· Notice that odds and Because these log odds values have no immediate meaning to most people, it is identical to two decimal places for small values(although to more decimal places sometimes helpful to remember that they the odds are always slightly higher) correspond directly and uniquely with proportions. As a rough guide the In the case of our model we can estimate following table shows correspondences the corresponding proportions as follows between log odds, odds(the exponential from the log odds we have alread of log odds)and proportions, for log odds calculated in the range-3 to +3 pregnancy2)exp(-1.1229(1+exp-11229)=025 pregnancy4)exp(0.2597)(1+exp(0.2597)=056 6)exp(16424(1texp(16424)=0.84 ddsOddsProportic Goodness of fit 005 The output gives us quite a lot of other information, of which the most important is the information about the likelihood ratio x 2 (called in the output -2 log likelihood for 060 ome reason). We will call this parameter LR, 2, noting that it is exactly the same 272 ter given by the statistic Chi-sq in the CROSSTABS procedure. It provides portant information about the goodness of fit of the logistic regression model. We find in two places in the output13 25 • Because these log odds values have no immediate meaning to most people, it is sometimes helpful to remember that they correspond directly and uniquely with proportions. As a rough guide the following table shows correspondences between log odds, odds (the exponential of log odds) and proportions, for log odds in the range -3 to +3. 26 14 27 • Notice that odds and proportions are identical to two decimal places for small values (although to more decimal places the odds are always slightly higher). • In the case of our model, we can estimate the corresponding proportions as follows from the log odds we have already calculated: (pregnancy 2) exp(-1.1229)/(1+exp(-1.1229)) = 0.25 (pregnancy 4) exp(0.2597)/(1+exp(0.2597)) = 0.56 (pregnancy 6) exp(1.6424)/(1+exp(1.6424)) = 0.84 28 Goodness of fit • The output gives us quite a lot of other information, of which the most important is the information about the likelihood ratio χ2 (called in the output '-2 log likelihood' for some reason). We will call this parameter LR χ2, noting that it is exactly the same parameter given by the statistic Chi-square in the CROSSTABS procedure. It provides important information about the goodness of fit of the logistic regression model. We find it in two places in the output: