January 2012
Intermediate to advanced
160 pages
4h 14m
English
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Dichotomous Logistic Regression
Throughout this book we referred extensively to the logistic regression methodology as a means to either calibrate the logit price-response functions of chapter 1 and chapter 2 or estimate the logit bid-response probability functions introduced in chapter 4. While the two contexts exhibit many similarities in terms of their final outcome (e.g., both functions are inverse S-shaped and approach zero at some high prices), from a methodological standpoint, they require two distinct sets of statistical tools. First, the logit price-response functions are computed using nonlinear regression models that attempt to minimize the sum of squared errors between the observed demand and the demand expected to materialize ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.