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Articles 1 - 30 of 38
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Mdc-R-Code 2016 Update, Joseph M. Hilbe
Mdc-R-Code 2016 Update, Joseph M. Hilbe
Joseph M Hilbe
Modeling Count Data: R code for download and use. Most recent update
Addition To Pglr Chap 6, Joseph M. Hilbe
Addition To Pglr Chap 6, Joseph M. Hilbe
Joseph M Hilbe
Addition to Chapter 6 in Practical Guide to Logistic Regression. Added section on Bayesian logistic regression using Stata.
Hilbe-Pglr-Errata-And-Comments, Joseph M. Hilbe
Hilbe-Pglr-Errata-And-Comments, Joseph M. Hilbe
Joseph M Hilbe
Errata and Comments for Practical Guide to Logistic Regression
Sas Code Only For Practical Guide To Logistic Regression, Joseph M. Hilbe
Sas Code Only For Practical Guide To Logistic Regression, Joseph M. Hilbe
Joseph M Hilbe
SAS code-only for Practical Guide to Logistic Regression
Sas Code & Output For Practical Guide To Logistic Regression, Joseph M. Hilbe
Sas Code & Output For Practical Guide To Logistic Regression, Joseph M. Hilbe
Joseph M Hilbe
SAS code for Practical Guide to Logistic Regression
Negative Binomial Regerssion, 2nd Ed, 2nd Print, Errata And Comments, Joseph Hilbe
Negative Binomial Regerssion, 2nd Ed, 2nd Print, Errata And Comments, Joseph Hilbe
Joseph M Hilbe
Errata and Comments for 2nd printing of NBR2, 2nd edition. Previous errata from first printing all corrected. Some added and new text as well.
Mdc-R-Code, Joseph M. Hilbe
Mdc-R-Code, Joseph M. Hilbe
Joseph M Hilbe
Modeling Count Data: R code in book provided for use
Mcd-Description, Joseph M. Hilbe
Mcd-Description, Joseph M. Hilbe
Joseph M Hilbe
Modeling Count Data - description of Data Files with examples using R, Stata and SAS
Mcd-Information-, Joseph M. Hilbe
Mcd-Information-, Joseph M. Hilbe
Joseph M Hilbe
Modeling Count Data - Information about book and resources
Mcd Information, Joseph M. Hilbe
Mcd Description Data Files: Stata-R-Sas-Excel, Joseph M. Hilbe
Mcd Description Data Files: Stata-R-Sas-Excel, Joseph M. Hilbe
Joseph M Hilbe
Modeling Count Data: Description of Data Files R, Stata, SAS examples
Mcd-Figures-Code, Joseph M. Hilbe
Mcd-Figures-Code, Joseph M. Hilbe
Joseph M Hilbe
Modeling Count Data, code for Figures in book - R and Stata
Mdc-Sas-Code, Joseph M. Hilbe
Mdc-Sas-Code, Joseph M. Hilbe
Joseph M Hilbe
Modeling Count Data, SAS files for download and use
Errata - Logistic Regression Models, Joseph Hilbe
Errata - Logistic Regression Models, Joseph Hilbe
Joseph M Hilbe
Errata for Logistic Regression Models, 4th Printing
Interpretation And Prediction Of A Logistic Model, Joseph M. Hilbe
Interpretation And Prediction Of A Logistic Model, Joseph M. Hilbe
Joseph M Hilbe
A basic overview of how to model and interpret a logistic regression model, as well as how to obtain the predicted probability or fit of the model and calculate its confidence intervals. R code used for all examples; some Stata is provided as a contrast.
Errata And Comments For Methods Of Statistical Model Estimation, Joseph M. Hilbe, Andew P. Robinson
Errata And Comments For Methods Of Statistical Model Estimation, Joseph M. Hilbe, Andew P. Robinson
Joseph M Hilbe
Errata and comments for Hilbe and Robinson's Methods of Statistical Model Estimation, Chapman & Hall/CRC (2013)
Beta Binomial Regression, Joseph M. Hilbe
Beta Binomial Regression, Joseph M. Hilbe
Joseph M Hilbe
Monograph on how to construct, interpret and evaluate beta, beta binomial, and zero inflated beta-binomial regression models. Stata and R code used for examples.
