Question
A data scientist is building an inferential model with a single predictor variable. A scatter plot of the independent variable against the real-number dependent variable shows a strong relationship between them. The predictor variable is normally distributed with very few outliers. Which of the following algorithms is the best fit for this model, given the data scientist wants the model to be easily interpreted?
- A. A logistic regression
- B. An exponential regression
- C. A linear regression Correct
- D. A probit regression
Explanation
The dependent variable is a real number and the scatter plot shows a strong relationship with a single, normally distributed predictor with few outliers, which fits the assumptions of simple linear regression. Its slope coefficient is directly interpretable as the change in the outcome per unit change in the predictor. Logistic and probit regression model binary outcomes, and exponential regression adds a transformation that makes interpretation less direct without evidence of a nonlinear pattern.
