The American Statistician
Book Recommendations:
Recommended by The American Statistician
“The author states that this book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. The primary focus is on examining statistical and graphical methods for assessing whether or not the model upon which one desires to draw inferences is valid. ... the examples…will have appeal to the students due to the variety of the techniques motivated by the datasets. The author has included numerous graphs and descriptions with associated flow charts to assist the student in ’visualizing’ the process one should take when modeling data using regression models. I found that the book was …very readable and that the graphics were …useful in the analysis of the problem under consideration. The book is also the ’right size’ with enough but not too much content. Personally, I was pleased not to see the voluminous R code that ’litters’ many of the books that are ‘with R.’ I was also pleased that some of the characteristic R output has been minimized and reformatted to improve the appearance of the text.…One of the aspects I found most appealing is that which is not found in the book. The supplementary material given on the author’s webpage is potentially very useful. The R code that was used to create the graphs and output in the book is provided in a separate document. This supplement will be very useful to the student who is learning R. In addition, there are similar documents that use SAS and STATA. I have found that having code to address a specific statistical problem is a very effective way for a student to learn a statistical software package. The author’s supplementary material using all three packages will provide an effective means for a student to learn multiple software packages without having to spend valuable classroom time and instructor supervision.” (from Amazon)
by Simon Sheather·You?
by Simon Sheather·You?
This book focuses on tools and techniques for building regression models using real-world data and assessing their validity. A key theme throughout the book is that it makes sense to base inferences or conclusions only on valid models. Plots are shown to be an important tool for both building regression models and assessing their validity. We shall see that deciding what to plot and how each plot should be interpreted will be a major challenge. In order to overcome this challenge we shall need to understand the mathematical properties of the fitted regression models and associated diagnostic procedures. As such this will be an area of focus throughout the book. In particular, we shall carefully study the properties of resi- als in order to understand when patterns in residual plots provide direct information about model misspecification and when they do not. The regression output and plots that appear throughout the book have been gen- ated using R. The output from R that appears in this book has been edited in minor ways. On the book web site you will find the R code used in each example in the text.
Recommended by The American Statistician
“Bayesian Methods covers a broad yet essential scope of topics necessary for one to understand and conduct applied Bayesian analysis. The numerous social science examples should resonate with the target audience, and the availability of the code and data in an R package, BaM, further enhances the appeal of the book.” (from Amazon)
An Update of the Most Popular Graduate-Level Introductions to Bayesian Statistics for Social Scientists Now that Bayesian modeling has become standard, MCMC is well understood and trusted, and computing power continues to increase, Bayesian Methods: A Social and Behavioral Sciences Approach, Third Edition focuses more on implementation details of the procedures and less on justifying procedures. The expanded examples reflect this updated approach. New to the Third Edition A chapter on Bayesian decision theory, covering Bayesian and frequentist decision theory as well as the connection of empirical Bayes with James–Stein estimationA chapter on the practical implementation of MCMC methods using the BUGS softwareGreatly expanded chapter on hierarchical models that shows how this area is well suited to the Bayesian paradigmMany new applications from a variety of social science disciplines Double the number of exercises, with 20 now in each chapter Updated BaM package in R, including new datasets, code, and procedures for calling BUGS packages from RThis bestselling, highly praised text continues to be suitable for a range of courses, including an introductory course or a computing-centered course. It shows students in the social and behavioral sciences how to use Bayesian methods in practice, preparing them for sophisticated, real-world work in the field.