ANOVA
PRAKTIKUM TM-2
- RAL ONE-WAY
read.csv("D:\\UNAIR\\SEMESTER 2\\METSTAT\\Data Praktikum M2-20240316\\M2-Data Praktikum 1.txt")
soal_prak1=read.table("D:\\UNAIR\\SEMESTER 2\\METSTAT\\Data Praktikum M2-20240316\\M2-Data Praktikum 1.txt", header=TRUE)
y_prak1=soal_prak1$Asam_Askorbat
perlakuan_prak1=soal_prak1$Varietas
summary(soal_prak1)
## Asam_Askorbat Varietas
## Min. :5.260 Length:30
## 1st Qu.:5.590 Class :character
## Median :6.275 Mode :character
## Mean :6.214
## 3rd Qu.:6.815
## Max. :7.120
ANOVA_prak1 <- aov(y_prak1 ~ perlakuan_prak1, data = soal_prak1)
summary(ANOVA_prak1)
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan_prak1 2 10.36 5.181 332.8 <2e-16 ***
## Residuals 27 0.42 0.016
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- RAL TWO-WAY
soal_prak2=read.table("D:\\UNAIR\\SEMESTER 2\\METSTAT\\Data Praktikum M2-20240316\\M2-Data Praktikum 2.txt", header=TRUE)
y_prak2=soal_prak2$Pertumbuhan_Tanaman
perlakuanA_prak2=soal_prak2$Penyiraman
perlakuanB_prak2=soal_prak2$Penyinaran_Matahari
summary(soal_prak2)
## Pertumbuhan_Tanaman Penyiraman Penyinaran_Matahari
## Min. :3.00 Length:40 Length:40
## 1st Qu.:4.55 Class :character Class :character
## Median :5.20 Mode :character Mode :character
## Mean :5.16
## 3rd Qu.:5.80
## Max. :6.60
#tanpa interaksi
ANOVA_prak2 <- aov(y_prak2 ~ perlakuanA_prak2+perlakuanB_prak2, data = soal_prak2)
summary(ANOVA_prak2)
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuanA_prak2 1 0.064 0.064 0.204 0.655
## perlakuanB_prak2 3 20.006 6.669 21.207 5.26e-08 ***
## Residuals 35 11.006 0.314
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#dengan interaksi
ANOVA_prak2_interaction <- aov(y_prak2 ~ perlakuanA_prak2*perlakuanB_prak2, data = soal_prak2)
summary(ANOVA_prak2_interaction)
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuanA_prak2 1 0.064 0.064 0.199 0.659
## perlakuanB_prak2 3 20.006 6.669 20.726 1.2e-07 ***
## perlakuanA_prak2:perlakuanB_prak2 3 0.710 0.237 0.736 0.539
## Residuals 32 10.296 0.322
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- RAKL
soal_prak3=read.table("D:\\UNAIR\\SEMESTER 2\\METSTAT\\Data Praktikum M2-20240316\\M2-Data Praktikum 3.txt", header=TRUE)
y_prak3=soal_prak3$Hardness
perlakuan_prak3=soal_prak3$Tip
blok_prak3=soal_prak3$Block
summary(soal_prak3)
## Hardness Tip Block
## Min. :-3.00 Min. :1.00 Min. :1.00
## 1st Qu.:-1.00 1st Qu.:1.75 1st Qu.:1.75
## Median : 1.00 Median :2.50 Median :2.50
## Mean : 1.25 Mean :2.50 Mean :2.50
## 3rd Qu.: 3.25 3rd Qu.:3.25 3rd Qu.:3.25
## Max. : 7.00 Max. :4.00 Max. :4.00
ANOVA_prak3 = aov(y_prak3 ~ perlakuan_prak3+blok_prak3, data = soal_prak3)
summary(ANOVA_prak3)
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan_prak3 1 11.25 11.25 3.507 0.083763 .
## blok_prak3 1 76.05 76.05 23.709 0.000306 ***
## Residuals 13 41.70 3.21
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
TUGAS TM 2
- RAL ONE-WAY
read.csv("D:\\UNAIR\\SEMESTER 2\\METSTAT\\soal1.txt")
Soal1=read.table("D:\\UNAIR\\SEMESTER 2\\METSTAT\\soal1.txt", header=TRUE, colClasses = c("numeric", "factor"))
y1=Soal1$Hasil
perlakuan=Soal1$Perlakuan
summary(Soal1)
## Hasil Perlakuan
## Min. :11 Perlakuan_1:4
## 1st Qu.:12 Perlakuan_2:5
## Median :15 Perlakuan_3:6
## Mean :15
## 3rd Qu.:18
## Max. :21
ANOVA1 <- aov(y1 ~ perlakuan, data = Soal1)
summary(ANOVA1)
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan 2 90 45.00 8.437 0.00515 **
## Residuals 12 64 5.33
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- RAL TWO-WAY
Soal2=read.table("D:\\UNAIR\\SEMESTER 2\\METSTAT\\soal2.txt", header=TRUE, colClasses = c("numeric", "factor", "factor"))
y2=Soal2$Gaji
Perlakuan_A=Soal2$Lokasi
Perlakuan_B=Soal2$Tipe
summary(Soal2)
## Gaji Lokasi Tipe
## Min. :44.00 Central:27 Type_1:27
## 1st Qu.:56.00 East :27 Type_2:27
## Median :62.00 West :27 Type_3:27
## Mean :62.62
## 3rd Qu.:69.00
## Max. :84.00
#ANOVA
#-----------tanpa interaksi-----------
ANOVA2 <- aov(y2 ~ Perlakuan_A + Perlakuan_B, data = Soal2)
summary(ANOVA2)
## Df Sum Sq Mean Sq F value Pr(>F)
## Perlakuan_A 2 2521 1260.5 47.79 3.65e-14 ***
## Perlakuan_B 2 2499 1249.7 47.38 4.37e-14 ***
## Residuals 76 2005 26.4
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#-----------dengan interaksi-----------
INTERACTION <- aov(y2 ~ Perlakuan_A * Perlakuan_B, data = Soal2)
summary(INTERACTION)
