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A spunky new lawyer in the city is in for more than she bargained for with her new boss, the big time parter at Anderson & Flom. Will he tame her or will she tame.
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What do the raw abandon rates look like when we look at the worst largest abandon rates? The ranking by rate looks completely different from ranking by count : The Casual Vacancy , Catch , American Gods , A Game of Thrones etc have all vanished and been replaced by an almost disjoint set. Proportion rankings look right.

The Most 'Abandoned' Books on GoodReads

So Black Leopard, Red Wolf topping the ranking may not be flattering, but may be fair. Implementing Bayesian correction… However—that brings us back to sampling error. Black Leopard, Red Wolf just came out in and hardly anyone has read it. Is it really plausible that they have the highest abandon rates? So the next step is Bayesian modeling to account for the uncertainty caused by small n for these most-abandonable books. Black Leopard, Red Wolf does not enjoy any reprieve here, and to my annoyance, the top ranking changes little from raw proportion to shrunken proportion:.

Ah well! Top addictive books. On a more positive note, of the books which are included in the dataset, which 30 have the lowest abandon rates? I find the results entirely plausible 5 :. What else can we do? If we just wanted to rank titles, we have already done that. The ranking is what it is. Just look at it above! If we add in additional covariates, the meaning of the coefficient numbers change, even if the names like Title do not.

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So what would the number mean for Title if we added in those other variables? So Rowling might have a low abandonment rate in general since the Harry Potter books are so sticky and her Casual Vacancy rated highly with a minor penalty for being a recently-published book, but then her Casual Vacancy is unexpectedly frequently abandoned because her legions of fans discover it is really not what they expected nor hoped for , and so its marginal coefficient is extremely high even as its concrete, absolute, raw abandonment rate is low ; this reflects that Casual Vacancy is quite anomalous among J.

Flexibly modeling the covariates with splines. Author must be treated like Title , as a random effects, because most authors are represented only once and are uninformative. Similarly, for Year. Finally, because the Author adds in levels, and we have 2 new spline variables, this model will be substantially harder to fit to convergence, and will need to run for much longer; I ran it overnight on my AMD Threadripper.

With this in mind, some examples are interesting and make sense:. Rowling wrote it, and discovering it was nothing like Harry Potter. Similarly, Cryptonomicon : discussion of how to implement cryptography with a deck of cards? But for other people maybe not so much.

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Perhaps polarizing books are an example of what one should strive for? GoodReads also has a list for voting on frequently-abandoned books, but after looking at it, the data seems to be garbage and deeply unrepresentative of GoodReads readers in general, so I abandoned any further analysis of it. GR users can vote on books either casting a vote from a book or, default, not voting on it , and add new books to the list.

The list page reports that it covers 2, books, and has votes from 12, voters. With the idea of perhaps combining the two datasets in a latent variable factor analysis to get a more accurate measure of abandonment, I extracted the data from it, same as with the tag. The data is, however, so skewed as to be borderline useless:.

Effectively, aside from the top books, books have only 1 or 2 votes. Still, I gave it a chance, albeit with heavier regularization:. The results, while not totally absurd Infinite Jest still shows up , are too idiosyncratic to take seriously. The American Heritage Dictionary , really? An Arabic book? And the voters seem to have something against The Wiccan Diaries ….

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