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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

Including Plots

desgaste<-c(264,260,258,241,262,255,208,220,216,200,213,206,220,263,219,225,230,228,217,226,215,227,220,222)
tipoCuero<-c(rep("A",6),rep("B",6),rep("C",6),rep("D",6))
datos<-data.frame(tipoCuero=tipoCuero,desgaste=desgaste)
datos
##    tipoCuero desgaste
## 1          A      264
## 2          A      260
## 3          A      258
## 4          A      241
## 5          A      262
## 6          A      255
## 7          B      208
## 8          B      220
## 9          B      216
## 10         B      200
## 11         B      213
## 12         B      206
## 13         C      220
## 14         C      263
## 15         C      219
## 16         C      225
## 17         C      230
## 18         C      228
## 19         D      217
## 20         D      226
## 21         D      215
## 22         D      227
## 23         D      220
## 24         D      222
desgaste<-c(264, 260, 258, 241, 262, 255,208, 220, 216, 200, 213, 206,220, 263, 219, 225, 230, 228,217, 226, 215, 227, 220, 222)
tipoCuero<-c(rep("A",6),rep("B",6),rep("C",6),rep("D",6))
datos<-data.frame(tipoCuero=tipoCuero,desgaste=desgaste)
datos
##    tipoCuero desgaste
## 1          A      264
## 2          A      260
## 3          A      258
## 4          A      241
## 5          A      262
## 6          A      255
## 7          B      208
## 8          B      220
## 9          B      216
## 10         B      200
## 11         B      213
## 12         B      206
## 13         C      220
## 14         C      263
## 15         C      219
## 16         C      225
## 17         C      230
## 18         C      228
## 19         D      217
## 20         D      226
## 21         D      215
## 22         D      227
## 23         D      220
## 24         D      222
datos$tipoCuero<-factor(datos$tipoCuero)
modelo<-aov(desgaste~tipoCuero,data=datos)
summary(modelo)
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## tipoCuero    3   7019  2339.8   22.75 1.18e-06 ***
## Residuals   20   2056   102.8                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
boxplot(desgaste~tipoCuero,data=datos,col=c("green","orange","red","blue"))

plot(modelo)

shapiro.test(modelo$residuals)
## 
##  Shapiro-Wilk normality test
## 
## data:  modelo$residuals
## W = 0.88326, p-value = 0.00967

Note that theecho = FALSE` parameter was added to the code chunk to prevent printing of the R code that generated the plot.

hist(modelo$residuals,breaks=5)

TukeyHSD(modelo)
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = desgaste ~ tipoCuero, data = datos)
## 
## $tipoCuero
##           diff        lwr        upr     p adj
## B-A -46.166667 -62.552998 -29.780336 0.0000008
## C-A -25.833333 -42.219664  -9.447002 0.0014117
## D-A -35.500000 -51.886331 -19.113669 0.0000349
## C-B  20.333333   3.947002  36.719664 0.0118160
## D-B  10.666667  -5.719664  27.052998 0.2926431
## D-C  -9.666667 -26.052998   6.719664 0.3742863

##Regresion Lineal

x1<-seq(0,10,0.5)
y<-0.8+2.3*x1
plot(x1,y)

x1<-seq(0,10,lenght=10)
## Warning: In seq.default(0, 10, lenght = 10) :
##  extra argument 'lenght' will be disregarded
y<-0.8+2.3*x1
y<-y+rnorm(10,0,1.5)
## Warning in y + rnorm(10, 0, 1.5): longitud de objeto mayor no es múltiplo de la
## longitud de uno menor
plot(x1,y)

modelo<-lm(y~x1)
summary(modelo)
## 
## Call:
## lm(formula = y ~ x1)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -1.8306 -1.0975 -0.3511  1.0105  3.0256 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)  -0.5228     0.9072  -0.576    0.579    
## x1            2.5373     0.1533  16.546  4.8e-08 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 1.608 on 9 degrees of freedom
## Multiple R-squared:  0.9682, Adjusted R-squared:  0.9646 
## F-statistic: 273.8 on 1 and 9 DF,  p-value: 4.798e-08
yajustado<-modelo$fitted.values
plot(x1,y)
lines(x1,yajustado,col="red",lwd=2)