The correct answer is: D. goodness of fit.
Goodness of fit is a measure of how well a model fits the data. It is often used in statistics to evaluate the validity of a model. A model with good goodness of fit will have a low residual sum of squares, which means that the data points are close to the line of best fit.
Badness of fit is the opposite of goodness of fit. It is a measure of how poorly a model fits the data. A model with bad goodness of fit will have a high residual sum of squares, which means that the data points are far from the line of best fit.
Residuals are the differences between the actual values and the predicted values of a model. The residual sum of squares is the sum of the squares of the residuals. A low residual sum of squares indicates that the model fits the data well. A high residual sum of squares indicates that the model does not fit the data well.
The line of best fit is the line that minimizes the sum of the squared distances between the data points and the line. The line of best fit is often used to represent the relationship between two variables.
In the context of the question, the strength of the relationship between a cost driver and cost is considered as goodness of fit. This is because a strong relationship between a cost driver and cost means that the cost driver is a good predictor of cost. A good predictor of cost will have a low residual sum of squares, which means that the data points are close to the line of best fit.