One Way and Two Way Manova
140610230040-Kevin Jonathan Sidabutar
2024-10-27
Sumber Data :
Website : kaggle.com link : https://www.kaggle.com/datasets/bhadramohit/agriculture-and-farming-dataset
One Way Manova
Deskripsi Data:
Data yang digunakan merupakan informasi tentang jenis tanaman yang ditanam, tingkat hasil, penggunaan lahan, indikator keberlanjutan, dan data ekonomi seperti biaya dan pendapatan dari aktivitas pertanian. Dataset ini berasal dari survei pertanian, laporan pemerintah, dan penelitian lapangan, memberikan wawasan mendalam bagi peneliti, pembuat kebijakan, dan profesional agribisnis untuk menganalisis praktik pertanian, keberlanjutan, dan dampak ekonomi dalam sektor pertanian.
data <- read.table("agriculture_1.csv", sep = ";", header = TRUE)
print(data)## Farm_ID Crop_Type Farm_Area.acres. Irrigation_Type Fertilizer_Used.tons.
## 1 F001 Cotton 329.40 Sprinkler 8.14
## 2 F002 Carrot 18.67 Manual 4.77
## 3 F003 Sugarcane 306.03 Flood 2.91
## 4 F004 Tomato 380.21 Rain-fed 3.32
## 5 F005 Tomato 135.56 Sprinkler 8.33
## 6 F006 Sugarcane 12.50 Sprinkler 6.42
## 7 F007 Soybean 360.06 Drip 1.83
## 8 F008 Rice 464.60 Drip 5.18
## 9 F009 Maize 389.37 Drip 0.57
## 10 F010 Soybean 184.37 Drip 2.18
## 11 F011 Rice 279.95 Drip 8.02
## 12 F012 Sugarcane 145.32 Flood 3.01
## 13 F013 Wheat 329.10 Drip 5.26
## 14 F014 Rice 246.02 Flood 1.01
## 15 F015 Sugarcane 305.15 Rain-fed 5.39
## 16 F016 Barley 60.22 Flood 2.19
## 17 F017 Carrot 284.01 Manual 5.89
## 18 F018 Maize 128.23 Rain-fed 4.91
## 19 F019 Maize 460.93 Drip 1.09
## 20 F020 Barley 58.85 Sprinkler 3.61
## 21 F021 Cotton 377.05 Drip 5.95
## 22 F022 Wheat 92.67 Flood 6.95
## 23 F023 Potato 15.67 Drip 9.95
## 24 F024 Rice 483.88 Drip 6.31
## 25 F025 Barley 75.64 Flood 6.69
## 26 F026 Wheat 162.28 Flood 5.85
## 27 F027 Cotton 375.10 Rain-fed 0.50
## 28 F028 Tomato 256.19 Flood 7.32
## 29 F029 Wheat 288.52 Manual 1.79
## 30 F030 Potato 286.52 Rain-fed 8.91
## 31 F031 Barley 136.16 Flood 5.89
## 32 F032 Carrot 350.42 Flood 8.40
## 33 F033 Barley 446.76 Drip 7.79
## 34 F034 Tomato 264.12 Drip 4.75
## 35 F035 Soybean 266.03 Drip 8.57
## 36 F036 Cotton 446.16 Manual 4.35
## 37 F037 Soybean 156.10 Manual 1.18
## 38 F038 Barley 431.22 Drip 5.71
## 39 F039 Cotton 220.48 Flood 9.96
## 40 F040 Cotton 166.82 Rain-fed 2.85
## 41 F041 Rice 370.79 Flood 8.18
## 42 F042 Sugarcane 418.99 Sprinkler 0.78
## 43 F043 Cotton 78.79 Flood 1.35
## 44 F044 Soybean 84.12 Manual 4.64
## 45 F045 Tomato 326.69 Sprinkler 5.24
## 46 F046 Carrot 112.80 Sprinkler 1.80
## 47 F047 Potato 347.66 Drip 3.86
## 48 F048 Potato 77.39 Sprinkler 9.34
## 49 F049 Barley 462.37 Sprinkler 2.30
## 50 F050 Tomato 292.25 Rain-fed 4.08
## Pesticide_Used.kg. Yield.tons. Soil_Type Season Water_Usage.cubic.meters.
## 1 2.21 14.44 Loamy Kharif 76648.20
## 2 4.36 42.91 Peaty Kharif 68725.54
## 3 0.56 33.44 Silty Kharif 75538.56
## 4 4.35 34.08 Silty Zaid 45401.23
## 5 4.48 43.28 Clay Zaid 93718.69
## 6 2.25 38.18 Loamy Zaid 46487.98
## 7 2.37 44.93 Sandy Rabi 40583.57
## 8 0.91 4.23 Silty Kharif 9392.38
## 9 4.93 3.86 Peaty Rabi 60202.14
## 10 2.67 17.25 Sandy Kharif 90922.15
## 11 1.24 32.85 Clay Zaid 5869.75
## 12 2.27 8.08 Clay Kharif 88976.51
## 13 0.83 5.44 Clay Zaid 45922.35
## 14 3.45 11.38 