Final answer:
The logistic regression model generates numeric probabilities, but the target values are categorical, such as '0' or '1' in binary classification cases. It is critical for the estimation based on logistic regression to be within the context of data's domain to ensure its reliability and to acknowledge that such models are approximations for real-world scenarios.
Step-by-step explanation:
The logistic regression model produces a numeric estimate, but the values of the target variable are categorical. In logistic regression, the model calculates the probability that a given input point belongs to a certain category. This is different from linear regression where the output is a continuous number. The estimation provided by logistic regression is represented by ŷ, known as y hat, which is the predicted probability for the target variable based on the logistic function, often used for binary classification problems. The actual target variable values in logistic regression are typically binary (0 or 1) or belong to a set of discrete categories.
It is important to note that numerical predictions from regression models should be interpreted within the context of the data they are based on. Using a value like 90 in an equation where the independent variable's domain is between 65 and 75 would result in a prediction that is not reliable. Thus, understanding the domain of the input data is crucial for making accurate predictions with any statistical model.
Moreover, real-life situations require approximations and estimations, and logistic regression provides a mathematical model to predict uncertain outcomes based on the relationship between variables, without assuming that they determine one another with complete precision. Such mathematical models, including the logistic curve, help in understanding complex systems, even though they may not capture every nuance.