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Advanced Statistics For Health Research by Richard J Butler, ... - 9789811262302
Advanced Statistics For Health Research by Richard J Butler, ... - 9789811262302
Advanced Statistics For Health Research by Richard J Butler, ... - 9789811262302
Advanced Statistics For Health Research by Richard J Butler, ... - 9789811262302

Advanced Statistics For Health Research by Richard J Butler, ... - 9789811262302

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Advanced Statistics For Health ResearchAuthor(s): Richard J Butler, Matthew J Butler, Barbara L Wilson\nFormat: Hardback\nPublisher: World Scientific Publishing Co Pte Ltd, Singapore\nImprint: World Scientific Publishing Co Pte Ltd\nISBN-13: 9789811262302, 978-9811262302\nSynopsis\nAdvanced Statistics for Health Research provides a rigorous geometric understanding of models used in the analysis of health data, including linear and non-linear regression models, and supervised machine learning models. Models drawn from the health literature include: ordinary least squares, two-stage least squares, probits, logits, Cox regressions, duration modeling, quantile regression and random forest regression. Causal inference techniques from the health literature are presented including randomization, matching and propensity score matching, differences-in-differences, instrumental variables, regression discontinuity, and fixed effects analysis. Codes for the respective statistical techniques pres.

Advanced Statistics For Health Research by Richard J Butler, ... - 9789811262302

Advanced Statistics For Health ResearchAuthor(s): Richard J Butler, Matthew J Butler, Barbara L Wilson\nFormat: Hardback\nPublisher: World Scientific Publishing Co Pte Ltd, Singapore\nImprint: World Scientific Publishing Co Pte Ltd\nISBN-13: 9789811262302, 978-9811262302\nSynopsis\nAdvanced Statistics for Health Research provides a rigorous geometric understanding of models used in the analysis of health data, including linear and non-linear regression models, and supervised machine learning models. Models drawn from the health literature include: ordinary least squares, two-stage least squares, probits, logits, Cox regressions, duration modeling, quantile regression and random forest regression. Causal inference techniques from the health literature are presented including randomization, matching and propensity score matching, differences-in-differences, instrumental variables, regression discontinuity, and fixed effects analysis. Codes for the respective statistical techniques pres.

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