```Date: Sun, 5 Dec 1999 05:53:53 -0300 Reply-To: hmaletta@overnet.com.ar Sender: "SPSSX(r) Discussion" From: "Hector E. Maletta" Subject: Re: Presentation of logistic and linear regression Comments: To: Staffan Lindberg Content-Type: text/plain; charset=us-ascii I've had recently to present logistic regression results to a lay audience, and can understand Staffan's plight. I chose to explain the gist of the procedure in verbal form, made a reference to the classification table an the pseudo R2 as measures of the analysis goodness of fit, and then discussed the most significant log reg coefficients in terms of "how much is the impact of an increase in X on the probability of Y, other things being equal?" I evaluated the effects of one independent variable for people with average values in other independent variables. For this I generated graphs of the logistic regression equation obtained, keeping all other indep variables at their meamn values, and showing the relation between p(Y) and X in the manner chosen by Herrnstein and Murray to present their results in The Bell Curve. These graphs are not a standard SPSS result: I used Excel (one column for X, and another for the value of the log reg equation for varying values of X while keeping other independent variables constant at their means). Below are some specific observations on Staffan's message. Staffan Lindberg wrote: > > I have done some logistic regressions with approximately a dozen > independent variables in each, all binary coded except age that has 4 > levels. David Nichols and John Hendrickx kindly explained the difference > between simple and indicator contrasts. Now I have a new headache. > Are the results in SPSS (I am > running 6.1.4) univariate or multivariate ? If you have more than one independent variable, they are multivariate in the usual sense of the word. But your dependent variable must be a dichotomous one. In later versions, SPSS introduced logistic regression for dependent variables with more than two categories. >Are both necessary ? I do not think this question has any sense. Univariate log reg is used when the problem involves only one independent variable, and multivariate when there are more, as with ordinary linear regression. >Should > confidence levels for Exp(b) be presented ? I guess so. Exp(b) is the log odds ratio, and their CI may be useful to grasp the decisiveness of the conclusions reached. >Is there some form of commonly > accepted table layout analogous to an ANOVA table ? No. > These questions also pertain to some linear regressions I made. In this > context I also wonder what is commonly presented, the R Square or the > adjusted R Square or both ? I ordinarily present both (sometimes adding a brief explanation of the "adjusted" coefficient). For lay audiences, it may help to state that both R2 reflect the proportion of variance in the dependent variable that is explained by the other variables in the equation. Remember, though, that log reg can yield deceptively high R2 values when you have many variables relative to the number of cases. The "actual" number of variables includes k-1 dummy variables for every categorical independent variable having k categories. Hector Maletta Universidad del Salvador Buenos Aires, Argentina ```

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