Practical Guide to Logistic Regression

Joseph M. Hilbe

ISBN 10: 1138469432 / ISBN 13: 9781138469433

Editore: Chapman And Hall/CRC, 2018

Riguardo questo articolo

nach der Bestellung gedruckt Neuware - Printed after ordering - In Changepower! 37 Secrets to Habit Change Success, author Meg Selig guides readers through a step-by-step process that will help them achieve any habit change goal. Whether the reader wants to break a hurtful habit like smoking or overeating, or build a healthy habit like exercising or speaking up, Changepower! provides a springboard for change.Selig helps habit-changers move beyond willpower and succeed with changepower - the synergy that comes from combining willpower with other resources, useful outside supports, and wise strategies. In Changepower!, she shows habit-changers how to beef up both their willpower and their changepower to achieve habit change success. The key is revving up motivation. Selig reveals the most powerful motivators for change - pain motivators, the Eight Great Motivators, and even not-so-noble motivators.Research has shown that most changes take place in stages rather than overnight. Selig provides a step-by-step plan for each stage, leaving plenty of room for flexibility depending on each person's needs. First-person stories, pithy quotes, and how-to exercises provide inspiration, humor, and encouragement as readers embark on their habit change journeys.

Codice articolo 9781138469433

Dati bibliografici

• Titolo: Practical Guide to Logistic Regression
• Editore: Chapman And Hall/CRC
• Data di pubblicazione: 2018
• Lingua: Inglese
• ISBN 10: 1138469432
• ISBN 13: 9781138469433
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 222 L x 145 A x 13 P (millimetri) 222 millimetri di larghezza per 145 millimetri di altezza per 13 millimetri di profondità
• Peso: 360 grammi

Descrizione articolo

Riassunto

Practical Guide to Logistic Regression covers the key points of the basic logistic regression model and illustrates how to use it properly to model a binary response variable. This powerful methodology can be used to analyze data from various fields, including medical and health outcomes research, business analytics and data science, ecology, fisheries, astronomy, transportation, insurance, economics, recreation, and sports. By harnessing the capabilities of the logistic model, analysts can better understand their data, make appropriate predictions and classifications, and determine the odds of one value of a predictor compared to another.

Drawing on his many years of teaching logistic regression, using logistic-based models in research, and writing about the subject, Professor Hilbe focuses on the most important features of the logistic model. Serving as a guide between the author and readers, the book explains how to construct a logistic model, interpret coefficients and odds ratios, predict probabilities and their standard errors based on the model, and evaluate the model as to its fit. Using a variety of real data examples, mostly from health outcomes, the author offers a basic step-by-step guide to developing and interpreting observation and grouped logistic models as well as penalized and exact logistic regression. He also gives a step-by-step guide to modeling Bayesian logistic regression.

R statistical software is used throughout the book to display the statistical models while SAS and Stata codes for all examples are included at the end of each chapter. The example code can be adapted to readers own analyses. All the code is available on the author‘s website.

Informazioni sull?autore

Joseph M. Hilbe is a Solar System Ambassador with NASA's Jet Propulsion Laboratory at the California Institute of Technology, an adjunct professor of statistics at Arizona State University, and an emeritus professor at the University of Hawaii. He also teaches five web-based courses on statistical modeling at Statistics.com. He is president of the International Astrostatistics Association, elected fellow of the American Statistical Association, elected member of the International Statistical Institute, and full member of the American Astronomical Society.
Professor Hilbe is one of the world's leading statisticians in modeling discrete and longitudinal data. He has authored 16 books related to statistical modeling, including the best-selling Logistic Regression Models and Modeling Count Data.
During the late 1980s and 1990s, Professor Hilbe was a leading figure in the then new area of health outcomes research, serving as director of research at a national chain of hospitals and later CEO of a national health economics firm. He was also on the executive committee forming the Health Policy Statistics Section of the American Statistical Association.