![]() ![]() \pi&=\dfrac\) percentile from the standard normal distribution. The multiple binary logistic regression model is the following: There is some discussion of the nominal and ordinal logistic regression settings in Section 15.2. We will investigate ways of dealing with these in the binary logistic regression setting here. Particular issues with modeling a categorical response variable include nonnormal error terms, nonconstant error variance, and constraints on the response function (i.e., the response is bounded between 0 and 1). Examples of ordinal responses could be how students rate the effectiveness of a college course on a scale of 1-5, levels of flavors for hot wings, and medical condition (e.g., good, stable, serious, critical). Ordinal Logistic Regression Used when there are three or more categories with a natural ordering to the levels, but the ranking of the levels does not necessarily mean the intervals between them are equal. Examples of nominal responses could include departments at a business (e.g., marketing, sales, HR), type of search engine used (e.g., Google, Yahoo!, MSN), and color (black, red, blue, orange). Nominal Logistic Regression Used when there are three or more categories with no natural ordering to the levels. Other examples of binary responses could include passing or failing a test, responding yes or no on a survey, and having high or low blood pressure. The cracking example given above would utilize binary logistic regression. Binary Logistic Regression Used when the response is binary (i.e., it has two possible outcomes). ![]()
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