Résumé :
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There are many books that are excellent sources of knowledge about individual statistical tools (survival models, general linear models, etc.), but the art of data analysis is about choosing and using multiple tools. In the words of Chatfield "...students typically know the technical details of regression for example, but not necessarily when and how to apply it. This argues the need for a better balance in the literature and in statistical teaching between techniques and problem solving strategies." Whether analysing risk factors, adjusting for biases in observational studies, or developing predictive models, there are common problems that few regression texts address. For example, there are missing data in the majority of datasets one is likely to encounter (other than those used in textbooks!) but most regression texts do not include methods for dealing with such data effectively, and texts on missing data do not cover regression modelling. A text for graduate students with a background in ordinary multiple regression and algebra, presenting full case studies of nontrivial datasets to illustrate regression modelling strategy. Focuses on the more popular methods, such as the binary logistic models and the linear regression model based on ordinary least squares. DLC: Regression analysis.
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