#Data 1
dataa<-read.csv("D:\\Asisten MK STA512\\Praktikum 13b.csv",header=TRUE,sep = ";",
colClasses =c("factor","factor","numeric"))
str(dataa)
## 'data.frame': 12 obs. of 3 variables:
## $ Perlakuan: Factor w/ 4 levels "a","b","c","d": 1 1 1 2 2 2 3 3 3 4 ...
## $ Kelompok : Factor w/ 3 levels "1","2","3": 1 2 3 1 2 3 1 2 3 1 ...
## $ Respon : num 9.9 12.3 11.4 11.4 12.9 12.7 12.1 13.4 12.9 10.1 ...
model1<- aov(Respon ~ Perlakuan + Kelompok, data =dataa)
summary(model1)
## Df Sum Sq Mean Sq F value Pr(>F)
## Perlakuan 3 5.200 1.733 19.44 0.001713 **
## Kelompok 2 7.172 3.586 40.22 0.000335 ***
## Residuals 6 0.535 0.089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Orthogonal Contrasts
levels(dataa$Perlakuan)
## [1] "a" "b" "c" "d"
kontras<-cbind(c(3,-1,-1,-1),c(0,1,-1,0),c(0,1,1,-2))
model11<- aov(Respon ~ Perlakuan + Kelompok, contrasts = list(Perlakuan=kontras), data =dataa)
summary.aov(model11,split=list(Perlakuan=list("a vs b,c,d"=1,"b vs d"=2,"b,c vs d, "=3)))
## Df Sum Sq Mean Sq F value Pr(>F)
## Perlakuan 3 5.200 1.733 19.439 0.001713 **
## Perlakuan: a vs b,c,d 1 2.151 2.151 24.125 0.002679 **
## Perlakuan: b vs d 1 0.327 0.327 3.664 0.104123
## Perlakuan: b,c vs d, 1 2.722 2.722 30.530 0.001480 **
## Kelompok 2 7.172 3.586 40.215 0.000335 ***
## Residuals 6 0.535 0.089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Polinomial Contrast
model1<- aov(Respon ~ Perlakuan + Kelompok, data =dataa)
contrasts(dataa$Perlakuan) <- contr.poly(4)
summary.aov(model1, split= list(Perlakuan=list("Linear" = 1, "Kuadratik" = 2, "Kubik"=3)))
## Df Sum Sq Mean Sq F value Pr(>F)
## Perlakuan 3 5.200 1.733 19.439 0.001713 **
## Perlakuan: Linear 1 0.640 0.640 7.178 0.036580 *
## Perlakuan: Kuadratik 1 4.500 4.500 50.467 0.000391 ***
## Perlakuan: Kubik 1 0.060 0.060 0.673 0.443404
## Kelompok 2 7.172 3.586 40.215 0.000335 ***
## Residuals 6 0.535 0.089
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Warning: package 'readxl' was built under R version 4.1.2
data2<-read_excel("D:/Asisten MK STA512/Praktikum 13.xlsx", col_names =T, sheet="Data")
