By Scott Menard
Emphasizing the parallels among linear and logistic regression, Scott Menard explores logistic regression research and demonstrates its usefulness in reading dichotomous, polytomous nominal, and polytomous ordinal based variables. The booklet is aimed toward readers with a heritage in bivariate and a number of linear regression.
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Extra info for Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences)
2. It is also the case that for very low values of X (or very high values if the relationship is negative) the conditional probability that Y = 1 will be so close to zero that it should change little with further decreases in X. The curve representing the relationship between X and Y should therefore be very shallow, with a slope close to zero, for very high and very low values of X, if X can, in principle, become indefinitely large or indefinitely small. If X and Y are related, then between the very high and very low values of X the slope of the curve will be steeper, significantly different from zero.
07-106 author : Menard, Scott W. publisher : Sage Publications, Inc. isbn10 | asin : 0803957572 print isbn13 : 9780803957572 ebook isbn13 : 9780585180823 language : English subject Regression analysis, Logistic distribution. 5/36 subject : Regression analysis, Logistic distribution. Applied Logistic Regression Analysis SAGE UNIVERSITY PAPERS Series: Quantitative Applications in the Social Sciences Series Editor: Michael S. Lewis-Beck, University of Iowa Editorial Consultants Richard A. Berk, Sociology, University of California, Los Angeles William D.
If the independent variables are useful in predicting Y, then, the value of Y predicted by the regression equation (the conditional mean of Y) will be a better predictor than of the values of Y, and the sum of squared errors S(Yj -)2 will be smaller than the sum of squared errors S(Yj )2. S(Yj )2 is called the Error Sum of Squares or SSE, and it is the quantity OLS selects parameters b1, b2, . , bk to minimize. A third sum of squares, the Regression Sum of Squares or SSR, is simply the difference between SST and SSE: SSR = SST SSE.