Notes Theme: - Kelas A: cayman
- Kelas B: tactile
- Kelas C: architect
- Kelas D: hpstr
Library:
> # install.packages("knitr")
> # install.packages("rmarkdown")
> # install.packages("prettydoc")
> # install.packages("equatiomatic")1 PENDAHULUAN
1.1 Latar Belakang Kasus
Mahasiswa sebagai kelompok generasi muda yang sedang berada dalam proses pendidikan tinggi, sering kali dihadapkan pada berbagai tantangan dalam mencapai keseimbangan antara akademik dan kehidupan sosial. Gaya hidup mahasiswa termasuk pola makan, aktivitas fisik, kebiasaan tidur, serta manajemen stres memiliki peran penting dalam menentukan kualitas dan hasil dari proses pembelajaran yang mereka jalani.
Seiring berjalannya waktu, gaya hidup mahasiswa modern semakin dipengaruhi oleh faktor eksternal, seperti perkembangan teknologi, tekanan sosial, dan tuntutan dunia kerja. Berbagai kebiasaan, seperti tidur larut malam, mengonsumsi makanan cepat saji, atau kurangnya aktivitas fisik, dapat memengaruhi tingkat konsentrasi, energi, dan kemampuan berpikir kritis mahasiswa. Semua faktor ini dapat berimplikasi langsung terhadap performa akademik mahasiswa, baik dalam hal nilai, penyelesaian tugas, maupun kemampuan berpikir kritis.
Di sisi lain, gaya hidup sehat yang mencakup pola makan seimbang, cukup tidur, dan olahraga teratur, dapat meningkatkan kemampuan kognitif, daya ingat, dan ketahanan mental mahasiswa dalam menghadapi tantangan akademik. Banyak penelitian yang menunjukkan bahwa kebiasaan hidup yang baik dapat meningkatkan konsentrasi, energi, dan produktivitas, yang semuanya berkontribusi positif terhadap pencapaian akademik.
Namun, meskipun banyak penelitian yang menunjukkan pengaruh gaya hidup terhadap performa akademik, masih ada ketidaktahuan dan kekurangan pemahaman mengenai kebiasaan apa saja yang paling efektif dalam mendukung keberhasilan akademik mahasiswa. Oleh karena itu, penting untuk mengeksplorasi lebih dalam mengenai bagaimana gaya hidup mahasiswa memengaruhi kinerja akademik mereka, serta untuk memberikan rekomendasi yang dapat membantu mahasiswa mengoptimalkan gaya hidup mereka agar dapat mencapai tujuan akademik yang diinginkan.
Dengan latar belakang ini, penelitian ini bertujuan untuk menilai pengaruh gaya hidup mahasiswa terhadap performa akademik mereka dan memberikan wawasan mengenai bagaimana mahasiswa dapat memodifikasi kebiasaan mereka untuk mencapai hasil yang lebih baik di bidang akademik.
1.2 Latar Belakang Metode
Dalam penelitian data yang melibatkan banyak variabel atau dimensi, sering kali diperlukan teknik analisis yang dapat membantu mengidentifikasi pola atau kelompok tersembunyi dalam data. Dua metode yang sering digunakan dalam konteks ini adalah K-Means Clustering dan Analisis Faktor. Kedua metode ini memiliki tujuan dan pendekatan yang berbeda, namun keduanya berfungsi untuk mengurangi kompleksitas data dan membantu dalam interpretasi hasil.
K-Means Clustering
K-Means Clustering adalah salah satu metode dalam pembelajaran tidak terawasi (unsupervised learning) yang digunakan untuk mengelompokkan data ke dalam beberapa kelompok (cluster) berdasarkan kesamaan karakteristik antar data. Metode ini bertujuan untuk mengoptimalkan pemisahan antar kelompok sehingga elemen dalam satu kelompok memiliki kesamaan yang lebih tinggi dibandingkan dengan elemen dari kelompok lain. Prosesnya dimulai dengan memilih jumlah cluster yang diinginkan (k), kemudian algoritma ini secara iteratif memperbarui posisi pusat cluster (centroid) hingga hasil clustering mencapai konvergensi, di mana data tidak lagi berubah kelompoknya.
K-Means Clustering banyak digunakan dalam berbagai bidang, termasuk segmentasi pasar, pengelompokkan data berdasarkan karakteristik tertentu, dan analisis pola perilaku. Dalam konteks penelitian akademik, metode ini bisa digunakan untuk mengidentifikasi kelompok mahasiswa dengan pola gaya hidup yang serupa, sehingga dapat dianalisis lebih lanjut bagaimana hubungan kelompok-kelompok ini dengan performa akademik mereka.
