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summary(cars)
##      speed           dist       
##  Min.   : 4.0   Min.   :  2.00  
##  1st Qu.:12.0   1st Qu.: 26.00  
##  Median :15.0   Median : 36.00  
##  Mean   :15.4   Mean   : 42.98  
##  3rd Qu.:19.0   3rd Qu.: 56.00  
##  Max.   :25.0   Max.   :120.00

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Y=c(14.823,
    14.676,
    14.72,
    14.5141,
    15.065,
    25.151,
    25.401,
    25.131,
    25.031,
    25.267,
    32.605,
    32.46,
    32.256,
    32.669,
    32.111
    
)
trat=rep(c("trat1","trat2","trat3"),each=5)

df<-data.frame(trat=trat,Y=Y)

modelo<-aov(Y~trat,data=df)
summary(modelo)
##             Df Sum Sq Mean Sq F value Pr(>F)    
## trat         2  788.3   394.2   10132 <2e-16 ***
## Residuals   12    0.5     0.0                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(Y~trat,data=df)

h<-TukeyHSD(modelo)
h
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Y ~ trat, data = df)
## 
## $trat
##                 diff      lwr      upr p adj
## trat2-trat1 10.43658 10.10377 10.76939     0
## trat3-trat1 17.66058 17.32777 17.99339     0
## trat3-trat2  7.22400  6.89119  7.55681     0
plot(h)

res<-modelo$residuals
qqnorm(res)
qqline(res)

shapiro.test(res)
## 
##  Shapiro-Wilk normality test
## 
## data:  res
## W = 0.97219, p-value = 0.8891