The correct answer is D. all of the above.
Goodness of fit is a measure of how well the regression line fits the data. It is usually measured by the coefficient of determination, $R^2$. A high $R^2$ value indicates that the regression line fits the data well.
Economic plausibility is a measure of whether the results of the regression make economic sense. For example, if the regression results indicate that a 1% increase in the price of a product leads to a 10% decrease in demand, this would be economically implausible, as it would suggest that consumers are very sensitive to price changes.
Significance of independent variable is a measure of whether the independent variable has a statistically significant effect on the dependent variable. This is usually measured by the p-value. A p-value less than 0.05 indicates that the independent variable has a statistically significant effect on the dependent variable.
All of these criteria are important to consider when evaluating a regression equation. A regression equation that does not have a good fit to the data, or that is not economically plausible, or that does not have statistically significant independent variables, is not likely to be very useful.