Analisis Faktor
Analisis Faktor adalah teknik statistik yang digunakan untuk mengidentifikasi struktur yang mendasari variabel-variabel yang diamati. Metode ini bertujuan untuk mengurangi jumlah variabel dalam dataset dengan mengelompokkan variabel-variabel yang berkorelasi tinggi ke dalam satu atau lebih faktor yang lebih mendasar. Dengan menggunakan analisis faktor, kita dapat mereduksi dimensi data tanpa kehilangan informasi yang relevan.
Analisis faktor sangat berguna ketika data yang dikumpulkan memiliki banyak variabel yang mungkin saling berhubungan. Dengan mengidentifikasi faktor-faktor utama yang mempengaruhi data, analisis ini membantu dalam memahami faktor-faktor dasar yang memengaruhi perilaku atau kejadian tertentu. Dalam konteks gaya hidup mahasiswa, misalnya, analisis faktor bisa digunakan untuk mengidentifikasi faktor-faktor utama (seperti pola makan, aktivitas fisik, atau kebiasaan tidur) yang memengaruhi performa akademik mereka.
Kaitan Antara K-Means Clustering dan Analisis Faktor
K-Means Clustering dan Analisis Faktor sering digunakan secara bersamaan untuk mendapatkan pemahaman yang lebih dalam dari dataset. Analisis faktor dapat digunakan terlebih dahulu untuk mereduksi dimensi data dan mengidentifikasi faktor utama yang mempengaruhi variabel-variabel dalam dataset. Setelah itu, K-Means Clustering dapat diterapkan untuk mengelompokkan objek (misalnya, mahasiswa) berdasarkan faktor-faktor utama yang telah diidentifikasi. Penggabungan kedua metode ini memungkinkan analisis yang lebih efektif, di mana peneliti dapat menemukan pola yang tersembunyi dalam data dan mengelompokkan entitas dengan karakteristik yang serupa, memberikan wawasan yang lebih kaya mengenai hubungan antara gaya hidup dan performa akademik mahasiswa.
Dengan menggunakan kombinasi K-Means Clustering dan Analisis Faktor, penelitian ini diharapkan dapat memberikan gambaran yang lebih jelas tentang bagaimana gaya hidup mahasiswa dapat dikelompokkan dan faktor-faktor apa saja yang paling berpengaruh terhadap performa akademik mereka.
1.3 Tujuan Penelitian
Tujuan dari penelitian ini adalah sebagai berikut.
Menganalisis pengaruh gaya hidup terhadap performa akademik mahasiswa
Mengidentifikasi pola gaya hidup mahasiswa melalui metode K-Means Clustering
Mengidentifikasi faktor-faktor utama yang memengaruhi gaya hidup mahasiswa melalui Analisis Faktor
Mengintegrasikan hasil K-Means Clustering dan Analisis Faktor untuk mendapatkan wawasan yang lebih komprehensif
1.4 Tinjauan Pustaka
1.4.1 K-Means Clustering
K-Means Clustering adalah teknik pengelompokkan data non-hierarki yang memisahkan data ke dalam cluster, mengelompokkan data dengan fitur yang sama bersama-sama dan mengelompokkan data dengan karakteristik yang berbeda ke dalam kelompok yang berbeda. Metode K-Means membagi data menjadi beberapa kelompok sehingga data dengan karakteristik yang sama berada pada cluster yang sama dan data dengan karakteristik yang berbeda berada pada cluster yang berbeda (Rohmawati, Defiyanti, & Jajuli, 2015). Secara lebih spesifik, algoritma K-Means adalah sebagai berikut, menurut Sarwono yang dikutip oleh Rohmawati, Defiyanti, dan Jajuli (2015):
Menetapkan K sebagai jumlah cluster yang diinginkan.
Menghasilkan nilai random untuk pusat cluster awal (centroid) sebanyak k.
Menggunakan rumus jarak setiap data input terhadap masing-masing centroid menggunakan rumus jarak Euclidian (Euclidian Distance) hingga ditemukan jarak yang paling pendek dari setiap data dengan centroid. Persamaan Euclidian Distance antara lain:
\[ d(x_i, \mu_j) = \sqrt{\sum (x_i - \mu_j)^2} \] Keterangan :
\(x_i\) = data kriteria
\(\mu_j\) = centroid pada cluster ke-j
Mengklasifikasikan setiap data berdasarkan kedekatannya dengan centroid (jarak terkecil).
Memperbaharui nilai centroid. Menurut Rahman dkk (2017) nilai centroid baru diperoleh dari rata-rata cluster yang bersangkutan dengan menggunakan rumus :
\[ C_k = \frac{1}{n_k} \sum d_i \] Keterangan :
\(n_k\) = jumlah data dalam cluster k
\(d_i\) = jumlah dari nilai jarak yang masuk dalam masing-masing cluster
Melakukan perulangan dari langkah 2 hingga 5 sampai anggota tiap cluster tidak ada yang berubah.
Jika langkah terakhir telah terpenuhi, maka nilai pusat cluster (\(\mu_j\)) pada iterasi terakhir akan digunakan sebagai parameter untuk menentukan klasifikasi data.
