In 2003, the U.S. Securities and Exchange Commission (SEC) mentioned that the R-square of the regression analysis was insufficient to determine the effectiveness of hedging. In a speech by a professional accounting fellow from the office of the chief accountant of the U.S. Securities and Exchange Commission, stated that determination of hedge effectiveness should consider the slope of the coefficient of the regression analysis. This coefficient reflects the minimum variance hedge ratio.1
The interpretation of the regression slope coefficient is the average change in the dependent variable: real total expenditure on food for a unit increase in the independent variable: Real total expenditure on goods and services. The slope coefficient it 0.32, thus for every 1 unit change in real total expenditure on goods and services, there is a 0.32 unit change in real expenditure on food.
Omission of an important independent variable such as real price of food relative to other goods will result in the decreased ability of the model to predict the real total expenditures on food given the real total expenditures on food.
The independent variables used to predict 99.9% of the dependent variable: real consumption expenditure as indicated by the value of the adjusted-R Squared. The Durbin-Watson value of 1.85 indicates there is no signs of first-order serial correlation in the residuals of a time series regression. The values of Akaike info criterion (AIC) -268.2093 and Schwarz criterion -273.6530 are extremely low indicating the need for modification in the regression model by changing the independent variables. The t-statistic value could be compared with the critical t-value which is not available.
The reported Probability is the p-value, or marginal