Sandy Rabi 71953.14
## 15 2.15 28.77 Peaty Kharif 33615.77
## 16 0.35 16.03 Sandy Zaid 25132.48
## 17 0.81 47.70 Loamy Zaid 88301.46
## 18 0.77 16.67 Loamy Rabi 18660.03
## 19 1.31 39.96 Sandy Zaid 54314.28
## 20 3.32 18.85 Sandy Kharif 92481.89
## 21 0.91 29.17 Clay Rabi 26743.55
## 22 3.64 30.70 Clay Rabi 42874.34
## 23 2.99 18.13 Loamy Zaid 41862.86
## 24 2.29 34.46 Clay Zaid 61383.07
## 25 3.57 6.14 Silty Zaid 43847.82
## 26 2.42 24.63 Loamy Rabi 65838.40
## 27 4.76 22.51 Clay Kharif 39362.44
## 28 2.19 48.02 Silty Rabi 81313.04
## 29 4.78 36.90 Silty Zaid 23208.04
## 30 0.77 30.50 Loamy Zaid 93407.38
## 31 1.36 11.86 Clay Zaid 30098.35
## 32 2.94 24.34 Clay Rabi 71580.87
## 33 0.96 46.47 Loamy Zaid 93656.06
## 34 4.79 12.92 Loamy Rabi 92745.01
## 35 1.35 34.45 Silty Zaid 43610.21
## 36 3.47 12.53 Loamy Zaid 38874.28
## 37 4.43 40.15 Loamy Zaid 73646.55
## 38 3.18 45.95 Silty Kharif 36065.94
## 39 2.91 10.53 Clay Zaid 82549.03
## 40 1.36 46.19 Sandy Zaid 12007.70
## 41 4.99 35.01 Sandy Kharif 85208.71
## 42 0.58 26.29 Clay Zaid 33705.69
## 43 3.00 11.45 Sandy Zaid 94754.73
## 44 2.53 24.77 Sandy Rabi 40614.40
## 45 0.55 18.34 Peaty Kharif 37466.11
## 46 1.01 31.57 Clay Kharif 79966.10
## 47 2.68 31.47 Sandy Kharif 86989.88
## 48 3.00 20.53 Silty Zaid 5874.17
## 49 0.14 39.51 Clay Kharif 53879.87
## 50 0.76 45.14 Silty Kharif 90232.08data_df <- data[, c(3, 5, 6, 7)]
data1 <- data_dflibrary(MVN)## Warning: package 'MVN' was built under R version 4.3.3test = mvn(data1, mvnTest = "mardia", univariateTest = "SW", multivariatePlot = "qq")
print(test)## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 7.94126395726055 0.992249254647234 YES
## 2 Mardia Kurtosis -2.35900141464378 0.0183241859136321 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk Farm_Area.acres. 0.9482 0.0288 NO
## 2 Shapiro-Wilk Fertilizer_Used.tons. 0.9599 0.0878 YES
## 3 Shapiro-Wilk Pesticide_Used.kg. 0.9366 0.0099 NO
## 4 Shapiro-Wilk Yield.tons. 0.9501 0.0343 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## Farm_Area.acres. 50 254.9638 139.417782 281.980 12.50 483.88 135.7100
## Fertilizer_Used.tons. 50 4.9054 2.732689 5.045 0.50 9.96 2.4375
## Pesticide_Used.kg. 50 2.3980 1.438613 2.330 0.14 4.99 0.9725
## Yield.tons. 50 27.0592 13.345789 28.970 3.86 48.02 16.1900
## 75th Skew Kurtosis
## Farm_Area.acres. 368.1075 -0.13195502 -1.250421
## Fertilizer_Used.tons. 6.8850 0.07462449 -1.142863
## Pesticide_Used.kg. 3.4175 0.24571341 -1.171036
## Yield.tons. 37.8600 -0.08289283 -1.263373Uji Normalitas
Hipotesis
H0: Data berdistribusi Normal Multivariat H1: Data berdistribusi Normal Multivariat
Taraf Signifikansi
alpha:5%
Statistik Uji: SKEW=(1/n^2) ∑(i=1..n),∑(j=1..n)(dij)^3 KURT=(1/n) ∑(i=1..n)(dij)^2
library(MVN)
test = mvn(data1, mvnTest = "mardia", univariateTest = "SW", multivariatePlot = "qq")
print(test)## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 7.94126395726055 0.992249254647234 YES