1.4.2 Analisis Faktor
1.4.2.1 Model Analisis Faktor
Model umum analisis faktor adalah sebagai berikut : \[ x_1 = \lambda_{11}f_1 + \lambda_{12}f_2 + \dots + \lambda_{1k}f_k + u_1 \\ x_2 = \lambda_{21}f_1 + \lambda_{22}f_2 + \dots + \lambda_{2k}f_k + u_2 \\ \vdots \\ x_q = \lambda_{q1}f_1 + \lambda_{q2}f_2 + \dots + \lambda_{qk}f_k + u_q \] Keterangan :
\(x'\) = \([x_1, x_2, \dots, x_q]\) adalah q variabel manifes yang dapat diobservasi
\(f'\) = \([f_1, f_2, \dots, f_q]\) adalah k faktor bersama yang tidak dapat diobservasi dengan \(k < q\)
\(\lambda_{ij}\) = nilai pembobot faktor ketika diestimasi
Model analisis faktor menyiratkan bahwa varians variabel manifes \(x_i\) dapat dihitung dengan \[ \sigma_i^2 = \sum_{j=1}^{k} \lambda_{ij}^2 + \psi_i \] \(\psi_i\) adalah varians dari \(u_i\). \(h_i^2 = \sum_{j=1}^{k} \lambda_{ij}^2\) disebut sebagai komunalitas dari variabel manifes yang mewakili varians yang dimiliki bersama dengan variabel manifes lain melalui faktor bersama. Sedangkan \(\psi_i\) disebut sebagai varians spesifik yang berkaitan dengan variabilitas dalam \(x_i\) yang tidak dimiliki bersama variabel lainnya.
Matriks kovarian populasi dari variabel manifes \(\sum\), dinyatakan dalam bentuk \[ \Sigma = \Lambda \Lambda^` + \Psi \] dimana \(\Psi = \text{diag}(\psi_i)\) Jika diestimasi dengan sampel maka \[ S = \hat{\Lambda} \hat{\Lambda}^` + \Psi \]
1.4.2.2 Measure of Sampling Adequacy (MSA)
Nilai MSA (nilai korelasi matriks anti-image) digunakan untuk menentukan apakah variabel sudah layak untuk dianalisis lebih lanjut. Jika nilai MSA suatu variabel lebih besar dari 0,5, maka variabel tersebut layak untuk dianalisis lebih lanjut. Jika nilai MSA suatu variabel kurang dari 0,5, variabel tersebut harus dikeluarkan dari analisis secara keseluruhan, diurutkan dari variabel yang memiliki nilai MSA terkecil dan tidak lagi digunakan dalam analisis.
1.4.2.3 Kaiser-Meyer-Olkin (KMO) dan Bartlett’s Test of Sphericity
Uji KMO dan Bartlett’s Test of Sphericity digunakan untuk menguji ketepatan penggunaan analisis faktor. Hasil uji KMO harus antara 0,5 dan 1, dan signifikansi Bartlett’s Test of Sphericity harus kurang dari taraf signifikansi (\(\alpha\) = 0,05).
1.4.2.4 Metode Ekstraksi Faktor
Principal Component Analysis (PCA) dan Principal Factor Analysis (PFA) adalah dua pendekatan ekstraksi faktor yang paling umum digunakan. PCA mengatakan bahwa masing-masing variabel dapat dijelaskan dengan kombinasi linier faktor bersama, yang berarti faktor-faktor tersebut mewakili varians total variabel. Sebaliknya, varians dari variabel dapat dibagi menjadi dua bagian, menurut metode PFA. Variasi dari variabel tertentu membentuk bagian pertama, yang dipengaruhi oleh varians dari masing-masing variabel dalam analisis.
1.5 Data
Data yang digunakan adalah data mengenai indikator gaya hidup dan performa akademik. Terdapat 8 variabel yang masing-masing variabel berjumlah 2000. Berikut merupakan cuplikan data penelitian.
# A tibble: 6 × 8
Student_ID Study_Hours_Per_Day Extracurricular_Hours_Per…¹ Sleep_Hours_Per_Day
<dbl> <chr> <chr> <chr>
1 1 6.9 3.8 8.7
2 2 5.3 3.5 8
3 3 5.1 3.9 9.2
4 4 6.5 2.1 7.2
5 5 8.1 0.6 6.5
6 6 6 2.1 8
# ℹ abbreviated name: ¹Extracurricular_Hours_Per_Day
# ℹ 4 more variables: Social_Hours_Per_Day <chr>,
# Physical_Activity_Hours_Per_Day <chr>, GPA <chr>, Stress_Level <chr>Penelitian ini menggunakan data sekunder yang tersedia untuk umum dan dapat digunakan secara terbuka dari website kaggle.com. Data ini disediakan untuk kepentingan akademik, sebagai bahan pembelajaran konsep analisis gaya hidup mahasiswa.
