Answer :
Final answer:
The claim that a logistic regression's dependent variable must be numeric is false; it is actually binary, taking values like 0 or 1, and used to predict probabilities of outcomes.
Explanation:
The statement that in logistic regression the dependent variable (DV) has to be numeric (e.g., 23.5, 4.76, 100.2 etc.) is false. The dependent variable in logistic regression is binary, typically coded as 0 or 1, representing two classes or categories. Logistic regression is used to model and predict the probability of a binary outcome based on one or more predictor variables.
Logistic regression is particularly popular in fields such as the medical sciences, where it might be used to predict the probability of an event such as a heart attack occurring based on various risk factors. This form of regression analysis is part of a larger group known as Generalized Linear Models (GLM).
The ability to perform complex calculations such as logistic regression has been significantly facilitated by the advent of machine learning and computer software. Prior to the late 1970s and early 1980s, these calculations would often have to be performed manually.