Provides a method of evaluating multiple process inputs when the process output is defined as the probability of a desired outcome. The experiment can be either a designed experiment using controlled factors or an uncontrolled experiment. Simply put, binary logistic regression turns the discrete responses of good/bad, yes/no, or buy/don't buy into a continuous % likelihood of yes, good, or buy, then uses regression methods to build a predictive model.
|When to Use||Purpose|
|Mid-project||Evaluate the effects of multiple process inputs on a process output, which is defined as the probability of one of two possible outcomes (good/bad, yes/no, or buy/don't buy).|
|Mid-project||Determine which inputs are the key inputs.|
|Mid-project||Build a predictive model using the key inputs.|
|Mid-project||Determine the settings of the key inputs that will result in the optimal process output.|
Discrete Y at two levels, categorical or numeric X's