2 SOURCE CODE
2.1 Library
> library(readxl)
> library(plotrix)
> library(ggplot2)
> library(corrplot)
> library(factoextra)
> library(NbClust)
> library(REdaS)
> library(psych)Setiap library memiliki fungsi tertentu sebagai berikut:
readxl: digunakan untuk mengimpor dan membaca data dari excel.
plotrix: menyediakan plot khusus dari fungsi aksesori, seperti scalling warna, penempatan teks, serta pembuatan legenda.
ggplot2: berfungsi untuk membuat grafik secara deklaratif dengan pemetaan variabel berdasarkan estetika dan mendetail sesuai konsep The Grammar of Graphics.
corrplot: menampilkan visualisasi grafis dari matriks korelasi beserta interval kepercayaannya.
factoextra: memvisualisasikan ojek dalam berbagai kelas.
NbClust: menentukan jumlah cluster optimal dan merekomendasikan skema pengelompokkan terbaik.
REdaS: untuk mendukung analisis data dengan menyediakan fungsi yang mudah digunakan untuk statistik deskriptif, inferensial, serta analisis data eksploratif.
psych: digunakan untuk analisis psikometri dan statistik multidimensi.
2.2 Input Data
2.3 Penyesuaian Tipe Data
Dalam file data yang diimpor, kolom selain Student_ID terbaca dalam bentuk karakter (chr). Agar data dapat dianalisis lebih lanjut dengan tepat, tipe data pada kolom selain Student_ID perlu diubah menjadi tipe numerik dan terkhusus untuk kolom Stress_Level perlu diubah menjadi tipe faktor yang terdiri dari tiga kategori, yaitu “Low”, “High”, dan “Moderate”.
> Data$Study_Hours_Per_Day <- as.numeric(as.character(Data$Study_Hours_Per_Day))
> Data$Extracurricular_Hours_Per_Day <- as.numeric(as.character(Data$Extracurricular_Hours_Per_Day))
> Data$Sleep_Hours_Per_Day <- as.numeric(as.character(Data$Sleep_Hours_Per_Day))
> Data$Social_Hours_Per_Day <- as.numeric(as.character(Data$Social_Hours_Per_Day))
> Data$Physical_Activity_Hours_Per_Day <- as.numeric(as.character(Data$Physical_Activity_Hours_Per_Day))
> Data$GPA <- as.numeric(as.character(Data$GPA))
> Data$Stress_Level <- as.factor(as.character(Data$Stress_Level))2.4 Statistika Deskriptif
Fungsi summary memberikan informasi mengenai statistik deskriptif seperti nilai rata-rata, minimum, maksimum, kuartil, dan median dari data.
2.5 Visualisasi Data Stress_Level
Karena variabel Stress_Level bersifat kategorikal, analisis visual
menggunakan pie chart sudah cukup untuk
memvisualisasikannya.Pie chart ini dibuat menggunakan fungsi
pie3D dari package plotrix.
> Level_Stress <- table(Data$Stress_Level)
> persentase <- round(Level_Stress/sum(Level_Stress)*100)
> label <- sprintf("%s %s%%", c("Low", "High", "Moderate"), persentase)
> library(plotrix)
> pie3D(Level_Stress, labels = label, main = "Perbandingan Tingkat Stress", col = c("red", "orange", "blue"), labelcex = 0.8, explode = 0.1) 2.6 Analisis Korelasi
Matriks korelasi menyajikan hubungan antara dua variabel dengan lebih
rinci, sementara fungsi corrplot digunakan untuk
memvisualisasikan korelasi tersebut dalam bentuk grafik. Dalam analisis
ini, parameter tl.cex atau ukuran teks pada judul grafik
diatur sedemikian rupa agar tetap terbaca dengan jelas tanpa terlihat
terlalu besar.
2.7 K-Means Clustering
2.7.1 Cluster Optimal
Jumlah cluster yang perlu dibentuk ditentukan menggunakan metode Silhouette (Silhouette method). Cluster yang optimal akan memiliki rata-rata Silhouette coefficient yang paling tinggi, yang menandakan bahwa data dalam cluster tersebut lebih kohesif dan terpisah dengan baik dari cluster lainnya.
2.7.2 Visualisasi Hasil Clustering
Fungsi set.seed yang sama akan menjaga agar
cluster yang digunakan tetap konsisten dan nomor
cluster tidak berubah.
