Publications by John Mount
Worry about correctness and repeatability, not p-values
In data science work you often run into cryptic sentences like the following: Age adjusted death rates per 10,000 person years across incremental thirds of muscular strength were 38.9, 25.9, and 26.6 for all causes; 12.1, 7.6, and 6.6 for cardiovascular disease; and 6.1, 4.9, and 4.2 for cancer (all P < 0.01 for linear trend). (From “Associati...
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Prefer = for assignment in R
We share our opinion that = should be preferred to the more standard <- for assignment in R. This is from a draft of the appendix of our upcoming book. This has the risk of becoming an R version of Javascript’s semicolon controversy, but here you have it. R has five common assignment operators: “=“, “<-“, “->“, “<<-” and “-...
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A pathological glm() problem that doesn’t issue a warning
I know I have already written a lot about technicalities in logistic regression (see for example: How robust is logistic regression? and Newton-Raphson can compute an average). But I just ran into a simple case where R‘s glm() implementation of logistic regression seems to fail without issuing a warning message. Yes the data is a bit patholog...
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Big News! “Practical Data Science with R” MEAP launched!
Nina Zumel and I ( John Mount ) have been working very hard on producing an exciting new book called “Practical Data Science with R.” The book has now entered Manning Early Access Program (MEAP) which allows you to subscribe to chapters as they become available and give us feedback before the book goes into print. Please subscribe to our ...
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What is “Practical Data Science with R”?
A bit about our upcoming book “Practical Data Science with R”. Nina and I share our current draft of the front matter from the book, which is a description which will help you decide if this is the book for you (we hope that it is). Or this could be the book that helps explain what you do to others. What is Data Science? The statistician W...
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Practical Data Science with R, deal of the day Aug 1 2013
Deal of the Day August 1: Half off my book Practical Data Science with R. Use code dotd0801au at www.manning.com/zumel/ Related posts: Data Science, Machine Learning, and Statistics: what is in a name? Data science project planning Setting expectations in data science projects Related To leave a comment for the author, please follow the link a...
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Practical Data Science with R October 2013 update
A quick status update on our upcoming book “Practical Data Science with R” by Nina Zumel and John Mount. We are really happy with how the book is coming out. We were able to cover most everything we hoped to. Part 1 (especially chapter 3) is already being used in courses, and has some very good stuff on how to review data. Part 2 covers th...
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Practical Data Science with R: Manning Deal of the Day November 19th 2013
Please share: Manning Deal of the Day November 19: Half off Practical Data Science with R. Use code dotd1119au at www.manning.com/zumel/. Related posts: Data Science, Machine Learning, and Statistics: what is in a name? Data science project planning Setting expectations in data science projects Related To leave a comment for the author, please...
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Sample size and power for rare events
We have written a bit on sample size for common events. We would like to extend this analysis to rare events. In web marketing and a lot of other applications you are trying to estimate a probability of an event (like conversion) where the probability is fairly low (say 5% to 0.5%). In this case we our rules of thumb given in 1 and 2 are a bit...
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Generalized linear models for predicting rates
I often need to build a predictive model that estimates rates. The example of our age is: ad click through rates (how often a viewer clicks on an ad estimated as a function of the features of the ad and the viewer). Another timely example is estimating default rates of mortgages or credit cards. You could try linear regression, but specialize...
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