Final answer:
R², also known as the coefficient of determination, is interpreted as the percentage of data variability that is explained by the regression model. It is the square of the correlation coefficient (r) and helps to quantify the predictive power of the model. The correct option is O percentage.
Step-by-step explanation:
R² may be interpreted as the percentage of data variability that can be accounted for by the model. This statistical measure is known as the coefficient of determination, which is the square of the correlation coefficient (r). When r² is expressed as a percentage, it indicates what portion of the variance in the dependent variable (y) can be predicted from the independent variable (x) through the use of the regression line.
For example, if the coefficient of determination (r²) is represented as 0.44 or 44% in the context of an example, it implies that approximately 44 percent of the variation in the final exam grades can be accounted for by the variation in the grades on the third exam, using the best-fit regression line.
On the other hand, 1 - r², when expressed as a percentage, indicates the amount of variability that is not accounted for by the model.
The correlation coefficient (r) measures the strength and direction of the linear relationship between x and y but does not itself indicate the extent to which variations in x explain variations in y.
That understanding is provided by the coefficient of determination, r², which is particularly useful in statistical analysis to assess the predictive power of a linear regression model.