## 2 Mardia Kurtosis -2.35900141464378 0.0183241859136321 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk Farm_Area.acres. 0.9482 0.0288 NO
## 2 Shapiro-Wilk Fertilizer_Used.tons. 0.9599 0.0878 YES
## 3 Shapiro-Wilk Pesticide_Used.kg. 0.9366 0.0099 NO
## 4 Shapiro-Wilk Yield.tons. 0.9501 0.0343 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## Farm_Area.acres. 50 254.9638 139.417782 281.980 12.50 483.88 135.7100
## Fertilizer_Used.tons. 50 4.9054 2.732689 5.045 0.50 9.96 2.4375
## Pesticide_Used.kg. 50 2.3980 1.438613 2.330 0.14 4.99 0.9725
## Yield.tons. 50 27.0592 13.345789 28.970 3.86 48.02 16.1900
## 75th Skew Kurtosis
## Farm_Area.acres. 368.1075 -0.13195502 -1.250421
## Fertilizer_Used.tons. 6.8850 0.07462449 -1.142863
## Pesticide_Used.kg. 3.4175 0.24571341 -1.171036
## Yield.tons. 37.8600 -0.08289283 -1.263373print(test)## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 7.94126395726055 0.992249254647234 YES
## 2 Mardia Kurtosis -2.35900141464378 0.0183241859136321 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk Farm_Area.acres. 0.9482 0.0288 NO
## 2 Shapiro-Wilk Fertilizer_Used.tons. 0.9599 0.0878 YES
## 3 Shapiro-Wilk Pesticide_Used.kg. 0.9366 0.0099 NO
## 4 Shapiro-Wilk Yield.tons. 0.9501 0.0343 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## Farm_Area.acres. 50 254.9638 139.417782 281.980 12.50 483.88 135.7100
## Fertilizer_Used.tons. 50 4.9054 2.732689 5.045 0.50 9.96 2.4375
## Pesticide_Used.kg. 50 2.3980 1.438613 2.330 0.14 4.99 0.9725
## Yield.tons. 50 27.0592 13.345789 28.970 3.86 48.02 16.1900
## 75th Skew Kurtosis
## Farm_Area.acres. 368.1075 -0.13195502 -1.250421
## Fertilizer_Used.tons. 6.8850 0.07462449 -1.142863
## Pesticide_Used.kg. 3.4175 0.24571341 -1.171036
## Yield.tons. 37.8600 -0.08289283 -1.263373print(test)## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 7.94126395726055 0.992249254647234 YES
## 2 Mardia Kurtosis -2.35900141464378 0.0183241859136321 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk Farm_Area.acres. 0.9482 0.0288 NO
## 2 Shapiro-Wilk Fertilizer_Used.tons. 0.9599 0.0878 YES
## 3 Shapiro-Wilk Pesticide_Used.kg. 0.9366 0.0099 NO
## 4 Shapiro-Wilk Yield.tons. 0.9501 0.0343 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## Farm_Area.acres. 50 254.9638 139.417782 281.980 12.50 483.88 135.7100
## Fertilizer_Used.tons. 50 4.9054 2.732689 5.045 0.50 9.96 2.4375
## Pesticide_Used.kg. 50 2.3980 1.438613 2.330 0.14 4.99 0.9725
## Yield.tons. 50 27.0592 13.345789 28.970 3.86 48.02 16.1900
## 75th Skew Kurtosis
## Farm_Area.acres. 368.1075 -0.13195502 -1.250421
## Fertilizer_Used.tons. 6.8850 0.07462449 -1.142863
## Pesticide_Used.kg. 3.4175 0.24571341 -1.171036
## Yield.tons. 37.8600 -0.08289283 -1.263373P-Value Uji Normalitas Multivariate
Mardia Skewness : 1.72480337041808e-11 Mardia Kurtosis : 3.49748267014505e-07
P-Values Uji Normalitas Univariat
Farm _Area :0.0288 Fertilizer_Used :0.0878 Pesticide_Used :0.0099 Yield :0.0343
Kriteria Uji
Uji Marrdia Skewness, tolak H0 jika Pvalue < alpha Uji Marrdia Kurtosis, tolak H0 jika Pvalue < alpha
Keputusan
Mardia Skewness < 0,05 -> tolak H0 Mardia Kurtosis < 0,05 -> tolak H0
Kesimpulan
Karena Pvalue untuk mardia skewness dan mardia kurtosis lebih kecil dari alpha (0,05) maka tolak H0 yang berarti data tidak berdistribusi normal, meskipun asumsi normalitas tidak terpenuhi namun dikarenakan untuk bahan pembelajaran maka data ini diasumsikan berdistribusi normal multivariat. ## UJi HOmogenitas Multivariat
Hipotesis :
H0: s1 = s2 = s3, menunjukkan bahwa matriks kovarians antar grup
adalah serupa.
