The Null Hypothesis - H0: There is autocorrelation
The Alternative Hypothesis - H1: There is no autocorrelation
Rejection Criteria: Reject H0 (n-s)R^{2} > = (1515 - 4) x (0.01) = 15.11 > 9.49 ()
1515 cases used, 4 cases contain missing values
Since 15.11 > 9.49 the chi-squared value with 4 lags (ET-1, ET-2, ET-3, and ET-4) there is evidence to suggest that we reject H0 meaning that there is no autocorrelation.
The regression equation is
RESI1 = - 0.0011 + 0.000005 totexp - 0.000001 income + 0.000017 age + 0.00007 nk
+ 0.0085 ET-1 + 0.0070 ET-2 - 0.0284 ET-3 - 0.0074 ET-4
Predictor Coef SE Coef T P
Constant -0.00105 0.01375 -0.08 0.939
totexp 0.00000471 0.00006080 0.08 0.938
income -0.00000082 0.00004314 -0.02 0.985
age 0.0000167 0.0003090 0.05 0.957
nk 0.000071 0.004785 0.01 0.988
ET-1 0.00847 0.02580 0.33 0.743
ET-2 0.00700 0.02584 0.27 0.786
ET-3 -0.02842 0.02587 -1.10 0.272
ET-4 -0.00743 0.02592 -0.29 0.774
As the T value decreases, the P value increases which is noticeable above due to the inclusions of lags. Most of the T values are now closer to 0 which shows that there is less reliability of the coefficient. ET-3 will be included in a further regression analysis as it is significant with a value of -1.10, conversely ET-1, ET-2, ET-4 will be removed as they are insignificant with low T values.
S = 0.0905514 R-Sq = 0.1% R-Sq(adj) = 0.0%
The inclusion of lags has caused the r-squared to be really low at 0.1% which certainly suggests that the model is inadequate for explaining the Y variable. It also indicates that data points are distributed away from the line of best fit and that the independent variables are poor predictors for the dependent variable. The remaining percentage (99.9%) is the variation which is unknown.
Analysis of Variance
Source DF SS MS F P
Regression 8 0.012127 0.001516 0.18 0.993
Residual Error 1506 12.348529 0.008200
Total 1514 12.360656
Source DF Seq SS
totexp 1 0.000029
income 1 0.000005
age 1 0.000011
nk 1 0.000000
ET-1 1 0.000903
ET-2 1 0.000544
ET-3 1 0.009961
ET-4 1 0.000673
Since the F value is small at 0.18 and the P value is high 0.993 it reveals that there is no relationship between the Y dependent variable and X independent variables. This indicates that as it is 0.18 it does not support the model and therefore the slopes are equal to 0.