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(This article was first published on <strong><a href="http://shape-of-code.coding-guidelines.com/2018/11/08/practical-statistics-books-for-software-engineers/"> The Shape of Code » R</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<p>So you have read my (draft) book on <a rel="nofollow" href="http://www.knosof.co.uk/ESEUR/" target="_blank">evidence-based software engineering</a> and want to learn more about the statistical techniques used, but are not interested lots of detailed mathematics. What books do I suggest?</p>
<p>All the following books are sitting on the shelf next to where I write (not that they get read that much these days).</p>
<p>Before I took the training wheels off my R usage, my general go to book was (I still look at it from time to time): “The R Book” by Crawley, second edition; “R in Action” by Kabacoff is a good general read.</p>
<p>In alphabetical subject order:</p>
<p>Categorical data: “Categorical Data Analysis” by Agresti, the third edition is a weighty tomb (in content and heaviness). Plenty of maths+example; more of a reference.</p>
<p>Compositional data: “Analyzing compositional data with R” by van den Boogaart and Tolosana-Delgado, is more or less the only book of its kind. Thankfully, it is quite good.</p>
<p>Count data: “Modeling count data” by Hilbe, may be more than you want to know about count data. Readable.</p>
<p>Circular data: “Circular statistics in R” by Pewsey, Neuhauser and Ruxton, is the only non-pure theory book available. The material seems to be there, but is brief.</p>
<p>Experiments: “Design and analysis of experiments” by Montgomery.</p>
<p>General: “Applied linear statistical models” by Kutner, Nachtsheim, Neter and Li, covers a wide range of topics (including experiments) using a basic level of mathematics.</p>
<p>Mixed-effects models: “Mixed-effects models in S and S-plus” by Pinheiro and Bates, is probably the book I prefer; “Mixed effects models and extensions in ecology with R” by Zuur, Ieno, Walker, Saveliev and Smith, is another view on an involved topic (plus lots of ecological examples).</p>
<p>Modeling: “Statistical rethinking” by McElreath, is full of interesting modeling ideas, using R and <a rel="nofollow" href="https://github.com/stan-dev/rstan/wiki/RStan-Getting-Started" target="_blank">Stan</a>. I wish I had some data to try out some of these ideas.</p>
<p>Regression analysis: “Applied Regression Analysis and Generalized Linear Models” by Fox, now in its third edition (I also have the second edition). I found this the most useful book, of those available, for a more detailed discussion of regression analysis. Some people like “Regression modeling strategies” by Harrell, but this does not appeal to me.</p>
<p>Survival analysis: “Introducing survival and event history analysis” by Mills, is a readable introduction covering everything; “Survival analysis” by Kleinbaum and Klein, is full of insights but more of a book to dip into.</p>
<p>Time series: The two ok books are: “Time series analysis and its application: with R examples” by Shumway and Stoffler, contains more theory, while “Time series analysis: with applications in R” by Cryer and Chan, contains more R code.</p>
<p>There are lots of other R/statistics books on my shelves (just found out I have 31 of Springer’s R books), some ok, some not so. I have a few ‘programming in R’ style books; if you are a software developer, R the language is trivial to learn (its library is another matter).</p>
<p>Suggestions for books covering topics I have missed welcome, or your own preferences (as a software developer).</p>
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