> #Berdasarkan Study_Hours_Per_Day dan GPA
> set.seed(222)
> ggplot(Data,
+ aes(x = Study_Hours_Per_Day, y = GPA)) +
+ geom_point(stat = "identity", aes(color = as.factor(k2$cluster))) +
+ scale_color_discrete(name = " ", breaks = c("1", "2"),
+ labels = c("Cluster 1", "Cluster 2")) +
+ ggtitle("Clustering berdasarkan Waktu Belajar per hari dan GPA",
+ subtitle = "K-means Clustering")> #Berdasarkan Extracurricular_Hours_Per_Day dan GPA
> set.seed(222)
> ggplot(Data,
+ aes(x = Extracurricular_Hours_Per_Day, y = GPA)) +
+ geom_point(stat = "identity", aes(color = as.factor(k2$cluster))) +
+ scale_color_discrete(name = " ", breaks = c("1", "2"),
+ labels = c("Cluster 1", "Cluster 2")) +
+ ggtitle("Clustering berdasarkan Waktu Ekskul per hari dan GPA",
+ subtitle = "K-means Clustering")> #Berdasarkan Sleep_Hours_Per_Day dan GPA
> set.seed(222)
> ggplot(Data,
+ aes(x = Sleep_Hours_Per_Day, y = GPA)) +
+ geom_point(stat = "identity", aes(color = as.factor(k2$cluster))) +
+ scale_color_discrete(name = " ", breaks = c("1", "2"),
+ labels = c("Cluster 1", "Cluster 2")) +
+ ggtitle("Clustering berdasarkan Waktu Tidur per hari dan GPA",
+ subtitle = "K-means Clustering")> #Berdasarkan Social_Hours_Per_Day dan GPA
> set.seed(222)
> ggplot(Data,
+ aes(x = Social_Hours_Per_Day, y = GPA)) +
+ geom_point(stat = "identity", aes(color = as.factor(k2$cluster))) +
+ scale_color_discrete(name = " ", breaks = c("1", "2"),
+ labels = c("Cluster 1", "Cluster 2")) +
+ ggtitle("Clustering berdasarkan Waktu Bersosialisasi per hari dan GPA",
+ subtitle = "K-means Clustering")> #Berdasarkan Physical_Activity_Hours_Per_Day dan GPA
> set.seed(222)
> ggplot(Data,
+ aes(x = Physical_Activity_Hours_Per_Day, y = GPA)) +
+ geom_point(stat = "identity", aes(color = as.factor(k2$cluster))) +
+ scale_color_discrete(name = " ", breaks = c("1", "2"),
+ labels = c("Cluster 1", "Cluster 2")) +
+ ggtitle("Clustering berdasarkan Waktu Aktivitas Fisik per hari dan GPA",
+ subtitle = "K-means Clustering")2.8 Analisis Faktor
2.8.1 Input Data yang telah Diperbaiki
Pada argumen Data diketahui bahwa data bersifat
singular, dimana determinannya sama dengan 0. Hal ini akan berdampak
untuk analisis berikutnya yaitu uji KMO. Dimana hasil output
KMO tidak akan keluar. Dengan demikian, diputuskan untuk menghapus salah
satu variabel sebagai solusi untuk mengatasi permasalahan ini.
2.8.2 Uji KMO
Setelah salah satu variabel dihapuskan, data tidak lagi bersifat
singular, analisis dapat dilanjutkan yakni dengan uji KMO. Statistik KMO
dihitung dengan fungsi KMOS(). Terlebih dahulu perlu
mengaktifkan packages REdaS. Argumen berisikan
Data. Fungsi KMOS() juga menghitung MSA untuk
tiap variabel. Jika nilai MSA < 0.5 maka variabel tersebut tidak
dapat di analisis lebih lanjut.
2.8.3 Input Data Tanpa Variabel dengan MSA < 0.5
Fungsi read_excel() digunakan untuk memuat data dari
file Data6_baru dengan argumen yang berisikan lokasi file
data tersimpan. Data tersebut disimpan dengan nama
Data6_baru. Kemudian digunakan fungsi
head(Data6_baru) untuk menampilkan 6 data teratas beserta
judul kolomnya.
2.8.4 Scree Plot
Digunakan fungsi cor() untuk menghitung matriks korelasi
antar variabel pada data. Argumen berisikan Data6_baru yang
sudah dalam bentuk data frame yang kemudian disimpan pada
korelasi. Lalu, nillai dan vektor eigen dapat ditentukan
dengan fungsi eigen() dengan argumen korelasi
yang kemudian disimpan dalam eigen.
> korelasi = cor(Data6_baru[2:4])
> eigen = eigen(korelasi)
> screeplot = plot(eigen$values, main = "Scree Plot", xlab = "Faktor", ylab = "Eigen Values", pch = 16, type = "o", col = "red", lwd = 1) + axis(1, at =seq(1,9)) +abline (h=1, col = "blue")Membuat scree plot digunakan fungsi plot() dengan
argumen sebagai berikut :
x = data yang ingin dibuat plot
(eigen$values)
main = judul plot (Scree Plot)
xlab = label sumbu x (Faktor)
ylab = label sumbu y (Eigen Values)
pch = kode jenis titik yang dipakai untuk mewakili data
(16)
type = tipe plot (o)
col = warna titik dan garis (red)
lwd = ketebalan garis (1)
Selanjutnya, ditambahkan batas berupa garis horizontal pada nilai
eigen = 1, sehingga fungsi plot digabungkan dengan fungsi
axis() dan abline()
Fungsi axis() memiliki argumen posisi sumbu dan nilai
pada sumbu (1 dan at=seq(1,28)).
