Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences)

I bought this book to teach myself logistic regression after buying a much much more expensive text . If you've had the experience of trying to learn a stats technique on your own then you know that you'll probably need more than one book. If I could go back, I would buy this one first and then move on to other more expensive and comprehensive texts. I had a good grasp of multiple regression already and found this book's orientation to logistic regression, done by drawing parallels with multiple regression, very understandable. It was easy to read cover to cover and gave great explanations of the background math, without being at all heavy with formulas. If you are taking a logistic regression course and are having a hard time following the explinations in the text assigned for the class, this would likely provide a good alternative for helping you grasp the concepts.

This book provided a great help in further understanding the statistical concepts.

This is an adequate reference book that I refer to occasionally. I would not use it to learn from.

Great reference book on regression analysis on a level most of us can understand. Saved this book for review and reference.

A good, cheap overview of logistic regression analysis.I bought and I'm glad I did, but I don't refer to it like I do Hosmer and Lemeshow's text.

very good experience

Logistic regression analysis is an analogue to multiple regression--with the dependent variable normally (but not always) pitched at the dichotomous level (just two values). It is a pretty sturdy statistical technique, not demanding a lot of assumptions about the nature of dependent and independent variables. This book is not terribly accessible (I get headaches in some sections), but it is a very nice brief introduction to the subject.The book begins by noting the rigorous assumptions for multiple regression to "work." Logistic regression does not demand so much of the data. Then, step by step, the book lays out the results from logistic regression and how to interpret these. There is no "explained variation" (multiple R squared) as with multiple regression. There is, however, a pseudo-R squared. Other key outputs? Predictive accuracy of the model; Wald's number; Model Chi Square; Goodness of fit; log-likelihood; standardized and unstandardized coefficients; and on and on. The book also lays out a set of diagnostics, to see if there are any threats to accepting the results of the statistical analysis as legitimate (e.g., outliers).The book is NOT an easy read. But if you want a brief introduction to the subject, this book is quite helpful. At times, I think, matters could be explained more lucidly, but--overall--this is a nice contribution.

Menard's little, green Sage paperback is an excellent introduction to logistic regression analysis. In spite of its brevity, it also serves well as a reference, including off-beat topics such as how to compute standardized regression coefficients for logistic regression equations. Moreover, some of the usual output of SPSS logistic regression runs would be uninterpretable, and commonplace questions would be unduly difficult to answer, if it were not for Mendard's text and its effective use of examples of SPSS output.Before I bought Menard's introduction, I tried to improve my understanding of logistic regression, including proper interpretation of unstandardized coefficients and various measures of goodness of fit, with the first edition of Hosmer and Lemeshow's Applied Logistic Regression. Compared to Mendard's book, however, Hosmer and Lemeshow's presentiation is tedious, plodding, and needlessly dense. Apparently it was written for an audience to which I do not belong.I use logistic regression fairly often, and I have yet to encounter an issue that I couldn't address through reference to Menard's Applied Logistic Regression Analysis. The explanations are clear, the formulas are easy to follow, and the examples are instructive. An awful lot of useful information is packed into one brief and inexpensive document.

SAGEのシリーズは安く、薄く、分かりやすくでファンの一人です。このDr.Menardの本もまさにパールの宝庫でした。自分が新しく発見したのはgoodness-of-fitを評価する方法で、"pseudo R square"を知ったこと(Menardお勧めらしい)predictive efficiencyの評価は今までROCのAUCしか知りませんでしたが、τp, λpなどを知ったこと。純粋な予測or探索モデルの変数選択時にステップワイズを使うときに、α=0.05は厳しすぎる条件ということ数学的に難しく考えず、単回帰分析の基本的な数理を知っていればそれからの類推で理解できるように説明されています。しかも徹頭徹尾同じケース(マリファナ使用の予測モデル)で議論しているので前後の整合性があり、読みやすくなっています。お勧めです。