illustrate evaluation research goals and methods, this book provides readers with an overview of the science and politics of evaluation research. The Second Edition includes coverage of meta-analysis, selection models and instrumental variables.
As a formal matter, conventional regression analysis does nothing more than produce from a data set a collection of conditional means and conditional variances. This work identifies a wide variety of problems with regression analysis as it is commonly used and then provides a number of ways in which practice could be improved.
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