> wine.mod1 <- lm(wine ~ adultpop)
> summary(wine.mod1)

Call:
lm(formula = wine ~ adultpop)

Residuals:
    Min      1Q  Median      3Q     Max 
-84.362 -38.334  -2.864  19.558 124.785 

Coefficients:
             Estimate Std. Error t value Pr(>|t|)    
(Intercept) -347.9736    21.9895  -15.82   <2e-16 ***
adultpop       4.3092     0.1417   30.40   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 48.64 on 67 degrees of freedom
Multiple R-squared: 0.9324,     Adjusted R-squared: 0.9314 
F-statistic: 924.2 on 1 and 67 DF,  p-value: < 2.2e-16 

> 
> library(lmtest)
> 
> dwtest(wine ~ adultpop)

        Durbin-Watson test

data:  wine ~ adultpop 
DW = 0.1199, p-value < 2.2e-16
alternative hypothesis: true autocorrelation is greater than 0 

> 
> library(nlme)
> 
> wine.mod2 <- gls(wine ~ adultpop, correlation=corAR1(form=~year), method='ML')
> summary(wine.mod2) 
Generalized least squares fit by maximum likelihood
  Model: wine ~ adultpop 
  Data: NULL 
       AIC      BIC    logLik
  591.7464 600.6828 -291.8732

Correlation Structure: AR(1)
 Formula: ~year 
 Parameter estimate(s):
      Phi 
0.9319506 

Coefficients:
                Value Std.Error   t-value p-value
(Intercept) -346.7972  71.60350 -4.843299       0
adultpop       4.2528   0.44049  9.654536       0

 Correlation: 
         (Intr)
adultpop -0.938

Standardized residuals:
        Min          Q1         Med          Q3         Max 
-1.63310579 -0.64323267  0.04944724  0.60053052  2.96796652 

Residual standard error: 45.19112 
Degrees of freedom: 69 total; 67 residual