LEYDI LISBETH BELIZARIO YANQUI

file.choose()

[1] "C:\\Users\\HP\\Downloads\\Diseño exoerimental\\Propulsora.xlsx"

ruta_propulsora <- "C:\\Users\\HP\\Downloads\\Diseño exoerimental\\Propulsora.xlsx"

excel_sheets(ruta_propulsora)

[1] "Hoja1"

Propul<-read_excel(ruta_propulsora)

print(head(Propul))

NA

view(Propul)

attach(Propul)

The following objects are masked from Propul (pos = 7):

    Formulacion, Lote, Operador, Rapidez

The following objects are masked from MoGL (pos = 8):

    Formulacion, Lote, Operador, Rapidez

The following objects are masked from MoGL (pos = 9):

    Formulacion, Lote, Operador, Rapidez

The following objects are masked from MoGL (pos = 10):

    Formulacion, Lote, Operador, Rapidez

The following objects are masked from Propul (pos = 12):

    Formulacion, Lote, Operador, Rapidez

names(Propul)

[1] "Rapidez"     "Formulacion" "Lote"        "Operador"   

summary(Propul)

    Rapidez     Formulacion             Lote      Operador
 Min.   :-8.0   Length:25          Min.   :1   Min.   :1  
 1st Qu.:-3.0   Class :character   1st Qu.:2   1st Qu.:2  
 Median :-1.0   Mode  :character   Median :3   Median :3  
 Mean   : 0.4                      Mean   :3   Mean   :3  
 3rd Qu.: 4.0                      3rd Qu.:4   3rd Qu.:4  
 Max.   :13.0                      Max.   :5   Max.   :5  

str(Propul)

tibble [25 × 4] (S3: tbl_df/tbl/data.frame)
 $ Rapidez    : num [1:25] -1 -8 -7 1 -3 -5 -1 13 6 5 ...
 $ Formulacion: chr [1:25] "A" "B" "C" "D" ...
 $ Lote       : num [1:25] 1 2 3 4 5 1 2 3 4 5 ...
 $ Operador   : num [1:25] 1 1 1 1 1 2 2 2 2 2 ...

OPERA <- factor(Propul$Operador) 

LOT <- factor(Propul$Lote)

FORM<- factor(Propul$Formulacion)

RAPI <-as.vector(Propul$Rapidez)

RAPI1<-as.numeric(RAPI)

par(mfrow=c(1,1))

boxplot(split(RAPI1,OPERA),xlab="Operacion", ylab="Rapidez") 

prop.aov<-aov(RAPI1 ~ OPERA+LOT+FORM)

anova(prop.aov)

Analysis of Variance Table

Response: RAPI1
          Df Sum Sq Mean Sq F value   Pr(>F)   
OPERA      4    150  37.500  3.5156 0.040373 * 
LOT        4     68  17.000  1.5938 0.239059   
FORM       4    330  82.500  7.7344 0.002537 **
Residuals 12    128  10.667                    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

file.choose()

[1] "C:\\Users\\HP\\Downloads\\OPERADORES 1.3.xlsx"

ruta_montajeGL <- "C:\\Users\\HP\\Downloads\\OPERADORES 1.3.xlsx"

excel_sheets(ruta_montajeGL)

[1] "Hoja1"

MoGL<-read_excel(ruta_montajeGL)

print(head(MoGL))

NA

view(MoGL)

attach(MoGL)

names(MoGL)

[1] "RAPIDEZ"     "FORMULACION" "LOTE"        "OPERADOR"    "Montaje"    

summary(MoGL)

