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Praktischer Leitfaden zur logistischen Regression, Taschenbuch von Hilbe, Joseph M., Marke...
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Artikelmerkmale
- Artikelzustand
- Book Title
- Practical Guide to Logistic Regression
- ISBN
- 9781498709576
- Publication Year
- 2015
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Publication Name
- Practical Guide to Logistic Regression
- Item Height
- 0.5in
- Item Length
- 8.5in
- Publisher
- CRC Press LLC
- Item Width
- 5.5in
- Item Weight
- 8.8 Oz
- Number of Pages
- 174 Pages
Über dieses Produkt
Product Information
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.
Product Identifiers
Publisher
CRC Press LLC
ISBN-10
1498709575
ISBN-13
9781498709576
eBay Product ID (ePID)
211790175
Product Key Features
Publication Name
Practical Guide to Logistic Regression
Format
Hardcover
Language
English
Publication Year
2015
Type
Textbook
Number of Pages
174 Pages
Dimensions
Item Length
8.5in
Item Height
0.5in
Item Width
5.5in
Item Weight
8.8 Oz
Additional Product Features
Lc Classification Number
Qa278.2.H534 2016
Reviews
"... this book is written in an exceptionally clear style ... An additional selling point of this text is that it introduces new R functions, which can be applied in one's own work, as well as equivalent SAS and Stata code. ... the emphasis on understanding logistic regression modelling rather than on the mechanistic application of techniques is one of the great strengths of the book. Anyone who reads this book will therefore feel that they have a good understanding of this subject ..." -- Significance Magazine , February 2016 "Big Data is ascendant, but even the biggest data often boil down to a decision between two categories: survive or die, purchase or don't purchase, click or don't click, fraudulent or honest, default or pay. Logistic regression is the classic workhorse for this 0/1 data, and Joseph Hilbe's new book presents a guide for the practitioner, chock full of useful R, Stata, and SAS code. Hilbe has worked with practitioners and aspiring practitioners in virtually every field that uses statistics, including for over a decade via his courses at Statistics.com. His new book is truly, in his own words, 'a tutorial between you and me.'" --Peter Bruce, Founder and President of the Institute for Statistics Education at Statistics.com, "Big Data is ascendant, but even the biggest data often boil down to a decision between two categories: survive or die, purchase or don't purchase, click or don't click, fraudulent or honest, default or pay. Logistic regression is the classic workhorse for this 0/1 data, and Joseph Hilbe's new book presents a guide for the practitioner, chock full of useful R, Stata, and SAS code. Hilbe has worked with practitioners and aspiring practitioners in virtually every field that uses statistics, including for over a decade via his courses at Statistics.com. His new book is truly, in his own words, 'a tutorial between you and me.'" --Peter Bruce, Founder and President of the Institute for Statistics Education at Statistics.com, "The book presents many worked examples, and the choice of interesting data sets all of which are available to the reader is one of its greatest assets. Data availability makes it easy for readers to reproduce the examples from the book, and example code is available for R, SAS and Stata: R code is incorporated into the book chapters, and the end of each chapter gives SAS and Stata code." --Ulrike Grömping, Beuth University of Applied Sciences Berlin, Journal of Statistical Software , July 2016 "... this book is written in an exceptionally clear style ... An additional selling point of this text is that it introduces new R functions, which can be applied in one's own work, as well as equivalent SAS and Stata code. ... the emphasis on understanding logistic regression modelling rather than on the mechanistic application of techniques is one of the great strengths of the book. Anyone who reads this book will therefore feel that they have a good understanding of this subject ..." -- Significance Magazine , February 2016 "Big Data is ascendant, but even the biggest data often boil down to a decision between two categories: survive or die, purchase or don't purchase, click or don't click, fraudulent or honest, default or pay. Logistic regression is the classic workhorse for this 0/1 data, and Joseph Hilbe's new book presents a guide for the practitioner, chock full of useful R, Stata, and SAS code. Hilbe has worked with practitioners and aspiring practitioners in virtually every field that uses statistics, including for over a decade via his courses at Statistics.com. His new book is truly, in his own words, 'a tutorial between you and me.'" --Peter Bruce, Founder and President of the Institute for Statistics Education at Statistics.com tanding of this subject ..." -- Significance Magazine , February 2016 "Big Data is ascendant, but even the biggest data often boil down to a decision between two categories: survive or die, purchase or don't purchase, click or don't click, fraudulent or honest, default or pay. Logistic regression is the classic workhorse for this 0/1 data, and Joseph Hilbe's new book presents a guide for the practitioner, chock full of useful R, Stata, and SAS code. Hilbe has worked with practitioners and aspiring practitioners in virtually every field that uses