A Poisson model is a good choice of regression model in which of the following situations:

Question

BlackTom AI · Accepted Answer

We begin by identifying what a Poisson regression is typically used for: it models count data where the responses are nonnegative integers that represent the number of times an event occurs in a fixed interval of time or space, with the assumption that events occur independently and the mean rate is constant.
Option 1: Predicting the number of fatal car crashes at an intersection in a small town in Utah. This is a classic count outcome (number of crashes) within a defined spatial unit (an intersection) and a fixed period, so a Poisson model is well-suited if the data fit the count/mean-variance assumptions reasonably well.
Option 2: Predicting the SAT test score of a student after they take a review course. SAT scores are continuous numerical values, not counts of discrete events, so a Poisson model would not be appropriate here.
Option 3: Predicting the amount of money a guest will win or lose at a casino in a weekend. This outcome is monetary and continuous (and can include negative values), not a count of discrete events, which makes Poisson regression unsuitable.
Option 4: Predicting the number of total deaths from heart disease every year in the United States. While this is a count, it is a single aggregated total for the entire country per year, which is a large count and may exhibit overdispersion or be better modeled with other approaches (e.g., negative binomial or time-series/Poisson with rate adjustments). The suitability is not as straightforward as with a small-town intersection count, and practical modeling often calls for different techniques when counts are large or overdispersed.
In summary, option 1 aligns with the typical scenario for Poisson regression: a count outcome measured over a defined, localized unit and interval, making it the best fit among the choices.

A Poisson model is a good choice of regression model in which of the following situations:单项选择题

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