options(repos = c(CRAN = "https://cran.rstudio.com/"))
writeLines('PATH="${RTOOLS40_HOME}\\usr\\bin;${PATH}"', con = "~/.Renviron")Faktor: Konsentrasi hormon tanaman (ppm) Level: 6 level (H0 = 0 ppm, H1 = 0.25 ppm, H2 = 0.5 ppm, H3 = 0.75 ppm, H4 = 1.00 ppm, H5 = 1.25 ppm Perlakuan: Pemberian hormon dengan berbagai konsentrasi pada tanaman kedelai Satuan Percobaan: Lahan pertanian tempat kedelai ditanam Satuan Amatan: Setiap ulangan percobaan dalam setiap level konsentrasi hormon Respon: Produksi kedelai dalam kuintal per hektar
\[ Y_{ij} = \mu_i + \varepsilon_{ij} \]
Hipotesis nol (H₀): Konsentrasi hormon tidak berpengaruh terhadap produksi kedelai. \[ H_0: \mu_0 = \mu_1 = \mu_2 = \mu_3 = \mu_4 = \mu_5 \] Hipotesis alternatif (H₁): Setidaknya ada satu tingkat konsentrasi hormon yang menghasilkan produksi kedelai yang berbeda secara signifikan.
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsdata <- data.frame(
UL_1 = c(8, 8.3, 8.9, 9.3, 9.7, 9.5),
UL_2 = c(8.1, 8.2, 8.9, 9, 9, 8.9),
UL_3 = c(7.5, 8.3, 8.3, 8.2, 8.8, 8.5),
UL_4 = c(7.7, 7.9, 8, 8.7, 9, 8.9)
)
rownames(data) <- c("H0", "H1", "H3", "H4", "H5", "H6")
data_long <- data %>%
rownames_to_column(var = "H") %>%
pivot_longer(cols = starts_with("UL"), names_to = "UL", values_to = "Nilai")
print(data_long)## # A tibble: 24 × 3
## H UL Nilai
## <chr> <chr> <dbl>
## 1 H0 UL_1 8
## 2 H0 UL_2 8.1
## 3 H0 UL_3 7.5
## 4 H0 UL_4 7.7
## 5 H1 UL_1 8.3
## 6 H1 UL_2 8.2
## 7 H1 UL_3 8.3
## 8 H1 UL_4 7.9
## 9 H3 UL_1 8.9
## 10 H3 UL_2 8.9
## # ℹ 14 more rowsdata_long$H <- as.factor(data_long$H)
data_long$UL <- as.factor(data_long$UL)
ANOVARAL <- aov(Nilai ~ UL, data = data_long)
summary(ANOVARAL)## Df Sum Sq Mean Sq F value Pr(>F)
## UL 3 1.743 0.5811 2.035 0.141
## Residuals 20 5.710 0.2855ANOVABYROW <- aov(Nilai ~ H, data = data_long)
summary(ANOVABYROW)## Df Sum Sq Mean Sq F value Pr(>F)
## H 5 4.873 0.9747 6.8 0.00101 **
## Residuals 18 2.580 0.1433
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Didapatkan Fhitung=6.8>Ftabel=2.77. Maka cukup bukti untuk menolak Ho. Ada pengaruh signifikan dari konsentrasi hormon terhadap produksi kedelai pada taraf nyata 5%
library(readxl)## Warning: package 'readxl' was built under R version 4.4.2DataBunga<-read_xlsx("C:/Users/Ainul Hayati/Documents/kuliah/Departemen/Semester 4/Metode Perancangan Percobaan/Praktikum/Latihan 2.xlsx")
DataBunga## # A tibble: 25 × 4
## Campuran `Tingkat Kematangan` `Teknik Pengeringan` rendeman
## <chr> <dbl> <dbl> <dbl>
## 1 A 1 1 5.39
## 2 A 2 2 5.38
## 3 A 3 3 5.36
## 4 A 4 4 5.35
## 5 A 5 5 5.4
## 6 B 1 2 5.63
## 7 B 2 3 5.64
## 8 B 3 4 5.61
## 9 B 4 5 5.8
## 10 B 5 1 5.62
## # ℹ 15 more rows\[ Y_{ij(k)} = \mu + \alpha_i + \beta_j + \tau(k) + \varepsilon_{ij(k)} \]
\[ H_0: \tau(1) = \tau(2) = \dots = \tau(t) = 0 \quad \text{(perlakuan tidak berpengaruh terhadap respon)}. H_1: \text{Minimal ada satu perlakuan } k \text{ dimana } \tau(k) \neq 0. \]
\[ H_0: \alpha_1 = \alpha_2 = \dots = \alpha_r = 0 \quad \text{(baris tidak berpengaruh terhadap respon)}. H_1: \text{Minimal ada satu baris } i \text{ dimana } \alpha_i \neq 0. \]
\[ H_0: \beta_1 = \beta_2 = \dots = \beta_r = 0 \quad \text{(lajur tidak berpengaruh terhadap respon)}. H_1: \text{Minimal ada satu lajur } j \text{ dimana } \beta_j \neq 0. \]
DataBunga$Campuran<-as.factor(DataBunga$Campuran)
DataBunga$`Tingkat Kematangan`<-as.factor(DataBunga$`Tingkat Kematangan`)
DataBunga$`Teknik Pengeringan`<-as.factor(DataBunga$`Teknik Pengeringan`)
ANOVARBSL<-aov(rendeman~Campuran+`Tingkat Kematangan`+`Teknik Pengeringan`,data=DataBunga)
summary(ANOVARBSL)## Df Sum Sq Mean Sq F value Pr(>F)
## Campuran 4 3.342 0.8354 569.840 1.44e-13 ***
## `Tingkat Kematangan` 4 0.006 0.0014 0.952 0.468
## `Teknik Pengeringan` 4 0.008 0.0019 1.327 0.315
## Residuals 12 0.018 0.0015
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1qf(0.05,3,6,lower.tail = FALSE)## [1] 4.757063Berdasarkan hasil analisis ANOVA, didapatkan FHitung=569.840>FTabel=4.76. Maka dapat disimpulkan bahwa terdapat pengaruh signifikan dari jenis pelarut dan teknik pengeringan terhadap rendemen antioksidan.