statistics, including for over a decade via his courses at Statistics.com. His new book is truly, in his own words, 'a tutorial between you and me.'" --Peter Bruce, Founder and President of the Institute for Statistics Education at Statistics.com, "The book presents many worked examples, and the choice of interesting data sets all of which are available to the reader is one of its greatest assets. Data availability makes it easy for readers to reproduce the examples from the book, and example code is available for R, SAS and Stata: R code is incorporated into the book chapters, and the end of each chapter gives SAS and Stata code." --Ulrike Grömping, Beuth University of Applied Sciences Berlin, Journal of Statistical Software , July 2016 "... this book is written in an exceptionally clear style ... An additional selling point of this text is that it introduces new R functions, which can be applied in one's own work, as well as equivalent SAS and Stata code. ... the emphasis on understanding logistic regression modelling rather than on the mechanistic application of techniques is one of the great strengths of the book. Anyone who reads this book will therefore feel that they have a good understanding of this subject ..." -- Significance Magazine , February 2016 "Big Data is ascendant, but even the biggest data often boil down to a decision between two categories: survive or die, purchase or don't purchase, click or don't click, fraudulent or honest, default or pay. Logistic regression is the classic workhorse for this 0/1 data, and Joseph Hilbe's new book presents a guide for the practitioner, chock full of useful R, Stata, and SAS code. Hilbe has worked with practitioners and aspiring practitioners in virtually every field that uses statistics, including for over a decade via his courses at Statistics.com. His new book is truly, in his own words, 'a tutorial between you and me.'" --Peter Bruce, Founder and President of the Institute for Statistics Education at Statistics.com, "The book presents many worked examples, and the choice of interesting data sets all of which are available to the reader is one of its greatest assets. Data availability makes it easy for readers to reproduce the examples from the book, and example code is available for R, SAS and Stata: R code is incorporated into the book chapters, and the end of each chapter gives SAS and Stata code." --Ulrike Grmping, Beuth University of Applied Sciences Berlin, Journal of Statistical Software , July 2016 "... this book is written in an exceptionally clear style ... An additional selling point of this text is that it introduces new R functions, which can be applied in one's own work, as well as equivalent SAS and Stata code. ... the emphasis on understanding logistic regression modelling rather than on the mechanistic application of techniques is one of the great strengths of the book. Anyone who reads this book will therefore feel that they have a good understanding of this subject ..." -- Significance Magazine , February 2016 "Big Data is ascendant, but even the biggest data often boil down to a decision between two categories: survive or die, purchase or don't purchase, click or don't click, fraudulent or honest, default or pay. Logistic regression is the classic workhorse for this 0/1 data, and Joseph Hilbe's new book presents a guide for the practitioner, chock full of useful R, Stata, and SAS code. Hilbe has worked with practitioners and aspiring practitioners in virtually every field that uses statistics, including for over a decade via his courses at Statistics.com. His new book is truly, in his own words, 'a tutorial between you and me.'" --Peter Bruce, Founder and President of the Institute for Statistics Education at Statistics.com
Table of Content
Statistical Models What Is a Statistical Model? Basics of Logistic Regression Modeling The Bernoulli Distribution Methods of Estimation SAS Code Stata Code Logistic Models: Single Predictor Models with a Binary Predictor Predictions, Probabilities, and Odds Ratios Basic Model Statistics Models with a Categorical Predictor Models with a Continuous Predictor Prediction SAS Code Stata Code Logistic Models: Multiple Predictors Selection and Interpretation of Predictors Statistics in a Logistic Model Information Criterion Tests The Model Fitting Process: Adjusting Standard Errors Risk Factors, Confounders, Effect Modifiers, and Interactions SAS Code Stata Code Testing and Fitting a Logistic Model Checking Logistic Model Fit Classification Statistics Hosmer-Lemeshow Statistic Models with Unbalanced Data and Perfect Prediction Exact Logistic Regression Modeling Table Data SAS Code Stata Code Reference Grouped Logistic Regression The Binomial Probability Distribution Function From Observation to Grouped Data Identifying and Adjusting for Extra Dispersion Modeling and Interpretation of Grouped Logistic Regression Beta-Binomial Regression SAS Code Stata Code References Bayesian Logistic Regression A Brief Overview of Bayesian Methodology Examples: Bayesian Logistic Regression SAS Code Stata Code Concluding Comments References
Copyright Date
2015
Topic
Probability & Statistics / General, Probability & Statistics / Multivariate Analysis, Probability & Statistics / Regression Analysis
Lccn
2015-012864
Dewey Decimal
519.5/36
Intended Audience
College Audience
Dewey Edition
23
Illustrated
Yes
Genre
Mathematics
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