DA0-002 Data Analysis Practice Question
A logistic regression model predicts customer churn (0=no churn, 1=churn). The model outputs probabilities. Which THREE of the following statements about logistic regression are correct?
⚠ Common exam trap
DA0-002 often tests logistic regression by mixing in linear regression concepts — candidates who assume R² applies or that the linear equation is used directly pick the wrong statements, missing that logistic regression uses log-odds and the sigmoid function.
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 model output is a probability between 0 and 1.
Option A is correct because logistic regression applies the sigmoid (logistic) function to a linear combination of predictors, producing an output strictly between 0 and 1 that can be interpreted as the probability of the positive class (churn = 1). Option C is correct because the model is linear in the log-odds: each coefficient βj gives the change in log-odds of the outcome for a one-unit increase in predictor Xj, holding other predictors constant (equivalently, e^βj is the odds ratio). Option D is correct because logistic regression is specifically designed for binary classification, here distinguishing churn (1) from no churn (0) by thresholding the predicted probability. Option B is not correct because R² (coefficient of determination) is a goodness-of-fit measure for ordinary least squares linear regression, not for logistic regression, which instead uses measures like log-likelihood, deviance, AIC/BIC, or pseudo-R². Option E is not correct because logistic regression does not use the linear equation y = mx + b directly; it models the log-odds as a linear function and then applies the logistic function to obtain probabilities, rather than predicting y linearly.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
The model output is a probability between 0 and 1.
Why this is correct
The sigmoid function maps any linear combination of predictors onto the interval between 0 and 1, so each output is interpretable as the estimated probability of churn. A threshold, commonly 0.5, then converts that probability into a class label.
- ✗
The coefficient of determination R² is used to assess model fit.
Why it's wrong here
R² measures variance explained in ordinary least squares linear regression; logistic regression uses maximum likelihood, so pseudo-R² or deviance, AIC and log-loss assess fit instead. R² is the correct metric when evaluating a linear regression model's goodness of fit on continuous outcomes.
- ✓
The coefficients represent the change in log-odds for a one-unit change in the predictor.
Why this is correct
Each fitted coefficient quantifies how the log-odds of churn change when its predictor increases by one unit, holding other predictors constant. Exponentiating a coefficient yields an odds ratio, which is how the model's directional effect on churn risk is interpreted.
- ✓
Logistic regression is used for binary classification.
Why this is correct
Logistic regression models a binary outcome by fitting a sigmoid function, so it suits the churn scenario where the target takes values 0 or 1. The technique estimates the probability of class membership rather than predicting a continuous numeric response.
- ✗
The model uses the linear regression equation y = mx + b directly.
Why it's wrong here
Logistic regression applies the logit link, modelling log-odds as a linear combination of predictors, then transforming via the sigmoid to bound output between 0 and 1. The raw linear equation would permit probabilities outside that range. The linear form is correct for linear regression predicting continuous outcomes.
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Last reviewed September 2026 · checked against the official CompTIA exam blueprint
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