## Df Sum Sq Mean Sq F value Pr(>F)
## Perlakuan_A 2 2521.0 1260.5 50.645 1.85e-14 ***
## Perlakuan_B 2 2499.4 1249.7 50.212 2.22e-14 ***
## Perlakuan_A:Perlakuan_B 4 212.7 53.2 2.137 0.085 .
## Residuals 72 1792.0 24.9
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
PRAKTIKUM TM 3
- LSD/RBSL
setwd("D:/UNAIR/SEMESTER 2/METSTAT/")
soal_prak1_2=read.table("m3 factorial design rakl 1.txt", header = TRUE, colClasses = c("factor","numeric","factor","factor"))
soal_prak1_2
summary(soal_prak1_2)
## Perlakuan Pertumbuhan_Tanaman_Jagung Baris Kolom Perlakuan_Code
## A:5 Min. : 8.00 1:5 1:5 1:5
## B:5 1st Qu.:13.00 2:5 2:5 2:5
## C:5 Median :16.00 3:5 3:5 3:5
## D:5 Mean :15.72 4:5 4:5 4:5
## E:5 3rd Qu.:18.00 5:5 5:5 5:5
## Max. :23.00
perlakuan_prak1_2=soal_prak1_2$Perlakuan
y_prak1_2=soal_prak1_2$Pertumbuhan_Tanaman_Jagung
baris_prak1_2=soal_prak1_2$Baris
kolom_prak1_2=soal_prak1_2$Kolom
ANOVA_prak1_2 = aov(y_prak1_2 ~ perlakuan_prak1_2+baris_prak1_2+kolom_prak1_2, data = soal_prak1_2)
summary(ANOVA_prak1_2)
## Df Sum Sq Mean Sq F value Pr(>F)
## perlakuan_prak1_2 4 454.6 113.66 122.655 1.27e-09 ***
## baris_prak1_2 4 3.0 0.76 0.820 0.5367
## kolom_prak1_2 4 14.2 3.56 3.842 0.0311 *
## Residuals 12 11.1 0.93
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- FACTORIAL RAL
setwd("D:/UNAIR/SEMESTER 2/METSTAT/")
soal_prak2_2=read.table("m3 factorial design rakl 2.txt", header = TRUE, colClasses = c("numeric","factor","factor"))
y_prak2_2=soal_prak2_2$Daya_Tahan_Battery
jb=soal_prak2_2$Jenis_Bahan
temp=soal_prak2_2$Temperatur
ANOVA_prak2_2= aov(y_prak2_2 ~ jb+temp+jb*temp, data=soal_prak2_2)
summary(ANOVA_prak2_2)
## Df Sum Sq Mean Sq F value Pr(>F)
## jb 2 10684 5342 7.911 0.00198 **
## temp 2 39119 19559 28.968 1.91e-07 ***
## jb:temp 4 9614 2403 3.560 0.01861 *
## Residuals 27 18231 675
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
- FACTORIAL RAKL
setwd("D:/UNAIR/SEMESTER 2/METSTAT/")
Data3 <- read.table("m3 factorial design rakl 3.txt", header = TRUE, colClasses = c("numeric","factor","factor","factor"))
Data3
summary(Data3)
## Kekuatan_Signal Jenis_Filter Operator Lokasi
## Min. : 80.00 1:12 1:6 1:8
## 1st Qu.: 89.25 2:12 2:6 2:8
## Median : 94.00 3:6 3:8
## Mean : 94.92 4:6
## 3rd Qu.:100.50
## Max. :114.00
#Subset Data
y3=Data3$Kekuatan_Signal
jf=Data3$Jenis_Filter
opt=Data3$Operator
lok=Data3$Lokasi
#ANOVA Faktorial_RAKL
#Interaksi 3 faktor
ANOVA3 <- aov(y3 ~ jf+opt+lok+jf*opt+jf*lok+opt*lok+jf*opt*lok, data = Data3)
summary(ANOVA3)
## Df Sum Sq Mean Sq
## jf 1 1066.7 1066.7
## opt 3 402.2 134.1
## lok 2 335.6 167.8
## jf:opt 3 34.3 11.4
## jf:lok 2 77.1 38.5
## opt:lok 6 99.1 16.5
## jf:opt:lok 6 32.9 5.5
#Interaksi 2 faktor
ANOVA4 <- aov(y3 ~ jf+opt+lok+jf*opt+jf*lok+opt*lok, data = Data3)
summary(ANOVA4)
## Df Sum Sq Mean Sq F value Pr(>F)
## jf 1 1066.7 1066.7 194.430 8.48e-06 ***
## opt 3 402.2 134.1 24.435 0.000920 ***
## lok 2 335.6 167.8 30.585 0.000713 ***
## jf:opt 3 34.3 11.4 2.086 0.203528
## jf:lok 2 77.1 38.5 7.025 0.026796 *
## opt:lok 6 99.1 16.5 3.010 0.102851
## Residuals 6 32.9 5.5
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1