Fungsi abline() memiliki argumen nilai pada sumbu tegak
dan warna garis (h=1 dan col=blue)
2.8.5 Ekstraksi Faktor dengan PCA
Ekstraksi faktor dengan PCA menggunakan fungsi
principal(). Terlebih dahulu mengaktifkan package
psych.
> library(psych)
> PCA = principal(r = korelasi, nfactors = 1, rotate = "varimax")
> PCA$communalityBerisi argumen:
r = matriks korelasi antar variabel
(korelasi)
nfactors = banyak komponen untuk diekstrak
(nfactors = 1)
rotate = metode rotasi yang digunakan
(varimax)
Selanjutnya ditampilkan komunalitas setiap variabel dengan fungsi
PCA$communality
2.8.6 Ekstraksi Faktor dengan PFA
Ekstraksi faktor dengan PFA menggunakan fungsi fa().
Terlebih dahulu mengaktifkan package `psych
> library(psych)
> PFA = fa(r = Data6_baru[2:4], nfactors = 1, rotate = "varimax", fm = "pa")
> PFA
> PFA$scoresBerisi argumen :
r = matriks korelasi atau data awal yang nantinya akan
dihitung otomatis matriks korelasinya (Data6_baru)
nfactors = banyak komponen untuk diekstrak
(nfactors = 1)
rotate = rotasi yang digunakan
(varimax)
fm = metode ekstraksi faktor (pa)
2.8.7 Visualisasi
Visualisasi dapat dilakukan dengan fungsi fa.diagram.
Terlebih dahulu mengaktifkan package psych.
Menyimpan loadings dari PFA yang diberi nama loads
dengan fungsi PFA$loadings
Fungsi fa.diagram dengan argumen loadings dari metode
PFA (loads)
Kemudian, menampilkan nilai loading dengan fungsi round
untuk membulatkan dengan argumen PFA$loadings[1:3,] yaitu
loadings dari 3 variabel dan 4 merupakan 4 angka di
belakang koma.
3 HASIL DAN PEMBAHASAN
3.1 Statistika Deskriptif
Student_ID Study_Hours_Per_Day Extracurricular_Hours_Per_Day
Min. : 1.0 Length:2000 Length:2000
1st Qu.: 500.8 Class :character Class :character
Median :1000.5 Mode :character Mode :character
Mean :1000.5
3rd Qu.:1500.2
Max. :2000.0
Sleep_Hours_Per_Day Social_Hours_Per_Day Physical_Activity_Hours_Per_Day
Length:2000 Length:2000 Length:2000
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
GPA Stress_Level
Length:2000 Length:2000
Class :character Class :character
Mode :character Mode :character
Berdasarkan hasil summary, terlihat bahwa rata-rata waktu belajar mahasiswa adalah 7 jam per hari. Dimana waktu belajar paling singkat yakni selama 5 jam per hari dan waktu belajar paling lama mahasiswa adalah selama 10 jam per hari. Berikutnya, rata-rata waktu ekstrakurikuler mahasiswa adalah selama 2 jam per hari. Dimana waktu ekstrakulikuler paling singkat yakni selama 4 jam per hari. Rata-rata waktu tidur mahasiswa adalah selama 7.5 jam per harinya, dimana waktu tidur mahasiswa paling singkat menyentuh angka 5 jam per hari dan waktu tidur yang paling lama adalah 10 jam per hari. Selanjutnya, rata-rata waktu mahasiswa untuk bersosialisasi adalah selama 3 jam per hari. Dimana waktu bersosialisasi mahasiswa paling lama adalah selama 6 jam per hari. Rata-rata aktivitas fisik yang dilakukan oleh mahasiswa adalah selama 4 jam per hari, dimana waktu aktivitas fisik paling lama yakni 13 jam per hari. Rata-rata GPA yang diperoleh mahasiswa adalah sebesar 3.116, dimana GPA terkecil menyentuh angka 2.240 dan GPA terbesar menyentuh angka 4.0 yakni angka sempurna. Berikutnya, jumlah mahasiswa yang memiliki tingkat stress pada level tertinggi yakni sebanyak 1029 mahasiswa.
3.2 Visualisasi Data Stress_Level
Berdasarkan hasil pie chart di atas dapat disimpulkan bahwa
persentase mahasiswa yang memiliki tingkat stress paling tinggi adalah
sebesar 51%. Lalu, disusul dengan tingkat stress sedang yakni sebesar
34%, serta persentase tingkat stress paling rendah sebesar 15%.