data2
## # A tibble: 27 x 3
## Perlakuan Kelompok Respon
## <chr> <dbl> <dbl>
## 1 kontrol 1 10.2
## 2 kontrol 2 9.26
## 3 kontrol 3 12.7
## 4 K2P1 1 32.0
## 5 K2P1 2 25.8
## 6 K2P1 3 19.7
## 7 K2P2 1 23.9
## 8 K2P2 2 22.0
## 9 K2P2 3 21.4
## 10 K2P3 1 17.2
## # ... with 17 more rows
## tibble [27 x 3] (S3: tbl_df/tbl/data.frame)
## $ Perlakuan: chr [1:27] "kontrol" "kontrol" "kontrol" "K2P1" ...
## $ Kelompok : num [1:27] 1 2 3 1 2 3 1 2 3 1 ...
## $ Respon : num [1:27] 10.19 9.26 12.73 32.02 25.76 ...
#View(data1)
respon=data2$Respon
kelompok=as.factor(data2$Kelompok)
perlakuan=as.factor(data2$Perlakuan)
Data1=data.frame(perlakuan,kelompok,respon)
plot(Data1$perlakuan,Data1$respon)
## [1] "K2P1" "K2P2" "K2P3" "K2P4" "K3P1" "K3P2" "K3P3"
## [8] "K3P4" "kontrol"
## [1] "1" "2" "3"
model2<- aov(respon ~ perlakuan + kelompok, data =Data1)
summary(model2)
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan 8 586.0 73.25 8.297 0.00019 ***
## kelompok 2 39.2 19.61 2.221 0.14090
## Residuals 16 141.3 8.83
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Orthogonal Contrasts
kontras2<-cbind(c(-1,-1,-1,-1,-1,-1,-1,-1,8),c(1,1,1,1,-1,-1,-1,-1,0),c(1,1,-1,-1,1,1,-1,-1,0))
model22<- aov(respon ~ perlakuan + kelompok, contrasts = list(perlakuan=kontras2), data =Data1)
summary.aov(model2,split=list(perlakuan=list("kontrol vs k2 k3"=1,"k2 vs k3"=2,"p1,p2 vs p3,p4"=3)))
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan 8 586.0 73.25 8.297 0.00019 ***
## perlakuan: kontrol vs k2 k3 1 111.1 111.08 12.582 0.00268 **
## perlakuan: k2 vs k3 1 8.7 8.70 0.985 0.33578
## perlakuan: p1,p2 vs p3,p4 1 44.3 44.27 5.015 0.03969 *
## kelompok 2 39.2 19.61 2.221 0.14090
## Residuals 16 141.3 8.83
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Polinomial Contrast
model2<- aov(respon ~ perlakuan + kelompok, data =Data1)
contrasts(Data1$perlakuan) <- contr.poly(9)
summary.aov(model2, split= list(perlakuan=list("Linear" = 1, "Kuadratik" = 2, "Kubik"=3, "Kuartik"=4)))
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan 8 586.0 73.25 8.297 0.00019 ***
## perlakuan: Linear 1 111.1 111.08 12.582 0.00268 **
## perlakuan: Kuadratik 1 8.7 8.70 0.985 0.33578
## perlakuan: Kubik 1 44.3 44.27 5.015 0.03969 *
## perlakuan: Kuartik 1 20.5 20.46 2.317 0.14746
## kelompok 2 39.2 19.61 2.221 0.14090
## Residuals 16 141.3 8.83
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Data 3
data11<-read.csv("D:\\Asisten MK STA512\\Data Responsi 13.csv",header=TRUE, sep=";",
colClasses =c("numeric","factor"))
View(data11)
levels(data11$Suhu)
## [1] "10" "30" "50" "70" "90"
model13<- aov(Respon ~ Suhu, data = data11)
summary(model13)
## Df Sum Sq Mean Sq F value Pr(>F)
## Suhu 4 4451 1112.8 87.85 9.51e-08 ***
## Residuals 10 127 12.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Orthogonal Contrasts
kontras3<-cbind(c(2,2,2,-3,-3),c(2,-1,-1,0,0),c(0,1,-1,0,0),c(0,0,0,1,-1))
model33<- aov(Respon ~ Suhu, contrasts = list(Suhu=kontras3), data =data11)
summary.aov(model33,split=list(Suhu=list("Sedang vs Tinggi"=1,"10 vs 30"=2,"30 vs 50"=3,"70 vs 90"=4)))
## Df Sum Sq Mean Sq F value Pr(>F)
## Suhu 4 4451 1112.8 87.850 9.51e-08 ***
## Suhu: Sedang vs Tinggi 1 1832 1831.5 144.593 2.87e-07 ***
## Suhu: 10 vs 30 1 2473 2473.4 195.268 6.89e-08 ***
## Suhu: 30 vs 50 1 140 140.2 11.066 0.00766 **
## Suhu: 70 vs 90 1 6 6.0 0.474 0.50695
## Residuals 10 127 12.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Polinomial Contrast
model13<- aov(Respon ~ Suhu, data = data11)
contrasts(data11$Suhu) <- contr.poly(5)
summary.aov(model33, split= list(Suhu=list("Linear" = 1, "Kuadratik" = 2, "Kubik"=3, "Kuartik"=4)))
## Df Sum Sq Mean Sq F value Pr(>F)
## Suhu 4 4451 1112.8 87.850 9.51e-08 ***
## Suhu: Linear 1 1832 1831.5 144.593 2.87e-07 ***
## Suhu: Kuadratik 1 2473 2473.4 195.268 6.89e-08 ***
## Suhu: Kubik 1 140 140.2 11.066 0.00766 **
## Suhu: Kuartik 1 6 6.0 0.474 0.50695
## Residuals 10 127 12.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dataa<-read.csv("D:\\Asisten MK STA512\\Data Responsi 13.csv",header=TRUE, sep=";")
plot(dataa$Suhu,dataa$Respon)
