DA0-002 Data Analysis Practice Question
A data scientist is building a predictive model to forecast monthly sales. The data shows a linear trend with no seasonality. Which regression technique is most appropriate?
⚠ Common exam trap
It's easy for candidates to confuse 'linear trend' with 'linear in parameters' and incorrectly choose polynomial regression, thinking it adds flexibility, when the question explicitly states no seasonality and a linear trend, making simple linear regression the optimal choice.
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
✓
Linear regression
Linear regression is the most appropriate technique because the data shows a linear trend with no seasonality, making a straight-line model the simplest and most effective fit. It directly models the relationship between the independent variable (e.g., time) and the dependent variable (monthly sales) using a linear equation, minimizing the sum of squared residuals.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
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Polynomial regression
Why it's wrong here
A linear trend with no seasonality is captured by straight-line regression; polynomial terms add curvature the data does not exhibit, risking overfitting. Polynomial regression suits curved relationships, such as diminishing returns or accelerating growth, where the slope itself changes across the range of the predictor.
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Logistic regression
Why it's wrong here
Logistic regression predicts a binary or categorical outcome via a sigmoid function, so it cannot model continuous monthly sales values. It is the right technique for classification tasks such as predicting whether a customer will churn.
- ✓
Linear regression
Why this is correct
Linear regression models a straight-line relationship between predictors and a continuous target, matching data with a linear trend and no seasonality. Seasonal techniques such as SARIMA or decomposition would add unnecessary parameters, while tree ensembles forgo the interpretable linear fit the pattern warrants.
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Ridge regression
Why it's wrong here
Ridge regression adds L2 regularisation to shrink coefficients, which addresses multicollinearity or overfitting rather than fitting a clean linear trend. It is the right choice when predictors are highly correlated and ordinary least squares produces unstable coefficients.
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