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Extreme outliers may be the result of incorrect data entry (or computation). Double-check that these outliers have been coded correctly. These transformations are what you should first use.Ĭheck the data for extreme outliers. Before using any of these transformations, determine which transformations, if any, are commonly used in your field of research. Logistic Regression uses a different method for estimating the parameters, which gives better results–better meaning unbiased, with lower variances.Some transformation options are offered below. That would be way too easy, but also give inaccurate results. will form a linear relationship with our parametersĪll three of these work just as well, but (believe it or not) the Logit function is the easiest to interpret.īut as it turns out, you can’t just run the transformation then do a regular linear regression on the transformed data. are not restricted to values between 0 and 1Ģ. To obtain a linear relationship, we need to transform this response too, Pr(success).Īs luck would have it, there are a few functions that:ġ. It turns out the relationship is not linear, but rather follows an S-shaped (or sigmoidal) curve. The right hand side of the equation can be any number, but the left hand side can only range from 0 to 1.Ģ. Why not use a simple transformation of Y, like probability of success–the probability that Y=1.ġ. The values of 0 and 1 are arbitrary.The important part is not to predict the numerical value of Y, but the probability that success or failure occurs, and the extent to which that probability depends on the predictor variables. The predicted values can be any positive or negative number, not just 0 or 1.ģ. You just can’t make a line out of that (at least not one that fits the data well).Ģ. Why not use a regular regression model? Just turn Y into an indicator variable–Y=1 for success and Y=0 for failure.ġ.It doesn’t make sense to model Y as a linear function of the parameters because Y has only two values. Logistic regression models can seem pretty overwhelming to the uninitiated.