By Scott Menard
Emphasizing the parallels among linear and logistic regression, Scott Menard explores logistic regression research and demonstrates its usefulness in interpreting dichotomous, polytomous nominal, and polytomous ordinal established variables. The publication is aimed toward readers with a history in bivariate and a number of linear regression.
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Additional info for Applied Logistic Regression Analysis (Quantitative Applications in the Social Sciences)
Title. Series. 5'36dc20 95-9071 99 10 9 8 7 6 5 Sage Project Editor: Susan McElroy When citing a university paper, please use the proper form. Remember to cite the current Sage University Paper series title and include the paper number. One of the following formats can be adapted (depending on the style manual used): (1) MENARD, S. (1995) Applied Logistic Regression Analysis. Sage University Paper series on Quantitative Applications in the Social Sciences, 07-106. Thousand Oaks, CA: Sage. OR (2) Menard, S.
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. It is possible in a sample of cases to get an apparent reduction in error of prediction by using the regression equation instead of to predict the values of Yj, even when the independent variables are really unrelated to Y. This occurs as a result of sampling variation, random fluctuations in sample values that may make it appear as though a relationship exists between two variables when there really is no relationship.
All variables are measured without error. 3 2. Specification: (a) All relevant predictors of the dependent variable are included in the analysis, (b) no irrelevant predictors of the dependent variable are included in the analysis, and (c) the form of the relationship (allowing for transformations of dependent or independent variables) is linear. 3. Expected value of error: The expected value of the error, e, is zero. 4. Homoscedasticity: The variance of the error term, e, is the same, or constant, for all values of the independent variables.