3.3 Analisis Korelasi
Gambar Analisis Korelasi
Berdasarkan output di atas, dapat dilihat bahwa nilai korelasi antar variabel menunjukkan angka yang cenderung kecil, yang mengindikasikan bahwa masing-masing variabel memiliki keragaman yang cukup berbeda. Matriks korelasi menyajikan hubungan antara dua variabel dengan lebih rinci, sementara grafik di atas digunakan untuk memvisualisasikan korelasi tersebut.
3.4 K-Means Clustering
3.4.1 Cluster Optimal
K-means clustering with 2 clusters of sizes 831, 1169
Cluster means:
Study_Hours_Per_Day Extracurricular_Hours_Per_Day Sleep_Hours_Per_Day
1 6.787485 1.615042 6.849940
2 7.965098 2.256715 7.964243
Social_Hours_Per_Day Physical_Activity_Hours_Per_Day GPA
1 1.955716 6.791817 3.015102
2 3.236869 2.577074 3.187656
Clustering vector:
[1] 2 2 1 1 1 1 2 1 1 2 2 1 1 2 1 1 2 2 1 2 1 2 2 2 1 2 2 1 1 2 2 1 1 2 1 2 2
[38] 2 2 1 1 2 1 2 2 2 2 2 2 2 2 2 1 2 1 1 1 1 2 1 2 2 1 1 1 2 1 2 2 1 2 1 2 2
[75] 2 1 2 1 2 2 1 1 2 2 2 1 1 2 2 2 2 2 1 2 2 2 2 2 2 2 2 2 1 2 2 1 1 2 2 2 1
[112] 2 1 1 1 2 1 2 2 1 1 2 2 1 2 1 1 1 2 2 1 1 1 2 2 2 1 2 1 2 1 2 2 2 2 2 2 2
[149] 2 2 2 2 2 2 2 1 1 2 1 2 2 2 2 1 2 2 1 1 1 2 2 1 2 1 2 2 2 2 2 2 1 2 1 1 1
[186] 2 1 1 2 2 1 2 2 2 2 2 2 1 1 2 1 2 2 1 1 2 2 2 2 1 2 2 2 2 1 1 2 2 2 2 1 2
[223] 1 2 2 2 2 2 2 2 2 1 2 2 1 2 2 1 1 1 1 2 1 1 1 2 1 2 2 2 1 2 2 1 2 1 2 2 2
[260] 2 2 2 1 2 1 2 1 2 1 1 1 2 1 2 2 2 2 1 2 2 2 1 2 1 2 1 2 2 2 1 1 1 1 1 2 2
[297] 1 2 2 1 1 2 2 1 1 1 2 2 2 1 2 2 2 1 1 2 2 2 1 2 2 1 1 2 1 1 1 2 2 1 2 1 1
[334] 2 1 2 2 2 2 1 1 2 2 2 1 1 2 2 1 2 2 2 2 2 1 2 2 2 2 2 2 1 1 2 2 2 2 2 1 1
[371] 1 2 1 1 1 1 2 1 2 2 1 2 1 1 2 2 2 1 2 1 2 2 1 1 2 1 2 2 2 2 2 1 2 2 2 2 2
[408] 2 1 1 2 2 1 2 1 2 2 2 1 2 2 2 1 1 2 1 2 1 2 1 2 2 1 1 1 2 1 2 2 2 2 2 2 1
[445] 1 1 2 2 1 1 1 2 2 2 2 1 1 1 1 1 1 1 2 2 2 2 1 2 2 2 1 2 2 2 2 1 2 2 1 2 2
[482] 2 2 2 2 1 2 2 1 2 1 2 2 2 2 1 1 2 1 2 2 1 2 2 2 1 2 2 1 2 1 1 1 1 2 1 1 1
[519] 2 2 1 2 2 2 2 1 2 1 1 1 1 2 2 1 2 2 1 1 2 1 1 2 2 2 2 1 2 1 2 1 1 2 2 1 1
[556] 2 2 2 2 1 2 1 2 2 2 2 2 1 2 2 2 2 1 2 1 1 2 1 2 1 2 2 1 2 1 2 2 2 2 1 2 1
[593] 1 1 2 1 1 2 2 2 2 2 2 2 1 1 2 2 1 1 2 2 1 2 2 1 2 1 1 2 2 1 1 1 2 1 2 2 2
[630] 1 1 2 1 2 1 2 2 1 2 2 1 2 1 2 2 1 2 1 1 2 2 1 2 1 1 2 2 2 2 2 2 2 2 2 2 2