#modelregresi
m1<-lm(Respon ~ Suhu , data=dataa)
summary(m1)
##
## Call:
## lm(formula = Respon ~ Suhu, data = dataa)
##
## Residuals:
## Min 1Q Median 3Q Max
## -10.533 -7.467 -2.533 7.467 14.733
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 52.70000 4.67958 11.26 4.46e-08 ***
## Suhu -0.54333 0.08146 -6.67 1.54e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 8.924 on 13 degrees of freedom
## Multiple R-squared: 0.7739, Adjusted R-squared: 0.7565
## F-statistic: 44.49 on 1 and 13 DF, p-value: 1.54e-05
suhu2=dataa$Suhu^2
m2<-lm(Respon ~ Suhu + suhu2, data=dataa)
summary(m2)
##
## Call:
## lm(formula = Respon ~ Suhu + suhu2, data = dataa)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.0190 -1.6667 -0.7714 0.8429 7.7143
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 71.319048 3.668929 19.439 1.94e-10 ***
## Suhu -1.638571 0.173210 -9.460 6.50e-07 ***
## suhu2 0.010952 0.001686 6.498 2.95e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.369 on 12 degrees of freedom
## Multiple R-squared: 0.95, Adjusted R-squared: 0.9416
## F-statistic: 113.9 on 2 and 12 DF, p-value: 1.571e-08
suhu3=dataa$Suhu^3
m3<-lm(Respon ~ Suhu + suhu2 + suhu3, data=dataa)
summary(m3)
##
## Call:
## lm(formula = Respon ~ Suhu + suhu2 + suhu3, data = dataa)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8190 -2.4857 -0.7952 2.3595 6.2286
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.181e+01 4.812e+00 17.001 3.03e-09 ***
## Suhu -2.769e+00 4.328e-01 -6.397 5.10e-05 ***
## suhu2 3.856e-02 1.010e-02 3.817 0.00286 **
## suhu3 -1.840e-04 6.674e-05 -2.757 0.01864 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.509 on 11 degrees of freedom
## Multiple R-squared: 0.9704, Adjusted R-squared: 0.9623
## F-statistic: 120.2 on 3 and 11 DF, p-value: 1.088e-08
suhu4=dataa$Suhu^4
m4<-lm(Respon ~ Suhu + suhu2 + suhu3 + suhu4, data=dataa)
summary(m4)
##
## Call:
## lm(formula = Respon ~ Suhu + suhu2 + suhu3 + suhu4, data = dataa)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.000 -2.000 -1.667 2.667 5.000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8.984e+01 1.079e+01 8.323 8.32e-06 ***
## Suhu -3.974e+00 1.511e+00 -2.630 0.0251 *
## suhu2 8.793e-02 6.010e-02 1.463 0.1742
## suhu3 -9.306e-04 8.980e-04 -1.036 0.3245
## suhu4 3.733e-06 4.477e-06 0.834 0.4239
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.559 on 10 degrees of freedom
## Multiple R-squared: 0.9723, Adjusted R-squared: 0.9613
## F-statistic: 87.85 on 4 and 10 DF, p-value: 9.508e-08
dataa<-read.csv("D:\\Asisten MK STA512\\Praktikum-13.csv",header=TRUE,sep = ";",
colClasses =c("factor","numeric"))
str(dataa)
## 'data.frame': 15 obs. of 2 variables:
## $ Perlakuan: Factor w/ 5 levels "0","16","32",..: 1 1 1 2 2 2 3 3 3 4 ...
## $ Respon : num 96 98 94 92 88 90 92 94 84 74 ...
model1<- aov(Respon ~ Perlakuan , data =dataa)
summary(model1)
## Df Sum Sq Mean Sq F value Pr(>F)
## Perlakuan 4 4025 1006.3 94.34 6.73e-08 ***
## Residuals 10 107 10.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Orthogonal Contrasts
model1<- aov(Respon ~ Perlakuan, data =dataa)
kontras3<-cbind(c(2,-1,0,-1,2),c(2,-1,-2,-1,2),c(-1,2,0,-2,1),c(1,-4,6,-4,1))
model33<- aov(Respon ~ Perlakuan, contrasts = list(Perlakuan=kontras3), data =dataa)
summary.aov(model1,split=list(Perlakuan=list("Linear" = 1, "Kuadratik" = 2, "Kubik"=3, "kuartik"=4)))
## Df Sum Sq Mean Sq F value Pr(>F)
## Perlakuan 4 4025 1006.3 94.337 6.73e-08 ***
## Perlakuan: Linear 1 385 385.1 36.100 0.000131 ***
## Perlakuan: Kuadratik 1 642 641.8 60.167 1.54e-05 ***
## Perlakuan: Kubik 1 6 5.6 0.521 0.487018
## Perlakuan: kuartik 1 2993 2992.7 280.562 1.21e-08 ***
## Residuals 10 107 10.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#Polinomial Contrast
model1<- aov(Respon ~ Perlakuan, data =dataa)