H1: Terdapat setidaknya satu matriks kovarians grup ang berbeda.
####Taraf Signifikansi : alpha = 5%
Statistik Uji :
grup = data$Crop_Type
print(grup)## [1] "Cotton" "Carrot" "Sugarcane" "Tomato" "Tomato" "Sugarcane"
## [7] "Soybean" "Rice" "Maize" "Soybean" "Rice" "Sugarcane"
## [13] "Wheat" "Rice" "Sugarcane" "Barley" "Carrot" "Maize"
## [19] "Maize" "Barley" "Cotton" "Wheat" "Potato" "Rice"
## [25] "Barley" "Wheat" "Cotton" "Tomato" "Wheat" "Potato"
## [31] "Barley" "Carrot" "Barley" "Tomato" "Soybean" "Cotton"
## [37] "Soybean" "Barley" "Cotton" "Cotton" "Rice" "Sugarcane"
## [43] "Cotton" "Soybean" "Tomato" "Carrot" "Potato" "Potato"
## [49] "Barley" "Tomato"``{r} library(“heplots”) owm = manova(cbind(Fertilizer_Used.tons., Pesticide_Used.kg.) ~ Crop_Type, data = data) summary(owm)
Df Pillai approx F num Df den Df Pr(>F) Crop_Type 9 0.31637
0.83517 18 80 0.6546 Residuals 40
``
Kriteria Uji:
Tolak H0 jika P-Value < alpha
Keputusan:
Karena nilai P-Valu(0,6456) = > dari alpha (0,05), maka H0 diterima Kesimpulan Dengan taraf signifikansi 5% dapat disimpulkan bahwa data tanaman pertanian ini memiliki matriks kovarians grup yang sama.
Independensi
Asumsi independensi dianggap terpenuhi jika pengamatan diambil secara acak. Dalam konteks ini, data mengenai tanaman pertanian diperoleh melalui metode pengambilan sampel yang acak.
One Way Manova
Hipotesis:
H0 : μ1=μ2=0 (Jenis tanaman tidak berpengaruh terhadap jumlah penggunaan pupuk) H1 : Terdapat minimal satu μ1 tidak sama dengan 0. i = 1,2 (Jenis tanaman berpengaruh penggunaan pupuk) Taraf Signifikansi alpha = 5%
Statistik Uji:
owm = manova(cbind(data\(Farm_Area(acres),data\)Fertilizer_Used(tons), data\(Pesticide_Used(kg), data\)Yield(tons)) ~ data$Yield_Group) summary(owm)
Kriteria Uji:
Tolak H0 jika P Value < alpha terima dalam keadaan lain
Keputusan
Karena nilai P Value = 0.112 > dari alpha = 0,05. maka H0 diterima
Kesimpulan
Dengan taraf signifikansi 5%, dapat disimpulkan bahwa jenis tanaman yang ditanam di lahan pertanian tidak berpengaruh signifikan terhadap hasil panen, penggunaan air, pestisida dan pupuk yang digunakan. .