    RAPIDEZ        FORMULACION             LOTE          OPERADOR       Montaje         
 Min.   :-8.0000   Length:24          Min.   :1.000   Min.   :1.000   Length:24         
 1st Qu.:-3.2500   Class :character   1st Qu.:2.000   1st Qu.:2.000   Class :character  
 Median : 0.0000   Mode  :character   Median :3.000   Median :3.000   Mode  :character  
 Mean   : 0.5417                      Mean   :2.917   Mean   :2.917                     
 3rd Qu.: 4.2500                      3rd Qu.:4.000   3rd Qu.:4.000                     
 Max.   :13.0000                      Max.   :5.000   Max.   :5.000                     

str(MoGL)

tibble [24 × 5] (S3: tbl_df/tbl/data.frame)
 $ RAPIDEZ    : num [1:24] -1 -8 -7 1 -3 -5 -1 13 6 5 ...
 $ FORMULACION: chr [1:24] "A" "B" "C" "D" ...
 $ LOTE       : num [1:24] 1 2 3 4 5 1 2 3 4 5 ...
 $ OPERADOR   : num [1:24] 1 1 1 1 1 2 2 2 2 2 ...
 $ Montaje    : chr [1:24] "b" "c" "d" "e" ...

OP <- factor(MoGL$OPERADOR) 

MO <- factor(MoGL$Montaje)

LO<-factor(MoGL$LOTE)

FO<-factor(MoGL$FORMULACION)

RA <-as.vector(MoGL$RAPIDEZ)

RA1<-as.numeric(RA)

par(mfrow=c(1,1))

boxplot(split(RA1,OP),xlab="OPERADOR", ylab="RAPIDEZ")

Ra.aov<-aov(RA1 ~ OP+MO+LO+FO)

anova(Ra.aov)

Analysis of Variance Table

Response: RA1
          Df Sum Sq Mean Sq F value Pr(>F)
OP         4 166.76  41.690  1.2976 0.3297
MO         4  61.05  15.263  0.4751 0.7535
LO         4  82.75  20.687  0.6439 0.6425
Residuals 11 353.40  32.127               

ggplot(Ra.aov, aes(OP, MO, LO, FO, fill=OP, MO, LO, FO, color=MO, LO)) + 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()

Warning: Duplicated aesthetics after name standardisation:

Form <- ggplot(MoGL, aes(x = FO, y = RA, fill=FO)) +
 geom_boxplot() + theme(legend.position = "none")

Lot <- ggplot(MoGL, aes(x = LO, y = RA, fill=LO)) +
 geom_boxplot() + theme(legend.position = "none")

Oper <- ggplot(MoGL, aes(x = OP, y = RA, fill=OP)) +
 geom_boxplot() + theme(legend.position = "none")

grid.arrange(Form,Lot,Oper, nrow=2,ncol=2)

cv.model(Ra.aov)

[1] 1046.417

Rapide.lm <- lm(RA1 ~ FO+LO+OP+MO)

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

Analysis of Variance Table

Response: RA1
          Df Sum Sq Mean Sq F value Pr(>F)
FO         4  82.41  20.602  0.6413 0.6441
OP         4 166.55  41.638  1.2960 0.3303
MO         4  61.60  15.400  0.4793 0.7506
Residuals 11 353.40  32.127               

Grafica <- interactionMeans(Rapide.lm)

Error in interactionMeans(Rapide.lm) : 
  could not find function "interactionMeans"

shapiro.test(Rapide.lm$res)

    Shapiro-Wilk normality test

data:  Rapide.lm$res
W = 0.95779, p-value = 0.3956

qqPlot(Ra.aov)

[1] 19 23

TukeyHSD(Ra.aov,conf.level = 0.95)

  Tukey multiple comparisons of means
    95% family-wise confidence level

Fit: aov(formula = RA1 ~ OP + MO + LO + FO)

$OP
    diff        lwr       upr     p adj
2-1  7.2  -4.393371 18.793371 0.3228992
3-1  2.8  -8.793371 14.393371 0.9309125
4-1  4.6  -6.993371 16.193371 0.7061315
5-1  6.6  -5.696627 18.896627 0.4528828
3-2 -4.4 -15.993371  7.193371 0.7368548
4-2 -2.6 -14.193371  8.993371 0.9461093
5-2 -0.6 -12.896627 11.696627 0.9998366
4-3  1.8  -9.793371 13.393371 0.9854464
5-3  3.8  -8.496627 16.096627 0.8503403
5-4  2.0 -10.296627 14.296627 0.9827318

