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A) The total number of data points used in the analysis.
B) The strength of the correlation between the dependent and independent variables.
C) The proportion of the variance in the dependent variable that can be explained by the independent variable(s).
D) The average error of predictions made by the model.
C) The proportion of the variance in the dependent variable that can be explained by the independent variable(s). A higher R-squared value indicates a better fit of the model to the data.
C. The proportion of the variance in the dependent variable that can be explained by the independent variable(s).
C) The proportion of the variance in the dependent variable that can be explained by the independent variable(s). A higher R-squared value indicates a better fit of the model to the data.