Trying to determine when to use a logistic regression and how to interpret the coefficients? Frustrated by the technical writing in other books on the topic? Pampel's book offers readers the first "nuts and bolts" approach to doing logist
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 text presents an overview of the full range of logistic models, including binary, proportional, ordered, and categorical response regression procedures.
... Noel A. C. Cressie, Garrett M. Fitzmaurice, Harvey Goldstein, Iain M. Johnstone, Geert Molenberghs, David W. Scott, Adrian F. M. Smith, Ruey S. Tsay, Sanford Weisberg Editors Emeriti: Vic Barnett, J. Stuart Hunter, Joseph B. Kadane, ...
This book is about making machine learning models and their decisions interpretable.
Joseph M. Hilbe ... Beta Binomial f(y,y)=f(y|p)f(p) = [(m y)py(1-p)"-y] * pA-1(1-p)* – T T(A+B) T(m+1) py. A-1 (1-p)"-veh(A)P(B) T(y+1) T(m = y + 1) By integrating with respect to p, we can obtain the margin distribution of Y, ...
Informal and nontechnical, this book both explains the theory behind logistic regression, and looks at all the practical details involved in its implementation using SAS. Several real-world examples are included in full detail.
Here we find mathematical modeling, probability, and statistics. Here I will take you on a journey into the art and science of predictive modeling using logistic regression, inside-and-out.
... Indicators Sullivan/Feldman Exploratory Data Analysis Hartwig/Dearing Reliability and Validity Assessment Carmines/Zeller Analyzing Panel Data Markus Discriminant Analysis Klecka Log-Linear Models Knoke/Burke Interrupted Time Series ...
This book is part of the SAS Press program.
This text realistically deals with model uncertainty and its effects on inference to achieve "safe data mining".