Errata And Comments For: Generalized Estimating Equations, 2nd Ed, Joseph M. Hilbe, James W. Hardin
Errata And Comments For: Generalized Estimating Equations, 2nd Ed, Joseph M. Hilbe, James W. Hardin
Joseph M Hilbe
Errata and Comments for Hardin & Hilbe, Generalized Estimating Equations, 2nd ed (published 10 Dec, 2012)
Nbr2 Errata And Comments, Joseph Hilbe
Nbr2 Errata And Comments, Joseph Hilbe
Joseph M Hilbe
Errata and Comments for Negative Binomial Regression, 2nd edition
Generalized Estimating Equations, Second Edition.Pdf, James W. Hardin, Joseph M.. Hilbe
Generalized Estimating Equations, Second Edition.Pdf, James W. Hardin, Joseph M.. Hilbe
Joseph M Hilbe
Generalized Estimating Equations, Second edition, updates the best-selling previous edition, which has been the standard text on the subject since it was published a decade ago. Combining theory and application, the text provides readers with a comprehensive discussion of GEE and related models. Numerous examples are employed throughout the text, along with the software code used to create, run, and evaluate the models being examined. Stata is used as the primary software for running and displaying modeling output; associated R code is also given to allow R users to replicate Stata examples. Specific examples of SAS usage are provided in …
International Astrostatistics Association, Joseph Hilbe
International Astrostatistics Association, Joseph Hilbe
Joseph M Hilbe
Overview of the history, purpose, Council and officers of the International Astrostatistics Association (IAA)
Glme3 Data And Adodo Files, Joseph Hilbe
Glme3 Data And Adodo Files, Joseph Hilbe
Joseph M Hilbe
A listing of Data Sets and Stata software commands and do files in GLME3 book
Risk, Odds, And Their Ratios, Joseph Hilbe
Risk, Odds, And Their Ratios, Joseph Hilbe
Joseph M Hilbe
A brief monograph explaining the meaning of the terms, risk, risk ratio, odds, and odds ratio and how to calculate each, together with standard errors and confidence intervals. Stata code is provided showing how all of the terms can be calculated by hand, as well as by using logistic and Poisson models.
Negative Binomial Regression Extensions, Joseph Hilbe
Negative Binomial Regression Extensions, Joseph Hilbe
Joseph M Hilbe
Negative Binomial Regression Extensions is an e-book extension of Negative Binomial Regression, 2nd edition, with added R and Stata code, and SAS macros all related to count models.
Suppliment To Logistic Regression Models, Joseph Hilbe
Suppliment To Logistic Regression Models, Joseph Hilbe
Joseph M Hilbe
No abstract provided.
Basic R Matrix Operations, Joseph Hilbe
Using R To Create Synthetic Discrete Response Regression Models, Joseph Hilbe
Using R To Create Synthetic Discrete Response Regression Models, Joseph Hilbe
Joseph M Hilbe
The creation of synthetic models allows a researcher to better understand models as well as the bias that can occur when the assumptions upon which a model is based is violated. This article provides R code that can be used or amended to create a variety of discrete response regression models.
Errata Negative Binomial Regression 1st Edition 1st Print, Joseph Hilbe
Errata Negative Binomial Regression 1st Edition 1st Print, Joseph Hilbe
Joseph M Hilbe
Errata for the first edition and printing of Negative Binomal Regression, August 2007. Many of the items listed here were corrected in the 2008 second printing.
Creation Of Synthetic Discrete Response Regression Models, Joseph Hilbe
Creation Of Synthetic Discrete Response Regression Models, Joseph Hilbe
Joseph M Hilbe
The development and use of synthetic regression models has proven to assist statisticians in better understanding bias in data, as well as how to best interpret various statistics associated with a modeling situation. In this article I present code that can be easily amended for the creation of synthetic binomial, count, and categorical response models. Parameters may be assigned to any number of predictors (which are shown as continuous, binary, or categorical), negative binomial heterogeneity parameters may be assigned, and the number of levels or cut points and values may be specified for ordered and unordered categorical response models. I …
Modeling Future Record Performances In Athletics, Joseph Hilbe
Modeling Future Record Performances In Athletics, Joseph Hilbe
Joseph M Hilbe
No abstract provided.