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[null,"1.","Record Nr.",null,"UNINA9910462543003321"]
[null,null,"Autore",null,"Ali Jamal (Jamal M.)"]
[null,null,"Titolo",null,"Black and green [[electronic resource] ] : black insights for the green movement / / Jamal Ali"]
[null,null,"Pubbl/distr/stampa",null,"Lanham, MD, : Hamilton Books, c2009"]
[null,null,"ISBN",null,"9786613912121"]
[{"$nulls":4},"1-283-59967-8"]
[{"$nulls":4},"0-7618-4723-5"]
[null,null,"Descrizione fisica",null,"1 online resource (90 p.)"]
[null,null,"Disciplina",null,"333.7208996073"]
[null,null,"Soggetti",null,"African Americans - Social life and customs"]
[{"$nulls":4},"Environmental responsibility - United States"]
[{"$nulls":4},"Sustainable living - United States"]
[{"$nulls":4},"Electronic books."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Description based upon print version of record."]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Cover; Title Page; Copyright Page; Dedication Page; Table of Contents; Preface; Acknowledgements; Introduction: Reuse is not New to Black People; Chapter 1: Going Green Starts at home; Chapter 2: Rediscovering your Neighborhood; Chapter 3: Food and Its Environmental Impact; Chapter 4: The Electric Car is here; Chapter 5: Invoking Green in Education; Chapter 6: Cashing in on Green; Chapter 7: Black Institutions that should be Green; Chapter 8: Bury me Green; Chapter 9: The Future of Green; End Notes; Resource Guide; Index"]
[null,null,null,"Sommario/riassunto","This book is a call to action for the Black community to join the green movement. The book offers insights, ideas, and strategies that demonstrate how Black people can benefit from and fuel the go-green effort."]
[null,"2.","Record Nr.",null,"UNINA9910711597003321"]
[null,null,"Autore",null,"Meinzer Oscar Edward <1876-1948, >"]
[null,null,"Titolo",null,"Outline of methods for estimating ground-water supplies / / by Oscar Edward Meinzer"]
[null,null,"Pubbl/distr/stampa",null,"[Washington, D.C.] : , : United States Department of the Interior, Geological Survey, , 1932"]
[{"$nulls":4},"Washington : , : United States Government Printing Office"]
[null,null,"Descrizione fisica",null,"1 online resource (iii, 144 pages)"]
[null,null,"Collana",null,"Geological Survey water-supply paper ; ; 638-C"]
[{"$nulls":4},"Contributions to the hydrology of the United States, 1931"]
[null,null,"Soggetti",null,"Groundwater - United States"]
[{"$nulls":4},"Water-supply - United States"]
[{"$nulls":4},"Groundwater"]
[{"$nulls":4},"Water-supply"]
[{"$nulls":4},"United States"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references."]
[null,"3.","Record Nr.",null,"UNINA9910437861803321"]
[null,null,"Autore",null,"Kuhn Max"]
[null,null,"Titolo",null,"Applied Predictive Modeling / / by Max Kuhn, Kjell Johnson"]
[null,null,"Pubbl/distr/stampa",null,"New York, NY : , : Springer New York : , : Imprint : Springer, , 2013"]
[null,null,"ISBN",null,"1-4614-6849-3"]
[null,null,"Edizione",null,"[1st ed. 2013.]"]
[null,null,"Descrizione fisica",null,"1 online resource (XIII, 600 p. 203 illus., 153 illus. in color.)"]
[null,null,"Disciplina",null,"519.5"]
[null,null,"Soggetti",null,"Statistics"]
[{"$nulls":4},"Statistics for Life Sciences, Medicine, Health Sciences"]
[{"$nulls":4},"Statistics and Computing/Statistics Programs"]
[{"$nulls":4},"Statistics, general"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Bibliographic Level Mode of Issuance: Monograph"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references (pages 569-587) and index."]
[null,null,"Nota di contenuto",null,"General Strategies -- Regression Models -- Classification Models -- Other Considerations -- Appendix -- References -- Indices."]
[null,null,null,"Sommario/riassunto","This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics. Dr. Kuhn is a Director of Non-Clinical Statistics at Pfizer Global R&D in Groton Connecticut. He has been applying predictive models in the pharmaceutical and diagnostic industries for over 15 years and is the author of a number of R packages. Dr. Johnson has more than a decade of statistical consulting and predictive modeling experience in pharmaceutical research and development. He is a co-founder of Arbor Analytics, a firm specializing in predictive modeling and is a former Director of Statistics at Pfizer Global R&D. His scholarly work centers on the application and development of statistical methodology and learning algorithms. Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression "]
[{"$nulls":4},"and classification techniques, always with an emphasis on illustrating and solving real data problems. Addressing practical concerns extends beyond model fitting to topics such as handling class imbalance, selecting predictors, and pinpointing causes of poor model performance—all of which are problems that occur frequently in practice. The text illustrates all parts of the modeling process through many hands-on, real-life examples. And every chapter contains extensive R code for each step of the process. The data sets and corresponding code are available in the book’s companion AppliedPredictiveModeling R package, which is freely available on the CRAN archive. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. Readers and students interested in implementing the methods should have some basic knowledge of R. And a handful of the more advanced topics require some mathematical knowledge. ."]