Final answer:
Option (c), In statistics and probability, a probabilistic or statistical model deals with uncertainty and is used where outcomes cannot be precisely predicted, such as in weather forecasts, while deterministic models provide exact outcomes, like calculating a consistent distance.
Step-by-step explanation:
Probabilistic vs. Deterministic Models
A probabilistic model, also known as a statistical model, involves randomness and uncertainty, making it suitable for situations where outcomes cannot be predicted with certainty. This contrasts with deterministic models, which provide a precise outcome based on given inputs. For instance, in weather forecasts, meteorologists use probabilistic models to predict the likelihood of rain, which cannot be determined with absolute certainty due to the complexity and variability of weather patterns.
Another example is the case of driving time from New York to Florida, which would best be modeled statistically because of factors like traffic and road conditions introducing variability. Conversely, a mathematical model would be more appropriate for describing the distance from your house to school, as it is consistent and precisely measurable.
Understanding the difference between these two types of models is crucial in statistics and probability, as they help researchers and analysts choose the right approach for making predictions about real-world phenomena.