----------------------------------
Modelo: Peso.gr~Ancho.cm 
Tabla ANOVA 
Analysis of Variance Table

Response: Peso.gr
           Df Sum Sq Mean Sq F value    Pr(>F)    
Ancho.cm    1  43512   43512  1219.9 < 2.2e-16 ***
Residuals 191   6813      36                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
Estimación de parámetros 

Call:
lm(formula = formula, data = datos)

Residuals:
     Min       1Q   Median       3Q      Max 
-33.9157  -3.1902  -0.5856   2.0997  25.5580 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -59.293      2.489  -23.82   <2e-16 ***
Ancho.cm      28.082      0.804   34.93   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 5.972 on 191 degrees of freedom
Multiple R-squared: 0.8646,	Adjusted R-squared: 0.8639 
F-statistic:  1220 on 1 and 191 DF,  p-value: < 2.2e-16 

----------------------------------
----------------------------------
Modelo: Peso.gr~log(Ancho.cm) 
Tabla ANOVA 
Analysis of Variance Table

Response: Peso.gr
               Df Sum Sq Mean Sq F value    Pr(>F)    
log(Ancho.cm)   1  40899   40899  828.75 < 2.2e-16 ***
Residuals     191   9426      49                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
Estimación de parámetros 

Call:
lm(formula = formula, data = datos)

Residuals:
    Min      1Q  Median      3Q     Max 
-34.019  -4.042  -1.283   2.656  31.149 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    -61.579      3.095  -19.90   <2e-16 ***
log(Ancho.cm)   80.009      2.779   28.79   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 7.025 on 191 degrees of freedom
Multiple R-squared: 0.8127,	Adjusted R-squared: 0.8117 
F-statistic: 828.7 on 1 and 191 DF,  p-value: < 2.2e-16 

----------------------------------
----------------------------------
Modelo: log(Peso.gr)~log(Ancho.cm) 
Tabla ANOVA 
Analysis of Variance Table

Response: log(Peso.gr)
               Df Sum Sq Mean Sq F value    Pr(>F)    
log(Ancho.cm)   1 89.818  89.818   706.1 < 2.2e-16 ***
Residuals     191 24.296   0.127                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
Estimación de parámetros 

Call:
lm(formula = formula, data = datos)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.75886 -0.06238  0.03348  0.10897  0.40018 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)    -1.0780     0.1571   -6.86 9.32e-11 ***
log(Ancho.cm)   3.7494     0.1411   26.57  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 0.3567 on 191 degrees of freedom
Multiple R-squared: 0.7871,	Adjusted R-squared: 0.786 
F-statistic: 706.1 on 1 and 191 DF,  p-value: < 2.2e-16 

----------------------------------
----------------------------------
Modelo: log(Peso.gr)~Ancho.cm 
Tabla ANOVA 
Analysis of Variance Table

Response: log(Peso.gr)
           Df Sum Sq Mean Sq F value    Pr(>F)    
Ancho.cm    1 88.227  88.227  650.98 < 2.2e-16 ***
Residuals 191 25.886   0.136                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
Estimación de parámetros 

Call:
lm(formula = formula, data = datos)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.74632 -0.07972  0.04919  0.14053  0.38674 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept) -0.81389    0.15341  -5.305  3.1e-07 ***
Ancho.cm     1.26450    0.04956  25.514  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 0.3681 on 191 degrees of freedom
Multiple R-squared: 0.7732,	Adjusted R-squared: 0.772 
F-statistic:   651 on 1 and 191 DF,  p-value: < 2.2e-16 

----------------------------------
----------------------------------
Modelo: log(Peso.gr)~I(1/Ancho.cm) 
Tabla ANOVA 
Analysis of Variance Table

Response: log(Peso.gr)
               Df Sum Sq Mean Sq F value    Pr(>F)    
I(1/Ancho.cm)   1 89.419  89.419   691.6 < 2.2e-16 ***
Residuals     191 24.695   0.129                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
Estimación de parámetros 

Call:
lm(formula = formula, data = datos)

Residuals:
     Min       1Q   Median       3Q      Max 
-3.79894 -0.05957  0.02442  0.10070  0.38582 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)     6.6307     0.1389   47.73   <2e-16 ***
I(1/Ancho.cm) -10.5873     0.4026  -26.30   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 0.3596 on 191 degrees of freedom
Multiple R-squared: 0.7836,	Adjusted R-squared: 0.7825 
F-statistic: 691.6 on 1 and 191 DF,  p-value: < 2.2e-16 

----------------------------------
----------------------------------
Modelo: Peso.gr~Ancho.cm+I(Ancho.cm^2) 
Tabla ANOVA 
Analysis of Variance Table

Response: Peso.gr
               Df Sum Sq Mean Sq F value    Pr(>F)    
Ancho.cm        1  43512   43512 1985.35 < 2.2e-16 ***
I(Ancho.cm^2)   1   2648    2648  120.84 < 2.2e-16 ***
Residuals     190   4164      22                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
Estimación de parámetros 

Call:
lm(formula = formula, data = datos)

Residuals:
      Min        1Q    Median        3Q       Max 
-32.33583  -1.59543  -0.04040   1.61034  16.74769 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)     39.107      9.162   4.269 3.10e-05 ***
Ancho.cm       -38.923      6.128  -6.352 1.53e-09 ***
I(Ancho.cm^2)   11.051      1.005  10.993  < 2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 4.682 on 190 degrees of freedom
Multiple R-squared: 0.9173,	Adjusted R-squared: 0.9164 
F-statistic:  1053 on 2 and 190 DF,  p-value: < 2.2e-16 

