DA0-002 Data Analysis Practice Question
A data analyst is building a logistic regression model to predict whether a customer will churn (yes/no). Which TWO statements about logistic regression are correct?
⚠ Common exam trap
DA0-002 often tests the confusion between linear and logistic regression assumptions — candidates incorrectly apply OLS assumptions (normal errors, continuous outcome) to logistic regression or miss that the linearity assumption applies to log-odds, not raw 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
✓
The output is a probability between 0 and 1.
Option C is correct because logistic regression applies the sigmoid (logistic) function to a linear combination of predictors, producing an output that is a probability bounded between 0 and 1, which is exactly what is needed to model the churn probability (yes/no). Option E is correct because logistic regression assumes linearity on the logit scale: the log-odds of the outcome, ln(p/(1-p)), is modeled as a linear function of the predictor variables. Option A is incorrect because logistic regression is a classification method for binary outcomes, not a time series forecasting technique. Option B is incorrect because the dependent variable is binary/categorical (churn yes/no), not continuous. Option D is incorrect because logistic regression does not require normally distributed errors; that assumption belongs to linear regression, whereas logistic regression uses maximum likelihood estimation with a binomial error distribution.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
It is used only for time series forecasting.
Why it's wrong here
Logistic regression models binary or categorical outcomes from cross-sectional features; it does not forecast ordered time-indexed values. It is tempting because regression techniques appear in forecasting workflows, and would be correct where the target is a future numeric value in a temporal sequence.
- ✗
The dependent variable is continuous.
Why it's wrong here
Churn is a binary categorical outcome, so the dependent variable must be dichotomous, not continuous. It is tempting because linear regression predicts a continuous dependent variable, and that framing would be correct if the target were, for example, customer lifetime value in currency.
- ✓
The output is a probability between 0 and 1.
Why this is correct
Logistic regression applies the sigmoid function to a linear combination of predictors, squashing its output into the 0 to 1 interval. That value is interpreted as the probability of the positive class, directly supporting the churn yes/no prediction the analyst requires.
- ✗
It requires normally distributed errors.
Why it's wrong here
Logistic regression uses maximum likelihood estimation and assumes independent Bernoulli-distributed outcomes, not normally distributed errors. It is tempting because ordinary least squares regression does assume normally distributed residuals, which would be correct when fitting a linear model to a continuous response.
- ✓
It assumes a linear relationship between predictors and the log-odds of the outcome.
Why this is correct
Logistic regression models the log-odds of the outcome as a linear combination of the predictor variables. This linearity assumption on the logit scale, not on the raw probability, is what distinguishes it from models capturing non-linear predictor effects.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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