Logistic regression is the most common method used to model binary response data. When the response is binary, it typically takes the form of 1/0, with 1 generally indicating a success and 0 a failure. However, the actual values that 1 and 0 can take vary widely, depending on the purpose of the study. For example, for a study of the odds of failure in a school setting, 1 may have the value of fail, and 0 of not-fail, or pass. The important point is that 1 indicates the foremost subject of interest for which a binary response study is designed. Modeling a binary response variable using normal linear regression introduces substantial bias into the parameter estimates.