Free Kindle A Modern Approach To Regression With R (Springer Texts In Statistics) ebooks Download
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.
Series: Springer Texts in Statistics
Hardcover: 393 pages
Publisher: Springer; 2009 edition (March 11, 2009)
Language: English
ISBN-10: 0387096078
ISBN-13: 978-0387096070
Product Dimensions: 6.1 x 0.9 x 9.2 inches
Shipping Weight: 1.4 pounds (View shipping rates and policies)
Average Customer Review: 3.4 out of 5 stars See all reviews (16 customer reviews)
Best Sellers Rank: #81,993 in Books (See Top 100 in Books) #35 in Books > Business & Money > Economics > Econometrics #231 in Books > Textbooks > Science & Mathematics > Mathematics > Statistics #331 in Books > Science & Math > Mathematics > Applied > Probability & Statistics
The back cover of the book is, in my view, quite accurate. It covers a traditional topic, regression, from a modern perspective. By that it means "assumes interactive use of software" and is very focused on formulating and answering questions about data rather than prescribing a certain course of action. It is not a textbook on "modern" regression such as smoothing or the like, though there is a chapter on that, along with logistic regression, basic time series and mixed models. The fact that all the datasets and examples are freely available and that the author doesn't skip steps in his treatment is a HUGE benefit to instructors using this book for class or those engaged in self-study. I am giving this book a spin in my class in the fall....
Some have criticized the book title "with R" because the only R connection in the book is in the printouts copied in its pages. What is missed by these reviewers is that the book website, [...] contains not only all the data sets used in the book, but also all the R code used by chapter and page number. The book and its accompanying website are excellent.A Modern Approach to Regression with R
Well balanced between theoretical and practical approach. Assigments treated with care represent real world problems. The rigor is solid, might be a little difficult for someone introduced to computational statistical methods.
I have looked at many books on statistics and R, and it is hard to understand and follow most of them if you are not very experienced in statistics, but you want to do a thorough analysis. On the topic of regressions, I think this book beats all the others. You need some basic knowledge of R before you can understand the code provided on author's website, but the fact that the book shows only R output I think is a great choice, because after all, you don't need to know much of R to make regression analysis, but you do need to understand outputs very well.I think the fact that the book is thoroughly covering the theory and the fact that the author highlights important conclusions (like what it means to our model if that condition is not met etc.) makes it definitely a must have book for all the students who learn statistics.
This book provides a very clear introduction to regression, and includes recent developments in model diagnostics which are very valuable to those wishing to do regression well. I found the examples interesting and entertaining. All of the R code is provided on the author's website, and the book includes output from that code. One thing which I would really like to see in a subsequent edition (and I have shared this with the author) is an appendix summarizing all of the R functions used in the text, as it is a bit inconvenient to have to delve into the code when I just want to refresh my memory as to how to use one or more of those functions. Including the relevant code associated with the R output would be even better. These latter issues are the only reason I've given the book 4 stars rather than 5, as I consider it among the better statistics texts in my library. I look forward to the next edition.
I'm happy with this book. First thing first, its not an R book. Don't expect it to teach you R. But again, if you'r willing to go that extra mile, it will only enhance your R knowledge base. Talking about regression, it will most definitely get you started in the right direction. Author's website is of good aid. It will give you R, SAS and Stata code.
I will echo the sentiments from other review in that the book only uses R as a means to an end - it is assumed that you already know about R. As such, the statistics topics were relevant, but there are too many ideas being mashed together. For instance, the math used to derive certain variables was condensed, meaning the author assumed that you had a background in linear algebra. Finally, the author drew conclusions from the R summary output too quickly.
A lot of useful information, but not very clear on how to apply it. Much better than other textbooks, but still pretty murky on applications. Some of the shorthand and abbreviations were non-standard and confusing. Also, it was nice to have a website with additional resources, but the sample programming code wasn't very useful. The entirety of each chapter was put into its own file and it was difficult to decipher where to select the part needed to reproduce an example or a homework problem.
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