----------------------------------
----------------------------------
Modelo: Peso.gr~I(1/Ancho.cm) 
Tabla ANOVA 
Analysis of Variance Table

Response: Peso.gr
               Df Sum Sq Mean Sq F value    Pr(>F)    
I(1/Ancho.cm)   1  37591   37591  563.86 < 2.2e-16 ***
Residuals     191  12734      67                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 
Estimación de parámetros 

Call:
lm(formula = formula, data = datos)

Residuals:
    Min      1Q  Median      3Q     Max 
-33.708  -4.842  -2.028   2.860  36.293 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)     99.917      3.154   31.68   <2e-16 ***
I(1/Ancho.cm) -217.076      9.142  -23.75   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 8.165 on 191 degrees of freedom
Multiple R-squared: 0.747,	Adjusted R-squared: 0.7456 
F-statistic: 563.9 on 1 and 191 DF,  p-value: < 2.2e-16 

----------------------------------
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[[3]]
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[[4]]
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[[5]]
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[[6]]
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[[7]]
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[[1]]

	Shapiro-Wilk normality test

data:  residuos$"1" 
W = 0.881, p-value = 3.184e-11


[[2]]

	Lilliefors (Kolmogorov-Smirnov) normality test

data:  residuos$"1" 
D = 0.1224, p-value = 1.935e-07


[[1]]

	Shapiro-Wilk normality test

data:  residuos$"2" 
W = 0.8732, p-value = 1.186e-11


[[2]]

	Lilliefors (Kolmogorov-Smirnov) normality test

data:  residuos$"2" 
D = 0.1276, p-value = 4.099e-08


[[1]]

	Shapiro-Wilk normality test

data:  residuos$"3" 
W = 0.3697, p-value < 2.2e-16


[[2]]

	Lilliefors (Kolmogorov-Smirnov) normality test

data:  residuos$"3" 
D = 0.2517, p-value < 2.2e-16


[[1]]

	Shapiro-Wilk normality test

data:  residuos$"4" 
W = 0.446, p-value < 2.2e-16


[[2]]

	Lilliefors (Kolmogorov-Smirnov) normality test

data:  residuos$"4" 
D = 0.2028, p-value < 2.2e-16


[[1]]

	Shapiro-Wilk normality test

data:  residuos$"5" 
W = 0.3693, p-value < 2.2e-16


[[2]]

	Lilliefors (Kolmogorov-Smirnov) normality test

data:  residuos$"5" 
D = 0.2661, p-value < 2.2e-16


[[1]]

	Shapiro-Wilk normality test

data:  residuos$"6" 
W = 0.8571, p-value = 1.763e-12


[[2]]

	Lilliefors (Kolmogorov-Smirnov) normality test

data:  residuos$"6" 
D = 0.1355, p-value = 3.248e-09


[[1]]

	Shapiro-Wilk normality test

data:  residuos$"7" 
W = 0.8603, p-value = 2.546e-12


[[2]]

	Lilliefors (Kolmogorov-Smirnov) normality test

data:  residuos$"7" 
D = 0.1432, p-value = 2.41e-10


$`1`
 lag Autocorrelation D-W Statistic p-value
   1       0.1398588      1.710943   0.032
 Alternative hypothesis: rho != 0

$`2`
 lag Autocorrelation D-W Statistic p-value
   1       0.2251169      1.543613   0.002
 Alternative hypothesis: rho != 0

$`3`
 lag Autocorrelation D-W Statistic p-value
   1      -0.0644059      2.125451   0.232
 Alternative hypothesis: rho != 0

$`4`
 lag Autocorrelation D-W Statistic p-value
   1     -0.05749208      2.111068   0.388
 Alternative hypothesis: rho != 0

$`5`
 lag Autocorrelation D-W Statistic p-value
   1     -0.04338615      2.083900   0.402
 Alternative hypothesis: rho != 0

$`6`
 lag Autocorrelation D-W Statistic p-value
   1       -0.082602      2.144473   0.376
 Alternative hypothesis: rho != 0

$`7`
 lag Autocorrelation D-W Statistic p-value
   1       0.2837379      1.428319       0
 Alternative hypothesis: rho != 0

$`1`

	studentized Breusch-Pagan test

data:  modelo 
BP = 11.7249, df = 1, p-value = 0.0006167


$`2`

	studentized Breusch-Pagan test

data:  modelo 
BP = 9.3605, df = 1, p-value = 0.002217


$`3`

	studentized Breusch-Pagan test

data:  modelo 
BP = 0.3766, df = 1, p-value = 0.5394


$`4`

	studentized Breusch-Pagan test

data:  modelo 
BP = 0.4745, df = 1, p-value = 0.4909


$`5`

	studentized Breusch-Pagan test

data:  modelo 
BP = 0.321, df = 1, p-value = 0.571


$`6`

	studentized Breusch-Pagan test

data:  modelo 
BP = 9.9718, df = 2, p-value = 0.006833


$`7`

	studentized Breusch-Pagan test

data:  modelo 
BP = 5.8929, df = 1, p-value = 0.01520


