1 item has been added to your cart.
Generalized Linear Models: An Applied Approach |
||||||||||||||||||||||||||||||||||||
 Click to enlarge See the back cover |
As an Amazon Associate, StataCorp earns a small referral credit from
qualifying purchases made from affiliate links on our site.
VitalSource eBook details
VitalSource eBooks are read using the Bookshelf How to access your eBook1) Visit Bookshelf online to sign in or create an account.
2) Once logged in, click redeem in the upper right corner. Enter your eBook code. Your eBook code will be in your order confirmation email under the eBook's title.
3) The eBook will be added to your library. You may then download Bookshelf on other devices and sync your library to view the eBook.
Bookshelf is available on the following:
Bookshelf allows you to have 2 computers and 2 mobile devices activated at any given time.
I was amazed at the VitalSource way of presenting the books....
Everything looks perfectly typeset, but yet you can "flip"
through the book in the same way you would "flip" through a very
long web page in your web browser. And best of all, whenever I
have my tablet with me, my books are just a swipe away.
— Michael Mitchell
Senior statistician at the USC Children's Data Network, author of four Stata Press books, and former UCLA statistical consultant who envisioned and designed the UCLA Statistical Consulting Resources website. Return policy for eBooksStata Press eBooks are nonreturnable and nonrefundable. ×eBook not available for this title |
|
||||||||||||||||||||||||||||||||||
Comment from the Stata technical groupGeneralized Linear Models: An Applied Approach, by John Hoffmann, presents the reader with an applied tour through the world of generalized linear models. Using real-world datasets, the author discusses a wide class of models, organizing the material according to what is to be assumed about the dependent variable, whether it be continuous, discrete, categorical, ordered, count, or time to failure. As such, this book is ideal for researchers wishing to apply these models without having to endure the detailed discussions of statistical theory or computational algorithms usually associated with GLIMs. The author focuses instead on the statistical reasoning behind the different models and in the interpretation of computer output, which for the most part is obtained using Stata. After a brief review of the simplest of GLIMs, the linear regression model, and a review of GLIM terminology, the text moves on to covering the various other models, including logistic/probit, ordered response, multinomial logit, count data, and survival or time to failure. A final chapter discussing advanced issues, such as sample selection and endogeneity, is also included. |
||||||||||||||||||||||||||||||||||||
Table of contentsView table of contents >> |
||||||||||||||||||||||||||||||||||||
)