Call:
lm(formula = LYL ~ LKL)

Residuals:
      Min        1Q    Median        3Q       Max 
-0.061408 -0.021241 -0.004331  0.023106  0.065288 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) 0.026196   0.004441   5.899 2.95e-07 ***
LKL         0.440466   0.023333  18.878  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.03222 on 51 degrees of freedom
Multiple R-squared:  0.8748,    Adjusted R-squared:  0.8723 
F-statistic: 356.4 on 1 and 51 DF,  p-value: < 2.2e-16

> 
> # 誤差項の仮定をテストする統計量
> fv <- result$fitted.values
> bptest(result,~fv**2)

        studentized Breusch-Pagan test

data:  result
BP = 5.8522, df = 1, p-value = 0.01556

> dwtest(result)

        Durbin-Watson test

data:  result
DW = 1.8724, p-value = 0.2707
alternative hypothesis: true autocorrelation is greater than 0

> e <- residuals(result)
> jarque.bera.test(e)

        Jarque Bera Test

data:  e
X-squared = 1.1973, df = 2, p-value = 0.5496

> reset(result)

        RESET test

data:  result
RESET = 0.374, df1 = 2, df2 = 49, p-value = 0.6899

> logLik(result)
'log Lik.' 107.8825 (df=3)
> AIC(result)
[1] -209.765
> 
>