In regression analysis, the dependent variable is typically

In regression analysis, the dependent variable is typically

• A. A continuous variable
• B. A categorical variable
• C. A binary variable
• D. An ordinal variable

Answer: (A)

Regression aims to predict a numeric, real-valued outcome from predictor variables. This requires the dependent variable to be continuous because the model produces a precise predicted value and the errors (residuals) are meaningful as numeric differences on the same scale. When the outcome is categorical, you’d use classification instead of regression; binary outcomes are commonly modeled with logistic regression to estimate probabilities of class membership, while ordinal outcomes call for ordinal regression that respects ordering. In the standard regression framework, the dependent variable is typically a continuous variable.

Regression aims to predict a numeric, real-valued outcome from predictor variables. This requires the dependent variable to be continuous because the model produces a precise predicted value and the errors (residuals) are meaningful as numeric differences on the same scale. When the outcome is categorical, you’d use classification instead of regression; binary outcomes are commonly modeled with logistic regression to estimate probabilities of class membership, while ordinal outcomes call for ordinal regression that respects ordering. In the standard regression framework, the dependent variable is typically a continuous variable.