By David W. Hosmer, Stanley Lemeshow
This ebook discusses the right way to version a binary end result variable from a linear regression research standpoint. It develops the logistic regression version and describes its use in equipment for modelling the connection among a dichotomous end result variable and a collection of covariates. dialogue of the translation of this version follows. The textual content contains a number of information units that are the resource of the examples and routines. The booklet additionally makes use of a few software program applications, together with BMDP, EGRET, GLIM, SAS, and SYSTAT, to investigate info units.
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Extra resources for Applied Logistic Regression (Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics Section)
If the estimated probability exceeds c then we let the derived variable be equal to 1; otherwise it is equal to zero. 5. The appeal of this type of approach to model assessment comes from the close relationship of logistic regression to discriminant analysis when the distribution of the covariates is multivariate normal within the two outcome groups. However, it is not limited to this model; see, for example, Efron (1975). In this approach, estimated probabilities are used to predict group membership.
Additional mathematical assumptions are also needed; but for the above case they are rather nonrestrictive and involve having a sufficiently large sample size, n. 3. 3, and the remainder of the expression simply substitutes n1 and n0 into Â < previous page < previous page page_15 page_150 next page > next page > Page 150 begin by briefly describing logistic regression diagnostics. Â , J. The derivation of the diagnostics is at a higher mathematical level than most of the material in this text. It is not necessary to understand the mathematical development to effectively apply the diagnostics in practice.
48 49 Â < previous page < previous page next page > next page > page_147 page_148 Page 148 For sake of completeness we present an R2-type measure which has been suggested for use with logistic regression. For reasons we will explain later, we do not advocate the use of this statistic for assessing goodness-of-fit. In linear regression, R2 is the ratio of the regression sum-of-squares to the total sum-of-squares. It is convenient to think of the regression sum-of-squares as being the difference between the total and residual sum-of-squares.