DA0-002 Data Analysis Practice Question
A retail company wants to predict sales based on advertising spend and season. Which data modeling technique should the analyst use?
⚠ Common exam trap
It's easy for candidates to confuse simple linear regression with multiple linear regression, thinking that 'linear regression' alone suffices, but the exam specifically tests whether you recognize that multiple predictors require multiple regression.
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
✓
Multiple linear regression
Multiple linear regression is the correct technique because the analyst needs to model a continuous outcome (sales) based on two or more predictor variables: advertising spend (continuous) and season (categorical, typically encoded as dummy variables). This allows the model to capture the independent effect of each predictor on sales, which simple linear regression cannot do because it only handles one predictor.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Simple linear regression
Why it's wrong here
Simple linear regression fits one continuous predictor, so it cannot incorporate the categorical season variable alongside advertising spend. Multiple linear regression handles several predictors, with season dummy-encoded. Simple regression would be correct if sales depended on advertising spend alone.
- ✓
Multiple linear regression
Why this is correct
Multiple linear regression models a continuous outcome, sales, as a linear function of two or more predictors, advertising spend and season. Season enters as a categorical dummy variable, satisfying the requirement to predict sales from both numeric and categorical inputs.
- ✗
Logistic regression
Why it's wrong here
Sales is a continuous target, so logistic regression's sigmoid output for class probabilities cannot represent it; the model would need to be repurposed, distorting predictions. Logistic regression is correct when the target is binary or categorical, such as predicting whether a customer purchases.
- ✗
K-means clustering
Why it's wrong here
K-means clustering groups unlabelled records by similarity, so it cannot predict a continuous target such as sales from advertising spend and season. It is tempting because it segments customers or stores, but forecasting a numeric outcome requires regression, which models the relationship between predictors and the target.
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