$MO
     diff        lwr       upr     p adj
c-b -1.40 -12.993371 10.193371 0.9943395
d-b -3.66 -15.956627  8.636627 0.8661526
e-b -2.80 -14.393371  8.793371 0.9309125
f-b  0.60 -10.993371 12.193371 0.9997936
d-c -2.26 -14.556627 10.036627 0.9731071
e-c -1.40 -12.993371 10.193371 0.9943395
f-c  2.00  -9.593371 13.593371 0.9785946
e-d  0.86 -11.436627 13.156627 0.9993216
f-d  4.26  -8.036627 16.556627 0.7930299
f-e  3.40  -8.193371 14.993371 0.8719996

$LO
     diff        lwr      upr     p adj
2-1  4.60  -6.993371 16.19337 0.7061315
3-1  3.80  -7.793371 15.39337 0.8225786
4-1  5.20  -6.393371 16.79337 0.6110294
5-1  2.95  -9.346627 15.24663 0.9324301
3-2 -0.80 -12.393371 10.79337 0.9993565
4-2  0.60 -10.993371 12.19337 0.9997936
5-2 -1.65 -13.946627 10.64663 0.9915588
4-3  1.40 -10.193371 12.99337 0.9943395
5-3 -0.85 -13.146627 11.44663 0.9993521
5-4 -2.25 -14.546627 10.04663 0.9735308

plot(TukeyHSD(Ra.aov))

plot(TukeyHSD (Ra.aov, conf.level = 0.95 ), las = 2.9 )

NA

LSD.test(y = Ra.aov, trt = "MO" ,group = T, console = T)

Study: Ra.aov ~ "MO"

LSD t Test for RA1 

Mean Square Error:  32.12727 

MO,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 11
Critical Value of t: 2.200985 

Groups according to probability of means differences and alpha level( 0.05 )

Treatments with the same letter are not significantly different.

LSD.test(Ra.aov,"FO",console=TRUE)

Study: Ra.aov ~ "FO"

LSD t Test for RA1 

Mean Square Error:  32.12727 

FO,  means and individual ( 95 %) CI

Alpha: 0.05 ; DF Error: 11
Critical Value of t: 2.200985 

Groups according to probability of means differences and alpha level( 0.05 )

Treatments with the same letter are not significantly different.

HSD.test(Ra.aov, "FO",console=TRUE)

Study: Ra.aov ~ "FO"

HSD Test for RA1 

Mean Square Error:  32.12727 

FO,  means

Alpha: 0.05 ; DF Error: 11 
Critical Value of Studentized Range: 4.573596 

Groups according to probability of means differences and alpha level( 0.05 )

Treatments with the same letter are not significantly different.

SNK.test(Ra.aov, "FO",console=TRUE)

Study: Ra.aov ~ "FO"

Student Newman Keuls Test
for RA1 

Mean Square Error:  32.12727 

FO,  means

Groups according to probability of means differences and alpha level( 0.05 )

Means with the same letter are not significantly different.

scheffe.test(Ra.aov, "FO",console=TRUE)

Study: Ra.aov ~ "FO"

Scheffe Test for RA1 

Mean Square Error  : 32.12727 

FO,  means

Alpha: 0.05 ; DF Error: 11 
Critical Value of F: 3.35669 

Groups according to probability of means differences and alpha level( 0.05 )

Means with the same letter are not significantly different.

duncan.test(Ra.aov, "FO",console=TRUE)

Study: Ra.aov ~ "FO"

Duncan's new multiple range test
for RA1 

Mean Square Error:  32.12727 

FO,  means

Groups according to probability of means differences and alpha level( 0.05 )

Means with the same letter are not significantly different.

shapiro.test(Ra.aov$res) 

    Shapiro-Wilk normality test

data:  Ra.aov$res
W = 0.95779, p-value = 0.3956

summary(Ra.aov$residuals)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  -8.30   -2.60    1.25    0.00    2.15    6.80