





Hi,
when I run CAR or SAR model (yield~temperature+precipitation)and I get a small p value,(SAR and CAR output given below) I reject null hypothesis concluding that
 there is significant spatial correlation in the residuals.
 What does this mean in terms of our actual data and variables(yield, temperature and
 precipitation)? 
 Could you please clarify me? (my question is:what does this output say, in terms of the data? how can i explain this output to general audience?)
 #SAR sar<-spautolm(yield~Temp+precept, data=data, listw=ND_queenW) summary(sar)
Call: spautolm(formula = yield ~ Temp + precept, data = data, listw = ND_queenW)
Residuals:       Min         1Q     Median         3Q        Max -0.6434199 -0.1417016 -0.0043398  0.1244232  0.3593251 
Coefficients:               Estimate Std. Error z value  Pr(>|z|)(Intercept) -3.7799871  1.2123430 -3.1179  0.001821Temp         0.1692466  0.0819171  2.0661  0.038822precept      0.0128527  0.0018602  6.9094 4.867e-12
Lambda: 0.16053 LR test value: 34.382 p-value: 4.5299e-09 
Log likelihood: 1.776498 ML residual variance (sigma squared): 0.044187, (sigma: 0.21021)Number of observations: 53 Number of parameters estimated: 5 AIC: 6.447
> #CAR> car<-spautolm(yield~Temp+precept, data=data, family="CAR", listw=ND_queenW)> summary(car)
Call: spautolm(formula = yield ~ Temp + precept, data = data, listw = ND_queenW,     family = "CAR")
Residuals:        Min          1Q      Median          3Q         Max -0.64028193 -0.13032199  0.00078585  0.13230058  0.42924072 
Coefficients:               Estimate Std. Error z value  Pr(>|z|)(Intercept) -3.5452794  1.0694730 -3.3150 0.0009165Temp         0.1360763  0.0757529  1.7963 0.0724437precept      0.0136418  0.0016601  8.2175  2.22e-16
Lambda: 0.17855 LR test value: 32.233 p-value: 1.3674e-08 
Log likelihood: 0.7022934 ML residual variance (sigma squared): 0.047052, (sigma: 0.21691)Number of observations: 53 Number of parameters estimated: 5 AIC: 8.5954

 		 	   		   		 	   		  
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