Question 1,113 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A data analyst is exploring a dataset and notices that the target variable has a Poisson distribution. Which type of model is most appropriate for this target?
⚠ Common exam trap
Many candidates confuse Poisson regression with logistic regression or linear regression, mistakenly applying a model for binary outcomes or continuous data to count data, without recognizing that the Poisson distribution's unique properties require a specialized GLM.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Poisson regression
Poisson regression is the correct choice because it is specifically designed for modeling count data where the target variable follows a Poisson distribution, which is characterized by non-negative integer values and a variance equal to the mean. This aligns directly with the data analyst's observation of a Poisson-distributed target, making Poisson regression the most appropriate generalized linear model (GLM) for this scenario.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Poisson regression
Why this is correct
Poisson regression models count data with Poisson distribution.
- ✗
Linear regression
Why it's wrong here
Linear regression assumes normally distributed errors.
- ✗
Cox proportional hazards model
Why it's wrong here
Cox model is for time-to-event data.
- ✗
Logistic regression
Why it's wrong here
Logistic regression is for binary classification.
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Last reviewed: Jun 24, 2026
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