Two Way Manova
Deskripsi Data:
Data yang digunakan merupakan informasi tentang jenis tanaman yang ditanam, tingkat hasil, penggunaan lahan, indikator keberlanjutan, dan data ekonomi seperti biaya dan pendapatan dari aktivitas pertanian. Dataset ini berasal dari survei pertanian, laporan pemerintah, dan penelitian lapangan, memberikan wawasan mendalam bagi peneliti, pembuat kebijakan, dan profesional agribisnis untuk menganalisis praktik pertanian, keberlanjutan, dan dampak ekonomi dalam sektor pertanian.
library(readr)## Warning: package 'readr' was built under R version 4.3.3agriculture <- read_csv("agriculture.csv")## Rows: 50 Columns: 1
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Farm_ID;Crop_Type;Farm_Area(acres);Irrigation_Type;Fertilizer_Used(...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.print(agriculture)## # A tibble: 50 × 1
## Farm_ID;Crop_Type;Farm_Area(acres);Irrigation_Type;Fertilizer_Used(tons);Pe…¹
## <chr>
## 1 F001;Cotton;329.4;Sprinkler;8.14;2.21;14.44;Loamy;Kharif;76648.2
## 2 F002;Carrot;18.67;Manual;4.77;4.36;42.91;Peaty;Kharif;68725.54
## 3 F003;Sugarcane;306.03;Flood;2.91;0.56;33.44;Silty;Kharif;75538.56
## 4 F004;Tomato;380.21;Rain-fed;3.32;4.35;34.08;Silty;Zaid;45401.23
## 5 F005;Tomato;135.56;Sprinkler;8.33;4.48;43.28;Clay;Zaid;93718.69
## 6 F006;Sugarcane;12.5;Sprinkler;6.42;2.25;38.18;Loamy;Zaid;46487.98
## 7 F007;Soybean;360.06;Drip;1.83;2.37;44.93;Sandy;Rabi;40583.57
## 8 F008;Rice;464.6;Drip;5.18;0.91;4.23;Silty;Kharif;9392.38
## 9 F009;Maize;389.37;Drip;0.57;4.93;3.86;Peaty;Rabi;60202.14
## 10 F010;Soybean;184.37;Drip;2.18;2.67;17.25;Sandy;Kharif;90922.15
## # ℹ 40 more rows
## # ℹ abbreviated name:
## # ¹`Farm_ID;Crop_Type;Farm_Area(acres);Irrigation_Type;Fertilizer_Used(tons);Pesticide_Used(kg);Yield(tons);Soil_Type;Season;Water_Usage(cubic meters)`x1 = data1$`Fertilizer_Used(tons)`
x2 = data1$`Pesticide_Used(kg)`
x3 = data1$`Pesticide_Used(kg)`
data1f = data.frame (x1=x1, x2=x2, x3=x3);data1f## data frame with 0 columns and 0 rowslibrary(readr)
library(MVN)
library(heplots)## Warning: package 'heplots' was built under R version 4.3.3## Loading required package: broomagriculture_data <- read_csv("agriculture.csv")## Rows: 50 Columns: 1## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): Farm_ID;Crop_Type;Farm_Area(acres);Irrigation_Type;Fertilizer_Used(...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.x1 <- data$Fertilizer_Used.tons.
x2 <- data$Pesticide_Used.kg.
x3 <- data$Yield.tons.
data1f <- data.frame(x1 = x1, x2 = x2, x3 = x3)
head(data1f)## x1 x2 x3
## 1 8.14 2.21 14.44
## 2 4.77 4.36 42.91
## 3 2.91 0.56 33.44
## 4 3.32 4.35 34.08
## 5 8.33 4.48 43.28
## 6 6.42 2.25 38.18alpha <- 0.05 # Taraf Signifikansi
####Statistik Uji
result <- mvn(data1f, mvnTest = "mardia", univariateTest = "SW", multivariatePlot = "qq")
print(result)## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 4.23514467691604 0.936116310221358 YES