contrasts(dataa$Perlakuan) <- contr.poly(5)
summary.aov(model1, split= list(Perlakuan=list("Linear" = 1, "Kuadratik" = 2, "Kubik"=3, "kuartik"=4)))
## Df Sum Sq Mean Sq F value Pr(>F)
## Perlakuan 4 4025 1006.3 94.337 6.73e-08 ***
## Perlakuan: Linear 1 385 385.1 36.100 0.000131 ***
## Perlakuan: Kuadratik 1 642 641.8 60.167 1.54e-05 ***
## Perlakuan: Kubik 1 6 5.6 0.521 0.487018
## Perlakuan: kuartik 1 2993 2992.7 280.562 1.21e-08 ***
## Residuals 10 107 10.7
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
dataa<-read.csv("D:\\Asisten MK STA512\\Praktikum-13.csv",header=TRUE,sep = ";")
#modelregresi
plot(dataa$Perlakuan,dataa$Respon)
m1<-lm(Respon ~ Perlakuan , data=dataa)
summary(m1)
##
## Call:
## lm(formula = Respon ~ Perlakuan, data = dataa)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.400 -4.867 -1.133 4.500 14.133
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101.33333 3.22395 31.431 1.19e-13 ***
## Perlakuan -0.67083 0.08226 -8.155 1.81e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.209 on 13 degrees of freedom
## Multiple R-squared: 0.8365, Adjusted R-squared: 0.8239
## F-statistic: 66.5 on 1 and 13 DF, p-value: 1.811e-06
Perlakuan2=dataa$Perlakuan^2
m2<-lm(Respon ~ Perlakuan + Perlakuan2, data=dataa)
summary(m2)
##
## Call:
## lm(formula = Respon ~ Perlakuan + Perlakuan2, data = dataa)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.9810 -2.3048 -0.5714 1.9238 7.3714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 94.571429 2.193180 43.121 1.57e-14 ***
## Perlakuan 0.174405 0.162375 1.074 0.303906
## Perlakuan2 -0.013207 0.002433 -5.428 0.000153 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 4.036 on 12 degrees of freedom
## Multiple R-squared: 0.9527, Adjusted R-squared: 0.9448
## F-statistic: 120.8 on 2 and 12 DF, p-value: 1.122e-08
Perlakuan3=dataa$Perlakuan^3
m3<-lm(Respon ~ Perlakuan + Perlakuan2 + Perlakuan3, data=dataa)
summary(m3)
##
## Call:
## lm(formula = Respon ~ Perlakuan + Perlakuan2 + Perlakuan3, data = dataa)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.2476 -1.8429 -0.2476 1.5619 7.3714
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 95.4380952 2.2730330 41.987 1.7e-13 ***
## Perlakuan -0.2137897 0.3613978 -0.592 0.566
## Perlakuan2 0.0037202 0.0143409 0.259 0.800
## Perlakuan3 -0.0001763 0.0001473 -1.197 0.256
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.965 on 11 degrees of freedom
## Multiple R-squared: 0.9581, Adjusted R-squared: 0.9467
## F-statistic: 83.92 on 3 and 11 DF, p-value: 7.292e-08
Perlakuan4=dataa$Perlakuan ^4
m4<-lm(Respon ~ Perlakuan + Perlakuan2 + Perlakuan3 + Perlakuan4, data=dataa)
summary(m4)
##
## Call:
## lm(formula = Respon ~ Perlakuan + Perlakuan2 + Perlakuan3 + Perlakuan4,
## data = dataa)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6.000 -1.667 0.000 2.000 4.000
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.600e+01 1.886e+00 50.912 2.07e-13 ***
## Perlakuan -1.677e+00 6.581e-01 -2.548 0.0289 *
## Perlakuan2 1.290e-01 5.162e-02 2.499 0.0315 *
## Perlakuan3 -3.377e-03 1.290e-03 -2.619 0.0256 *
## Perlakuan4 2.501e-05 1.003e-05 2.493 0.0318 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.266 on 10 degrees of freedom
## Multiple R-squared: 0.9742, Adjusted R-squared: 0.9639
## F-statistic: 94.34 on 4 and 10 DF, p-value: 6.733e-08
plot(dataa$Perlakuan,dataa$Respon)
library(ggplot2)
ggplot(dataa,aes(x=Perlakuan, y=Respon)) +
geom_point(alpha=0.95, color="black") +
stat_smooth(method = "lm",
formula = y~poly(x,4,raw=T),
lty = 1, col = "blue",se = F)+
stat_smooth(method = "lm",
formula = y~poly(x,2,raw=T),
lty = 1, col = "red",se = F)+
stat_smooth(method = "lm",
formula = y~poly(x,1,raw=T),
lty = 1, col = "orange",se = F)+
theme_bw()
