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I took several statistics courses in college but I found that my education was very theory driven.

I was wondering if any of you had a text in Applied Statistics (at the graduate level) that you recommend or have had good experience with.

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    $\begingroup$ Graduate-level textbooks are usually rather specialized, with titles like Negative Binomial Regression, or Time Series Analysis by State-Space Methods. Can you be more specific about the area you're interested in, or are you looking for some kind of overview? $\endgroup$ – Scortchi - Reinstate Monica Jun 18 '14 at 14:15
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    $\begingroup$ It would help if you told us some more about your applications! $\endgroup$ – kjetil b halvorsen Jun 18 '14 at 14:15
  • $\begingroup$ I'm mostly interested in regression methods and some modeling. I encounter a lot of binomial RVs as well as random variables rough or unclear distributions. The applications are are rather wide so an overview would be 'ideal' but clearly isn't the most feasible of a request haha. $\endgroup$ – jameselmore Jun 18 '14 at 14:19

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Some very good books: "Statistics for Experimenters: Design, Innovation, and Discovery , 2nd Edition" by Box, Hunter & Hunter. This is formally an introductory text (more for chemistry & engineering people) but extremely good on the applied side.

"Data Analysis Using Regression and Multilevel/Hierarchical Models" by Andrew Gelman & Jennifer Hill. Very good on application of regression modelling.

"The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition" (Springer Series in Statistics) 2nd (2009) Corrected Edition by Hastie Trevor, Tibshirani Robert & Friedman Jerome. More theoretical than the two first in my list, but also extremely good on the whys and ifs of applications. -- PDF Released Version

"An Introduction to Statistical Learning" (Springer Series in Statistics) 6th (2015) by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani -- PDF Released Version

Working your way through these three books should give a very good basis for applications.

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Harrell (2001), Regression Modelling Strategies is distinguished by

  • covering modelling from start to finish—so data reduction, imputation of missing values, & model validation are among the topics included
  • an emphasis on explaining how to employ different methods at different stages
  • thoroughly worked-out examples (& S-Plus/R code) taking up much of the book
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In addition to those, Introductory Econometrics: A Modern Approach by Wooldrige has pretty much everything you could ever want to know about regression, at an advanced undergraduate level.

edit: if you're dealing with categorical outcomes, Hastie et al is indispensable. Also, Categorical Data Analysis by Agresti is a good classical approach, as opposed to Hastie et al's machine learning approach.

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    $\begingroup$ I do not think Wooldridge is particularly advanced. In my opinion a better reference would be Hayashi's Econometrics or even Wooldridge's second text, " Econometric Analysis of Cross Section and Panel Data". $\endgroup$ – JohnK Jun 18 '14 at 14:59
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    $\begingroup$ Using Hayashi for "applied statistics" is like using a flamethrower to light a candle. He asked for less theory, not more. Also, I think Wooldridge is conceptually sophisticated for an undergrad book even if it's not so technical. It's not like I recommended Stock & Watson. $\endgroup$ – shadowtalker Jun 18 '14 at 15:01
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    $\begingroup$ I do not agree but I do like the metaphore ;) $\endgroup$ – JohnK Jun 18 '14 at 15:03
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Bayesian Data Analysis third edition (2013) by Gelman et al. The level is mixed but the treatment I find so good that something valuable can be got from most chapters. If you're interested in principled application of methods I'd recommend this book.

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I've gotten a lot of use out of Sheskin's Handbook of Parametric and Nonparametric Statistical Procedures. It's a broad survey of hypothesis testing methods, with good introductions to the theory and tons of notes about the subtleties of each. You can see the TOC at the publisher's site (linked above).

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Regression Modeling Strategies by Frank Harrell, is a great book if you already know some basics. It is heavily focused on applications (lot's of examples with code), specifying models, diagnostic of models, dealing with common pitfalls and avoiding problematic methods.

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I used "Engineering Statistics" by Montgomery and Runger. It's pretty good (especially if you have a strong math background). I'd also highly recommend checking out CalTech's online Machine Learning course. It's great for an introduction to ML Concepts (if that's part of your data analysis). https://work.caltech.edu/telecourse.html.

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I wrote the book Nonlinear Regression Modeling for Engineering Applications: Modeling, Model Validation, and Enabling Design of Experiments, Wiley, New York, NY, September, 2016. ISBN 9781118597965, Rhinehart, R. R. because I sensed such a need. The book is 361 pages and has a companion web site with Excel/VBA open-code solutions for many of the techniques. Visit www.r3eda.com.

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The UW Stat PhD program's top-level regression methods sequence uses Wakefield's "Bayesian and Frequentist Regression Methods" which is a particularly good choice for folks like you who've seen lots of mathematical statistics. It gives a lot more perspective than most books on even the simplest applied methods since it leverages so much math.

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I used College Statistics Made Easy by Sean Connolly. It is aimed at a first / second course in statistics. The material very, very easy to follow. I tried a few books and none compare to this.

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  • $\begingroup$ Given that the asker's had a lot of theoretical stats, this probably isn't what they're going for. $\endgroup$ – Sheridan Grant Aug 14 at 21:45

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