The correct answer is: A. coefficient of determination
The coefficient of determination, denoted $R^2$, is a statistical measure of how well data fit a straight line. It is calculated by taking the square of the correlation coefficient, which is a measure of the linear correlation between two variables. A perfect fit would have a coefficient of determination of 1, while a completely random fit would have a coefficient of determination of 0.
The coefficient of determination is often used in regression analysis to measure the goodness of fit of a model. It can also be used to compare the fit of different models. For example, if you have two models that both fit the data well, the model with the higher coefficient of determination is a better fit.
The coefficient of determination is a useful tool for understanding how well data fit a model. However, it is important to note that it is not a perfect measure of goodness of fit. For example, a model with a high coefficient of determination may still be a poor fit if the data are not normally distributed.
The other options are incorrect because they do not accurately describe the goodness of fit predicted values.
- Option B, coefficient of index, is not a commonly used term in statistics. It is not clear what this term would refer to.
- Option C, coefficient of residual, is a measure of the error in a model. It is calculated by taking the difference between the observed values and the predicted values.
- Option D, coefficient of prediction, is a measure of the accuracy of a model’s predictions. It is calculated by taking the mean of the squared differences between the observed values and the predicted values.