DA0-002 Data Analysis Practice Question
A marketing analyst wants to predict whether a customer will churn (yes/no) based on account age and monthly charges. Which regression technique is most appropriate?
⚠ Common exam trap
The trap is confusing regression techniques: candidates might think any regression can predict binary outcomes, but only logistic regression is suited for classification. Linear regression outputs continuous values, which are not probabilities.
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
✓
Logistic regression
Logistic regression is specifically designed for binary classification problems, such as predicting churn (yes/no). It models the probability of the outcome using a logistic function, making it appropriate for this scenario. Simple and multiple linear regression are for continuous outcomes, and K-means is for clustering, not prediction.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Logistic regression
Why this is correct
Churn is a binary yes/no outcome, and logistic regression models the probability of a categorical dependent variable using a sigmoid function. Linear regression would predict continuous values outside 0–1, making it unsuitable for this classification scenario.
- ✗
Simple linear regression
Why it's wrong here
Simple linear regression models one continuous predictor against a continuous outcome, so it cannot represent a binary churn label or use both account age and monthly charges. It fits forecasting a numeric value from a single variable. Logistic regression is needed because the outcome is categorical with two classes.
- ✗
Multiple linear regression
Why it's wrong here
Multiple linear regression predicts a continuous numeric outcome from several predictors, so it outputs values outside the 0-1 range and cannot classify churn as yes or no. It suits estimating continuous quantities such as revenue. Logistic regression applies a sigmoid function to produce class probabilities for a binary target.
- ✗
K-means clustering
Why it's wrong here
K-means is unsupervised clustering that groups unlabelled records by distance, producing cluster assignments rather than a yes/no prediction from labelled churn outcomes. It suits customer segmentation, not classification. Logistic regression is required because the target is binary, and account age plus monthly charges act as predictor variables.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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