## 2 Mardia Kurtosis -2.1906792376609 0.0284750125914426 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk x1 0.9599 0.0878 YES
## 2 Shapiro-Wilk x2 0.9366 0.0099 NO
## 3 Shapiro-Wilk x3 0.9501 0.0343 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th 75th Skew Kurtosis
## x1 50 4.9054 2.732689 5.045 0.50 9.96 2.4375 6.8850 0.07462449 -1.142863
## x2 50 2.3980 1.438613 2.330 0.14 4.99 0.9725 3.4175 0.24571341 -1.171036
## x3 50 27.0592 13.345789 28.970 3.86 48.02 16.1900 37.8600 -0.08289283 -1.263373print(test)## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 7.94126395726055 0.992249254647234 YES
## 2 Mardia Kurtosis -2.35900141464378 0.0183241859136321 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk Farm_Area.acres. 0.9482 0.0288 NO
## 2 Shapiro-Wilk Fertilizer_Used.tons. 0.9599 0.0878 YES
## 3 Shapiro-Wilk Pesticide_Used.kg. 0.9366 0.0099 NO
## 4 Shapiro-Wilk Yield.tons. 0.9501 0.0343 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## Farm_Area.acres. 50 254.9638 139.417782 281.980 12.50 483.88 135.7100
## Fertilizer_Used.tons. 50 4.9054 2.732689 5.045 0.50 9.96 2.4375
## Pesticide_Used.kg. 50 2.3980 1.438613 2.330 0.14 4.99 0.9725
## Yield.tons. 50 27.0592 13.345789 28.970 3.86 48.02 16.1900
## 75th Skew Kurtosis
## Farm_Area.acres. 368.1075 -0.13195502 -1.250421
## Fertilizer_Used.tons. 6.8850 0.07462449 -1.142863
## Pesticide_Used.kg. 3.4175 0.24571341 -1.171036
## Yield.tons. 37.8600 -0.08289283 -1.263373print(test)## $multivariateNormality
## Test Statistic p value Result
## 1 Mardia Skewness 7.94126395726055 0.992249254647234 YES
## 2 Mardia Kurtosis -2.35900141464378 0.0183241859136321 NO
## 3 MVN <NA> <NA> NO
##
## $univariateNormality
## Test Variable Statistic p value Normality
## 1 Shapiro-Wilk Farm_Area.acres. 0.9482 0.0288 NO
## 2 Shapiro-Wilk Fertilizer_Used.tons. 0.9599 0.0878 YES
## 3 Shapiro-Wilk Pesticide_Used.kg. 0.9366 0.0099 NO
## 4 Shapiro-Wilk Yield.tons. 0.9501 0.0343 NO
##
## $Descriptives
## n Mean Std.Dev Median Min Max 25th
## Farm_Area.acres. 50 254.9638 139.417782 281.980 12.50 483.88 135.7100
## Fertilizer_Used.tons. 50 4.9054 2.732689 5.045 0.50 9.96 2.4375
## Pesticide_Used.kg. 50 2.3980 1.438613 2.330 0.14 4.99 0.9725
## Yield.tons. 50 27.0592 13.345789 28.970 3.86 48.02 16.1900
## 75th Skew Kurtosis
## Farm_Area.acres. 368.1075 -0.13195502 -1.250421
## Fertilizer_Used.tons. 6.8850 0.07462449 -1.142863
## Pesticide_Used.kg. 3.4175 0.24571341 -1.171036
## Yield.tons. 37.8600 -0.08289283 -1.263373####Kriteria Uji Tolak H0 jika P value < alpha
####Keputusan dan Kesimpulan mardia kurtosis lebih kecil dari alpha (0,05) maka tolak H0, berarti data tak berdistribusi normal
##Uji Homogenitas Multivariat
####Hipotesis: H0 : s1 = s2 = s3, matriks kovarians grup adalah sama. H1 : Minimal ada satu matriks kovarians grup yang berbeda
####Statistik Uji1
{r} Y <- data1_fix group <- data$Crop_Type group result <- boxM(Y=data1_fix, group = group) print(result)
####Kriteria Uji Tolak H0 jika p-value < a
####Keputusan dan Kesimpulan Pvalue untuk mardia skewness dan mardia kurtosis lebih kecil dari alpha (0,05) maka terima H0 berarti data tidak berdistribusi normal
####Uji Independensi Asumsi Independensi terpenuhi jika pdata didapatkan secara random.
Kriteria Uji
Tolak H0 jika P-Value < a terima dalam hal lainnya
####Keputusan Tipe irigasi : Tolak H0 Jenis Tanaman : Tolak H0 Irigasi : Jenis Tanaman : Tolak H0
####Kesimpulan
Dengan taraf signifikansi 5% dapat disimpulkan bahwa: 1.Tipe irigasi tidak berpengaruh signifikan terhadap hasil panen, penggunaan pupuk, dan penggunaan air. 2.Jenis tanaman tidak berpengaruh terhadap hasil panen, penggunaan pupuk, dan penggunaan air. 3.Interaksi antara tipe irigasi dan jenis tanaman tidak berpengaruh signifikan terhadap hasil panen, penggunaan pupuk, dan penggunaan air.