Which statement best describes simple linear regression?

Which statement best describes simple linear regression?

• A. It classifies observations into discrete categories.
• B. It clusters similar data points.
• C. It discovers association rules between variables.
• D. It models a linear relationship to predict a numeric outcome.

Answer: (D)

Modeling a linear relationship between one predictor and a numeric outcome to predict that outcome is what simple linear regression does. It fits a straight line, y = β0 + β1x, by choosing the intercept and slope to minimize the squared differences between observed values and the line, so you can estimate the numeric outcome for new predictor values. This differs from tasks that classify into categories, cluster data into groups, or derive rules between variables; those are not about predicting a continuous number from a single predictor. Simple linear regression focuses on predicting a continuous outcome using a straight-line relationship.

Modeling a linear relationship between one predictor and a numeric outcome to predict that outcome is what simple linear regression does. It fits a straight line, y = β0 + β1x, by choosing the intercept and slope to minimize the squared differences between observed values and the line, so you can estimate the numeric outcome for new predictor values. This differs from tasks that classify into categories, cluster data into groups, or derive rules between variables; those are not about predicting a continuous number from a single predictor. Simple linear regression focuses on predicting a continuous outcome using a straight-line relationship.