Leydi Lisbeth Belizario Yanqui

file.choose()

[1] "C:\\Users\\Lisbeth\\Downloads\\EnsambleRev.xlsx"

ruta_Ensamble <-"C:\\Users\\Lisbeth\\Downloads\\EnsambleRev.xlsx"

excel_sheets(ruta_Ensamble)

[1] "Hoja1"

casoDBCA1<-read_excel(ruta_Ensamble)

print(head(casoDBCA1))

View(casoDBCA1)

attach(casoDBCA1)

names(casoDBCA1)

[1] "METODO"       "OPERADORES"   "TIEMPO (min)"

summary(casoDBCA1)

    METODO           OPERADORES         TIEMPO (min)
 Length:16          Length:16          Min.   : 6   
 Class :character   Class :character   1st Qu.: 8   
 Mode  :character   Mode  :character   Median :10   
                                       Mean   :10   
                                       3rd Qu.:11   
                                       Max.   :16   

str(casoDBCA1)

tibble [16 × 3] (S3: tbl_df/tbl/data.frame)
 $ METODO      : chr [1:16] "A" "B" "C" "D" ...
 $ OPERADORES  : chr [1:16] "OP1" "OP1" "OP1" "OP1" ...
 $ TIEMPO (min): num [1:16] 6 7 10 10 9 10 16 13 7 11 ...

print(head(casoDBCA1))

MET<-factor(casoDBCA1$METODO)

OPER <-factor(casoDBCA1$OPERADORES)

TIEM<-as.vector(casoDBCA1$`TIEMPO (min)`)

TIEM1<-as.numeric(TIEM)

par(mfrow=c(1,1))

boxplot(split(TIEM1,MET),xlab="Metodo", ylab="tiempo")

resaov<-aov(TIEM1 ~ OPER + MET)

ggplot(resaov, aes(MET, TIEM, fill=MET, color=TIEM)) + geom_boxplot() +  geom_jitter() + theme(legend.position = "none")  + geom_point(color = 'red', fill = 'red', size = 5, shape = 18, alpha = 0.5) + geom_jitter(size = 2, color = 'gray', alpha = 0.8)    + geom_boxplot() + theme_bw()

anova(resaov)

Analysis of Variance Table

Response: TIEM1
          Df Sum Sq Mean Sq F value   Pr(>F)   
OPER       3   28.5     9.5    4.75 0.029846 * 
MET        3   61.5    20.5   10.25 0.002919 **
Residuals  9   18.0     2.0                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

cv.model(resaov)

[1] 14.14214

euc.lm <- lm(TIEM1 ~ OPER + MET)

anova(euc.lm , test="F")

Analysis of Variance Table

Response: TIEM1
          Df Sum Sq Mean Sq F value   Pr(>F)   
OPER       3   28.5     9.5    4.75 0.029846 * 
MET        3   61.5    20.5   10.25 0.002919 **
Residuals  9   18.0     2.0                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Grafica <- interactionMeans(euc.lm)
plot(Grafica)
par(mfrow=c(1,1))

shapiro.test(euc.lm$res)

    Shapiro-Wilk normality test

data:  euc.lm$res
W = 0.97299, p-value = 0.8844

qqPlot(resaov)

[1] 10 11

fitb <- fitted(resaov)

res_stb <- rstandard(resaov)

plot(fitb,res_stb,xlab="Valores predichos", ylab="Residuos estandarizados",abline(h=0))

bartlett.test(TIEM1 ~ MET)

    Bartlett test of homogeneity of variances

data:  TIEM1 by MET
Bartlett's K-squared = 1.5989, df = 3, p-value = 0.6596

leveneTest(TIEM1 ~ MET, center = "median")

Levene's Test for Homogeneity of Variance (center = "median")
      Df F value Pr(>F)
group  3  1.8667  0.189
      12