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I'm a software engineer and I'm looking for recommendations for statistics books to further my knowledge in statistics. However, I'm not sure if I should phrase the question specifically about statistics books for software engineers, if I should group software engineers with other engineering disciplines, or if I should group books of interest to software engineers with computer scientists. My initial thought is to ask about books that would be of interest to computer scientists, software engineers, and information technologists rather than general engineering, but what type of question would best serve the community?

Also, I would appreciate it if no one else asked this question. I would like to use it to move my commitment to this exchange forward (I'm trying to free up a commitment slot so I can commit to my own proposal). Thanks.

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  • $\begingroup$ You might start by looking at other book questions, and then phrase you question such that it is sufficiently different. What is it about engineers that would make particular book recommendations relevant? $\endgroup$ – Shane Aug 13 '10 at 20:37
  • $\begingroup$ I'm not a statistician, but I have a book titled "Probably and Statistics for Engineers and Scientists" on my shelf and I took a course called "Engineering Statistics", which leads me to believe that engineers tend to use a particular subset of statistics. I also have a book called "Probability and Statistics for Computer Scientists" that covers topics I've seen a lot in software - Monte Carlo methods, Markov chains, Bayesian estimation, regression analysis, queuing systems. But I don't have enough of a background to determine how to make my question most beneficial to me and the community. $\endgroup$ – Thomas Owens Aug 13 '10 at 22:06
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I thought about it over dinner, and I think I can justify two questions. One for engineering-related statistics - topics related to cost, safety, product quality, experimental design, process control, system reliability - and another for computer software statistics - data mining, machine learning, Markov chains, Monte Carlo methods, and neural networks (apparently they are related to statistics? I've never done any work in that area...)

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