The decomposition of the total variation in y in to a part that is explained by the regression model and a part that is unexplained allows us to define a measure, coefficient of determination (R2). That is the proportion of variation in y
esplained by x within the regression model.
R2 = SSR = 1 - SSE
SST
SST
The closer to 1, the closer the sample values yi are to the fitted regression equation y�i = b1 + b2xi . If R2 = 1, then all the sample data fall exactly on the fitted least squares line, so SSE = 0 and the model fits the data perfectly. If
they are uncorrelated and show no linear association, then the least squares fitted line is identical to y , so that SSR = 0 and R2 = 0. When 0 < R2 < 1, means that the proportion of the variation in y about its mean that is explained by the regression model.
- Durbin-Watson (DW) Statistic
Durbin–Watson statistic is a test statistic used to detect the presence of autocorrelation (a relationship between values separated from each other by a given time lag) in the residuals (prediction errors) from a regression analysis. The hypotheses usually considered in the Durbin-Watson test are:
HO p = 0 Ha p = 1
with the test statistic:
∑n (ei - ei-1)2
i=2
i=2
ei 2
Where is the number of observations, d = 2 indicates no autocorrelation. The value of d always lies between 0 and 4. If the Durbin–Watson