[667] 1 1 1 2 2 2 2 1 1 1 1 1 1 2 1 1 2 2 2 2 2 2 2 1 1 1 2 1 2 1 1 1 2 1 2 1 1
[704] 1 2 2 1 2 2 2 2 1 2 1 2 2 2 2 1 2 1 2 2 2 1 1 2 2 2 1 1 2 1 2 2 1 2 2 1 2
[741] 1 1 1 2 2 2 2 2 2 1 2 2 2 2 1 2 1 1 2 1 1 2 1 2 1 1 2 1 1 2 2 1 2 2 1 2 1
[778] 2 2 2 1 1 1 2 1 1 2 1 2 2 1 1 2 2 2 2 2 1 1 2 1 1 2 1 2 1 1 1 2 1 2 2 1 1
[815] 2 1 1 1 1 1 2 2 1 2 2 2 2 1 2 2 2 2 1 2 1 1 1 2 1 2 1 2 1 1 1 1 2 1 2 2 2
[852] 1 2 2 2 2 1 1 2 1 1 2 1 1 2 2 1 1 1 2 2 2 2 1 2 2 2 2 2 1 2 1 1 2 2 1 2 2
[889] 2 2 1 2 2 1 2 1 1 1 2 2 2 1 2 1 2 1 1 2 2 2 2 1 1 1 2 1 1 1 2 1 2 1 1 2 1
[926] 1 2 2 2 1 1 2 1 2 1 1 1 2 2 1 2 1 2 2 2 2 2 2 1 2 2 1 1 2 2 1 2 2 1 1 2 1
[963] 2 2 2 1 1 1 2 2 2 1 2 1 1 2 1 1 2 2 2 2 2 2 2 1 1 2 1 2 2 2 1 2 2 2 2 1 2
[1000] 1 1 2 2 2 2 2 2 1 1 2 2 1 1 1 2 1 2 1 1 2 1 2 2 2 2 2 2 1 2 2 1 2 2 1 1 2
[1037] 2 1 2 1 1 1 2 1 2 1 2 2 2 1 2 1 1 2 2 1 1 1 1 1 1 2 2 2 2 2 1 1 2 2 1 2 1
[1074] 2 2 2 1 1 1 2 2 1 2 2 1 2 2 2 2 2 2 1 1 1 2 2 2 2 2 1 2 1 2 1 1 2 2 1 2 1
[1111] 2 1 2 2 1 1 2 1 1 2 2 2 2 2 2 2 1 1 2 2 1 2 1 1 1 2 1 2 2 2 2 1 1 2 1 1 2
[1148] 2 2 2 1 2 2 2 2 2 2 2 1 1 1 1 1 2 2 1 2 2 2 1 1 2 1 2 1 1 1 1 1 1 1 1 2 1
[1185] 2 1 2 2 1 2 2 2 1 2 2 2 1 1 2 2 1 2 2 2 2 2 1 1 2 1 1 1 2 1 1 2 2 1 2 2 2
[1222] 2 2 2 2 1 2 2 1 2 2 1 2 2 2 1 1 2 1 1 1 2 2 1 2 2 2 2 1 1 1 1 2 1 2 1 1 1
[1259] 2 1 2 2 1 2 1 2 2 2 1 1 2 1 1 2 1 1 2 2 2 1 2 1 1 2 2 2 2 2 1 2 2 2 2 2 2
[1296] 2 1 2 1 2 2 1 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 1 2 2 2 1 2 2 1 1 2 2 2 1 2 2
[1333] 1 2 1 1 1 1 2 1 2 2 2 1 2 2 2 1 1 2 2 1 2 2 1 2 2 2 1 1 2 2 2 2 2 2 2 2 2
[1370] 2 2 1 2 1 2 1 2 2 1 2 2 2 2 1 1 2 2 1 1 2 1 1 2 2 2 2 1 2 2 2 1 1 2 2 2 1
[1407] 1 2 1 1 1 1 2 2 1 1 1 1 1 1 1 2 1 2 2 1 1 2 1 2 2 2 1 1 2 2 1 2 2 2 2 2 2
[1444] 1 2 1 1 2 1 2 1 2 2 2 2 1 1 1 1 2 2 2 2 1 2 2 1 1 2 1 1 2 1 1 1 1 2 1 2 2
[1481] 2 1 1 1 2 2 2 1 2 2 2 2 1 2 2 2 1 1 1 1 2 1 2 1 2 1 2 2 1 2 1 2 1 2 2 1 2
[1518] 1 1 2 1 2 1 1 2 1 2 2 2 2 1 2 2 1 2 1 2 1 2 1 1 2 1 2 2 1 1 2 2 1 2 2 2 2
[1555] 1 2 2 2 1 1 2 1 1 2 2 1 2 2 1 2 2 2 1 1 2 2 2 2 2 1 1 1 1 2 1 2 2 2 2 2 1
[1592] 2 1 2 1 2 2 2 2 1 1 1 2 1 1 2 1 2 2 2 2 2 1 2 1 1 2 1 2 2 2 1 2 2 2 2 2 2
[1629] 1 2 2 1 1 2 2 2 2 2 1 2 2 1 1 2 2 1 1 1 1 2 2 1 1 1 1 2 2 2 2 2 2 1 2 1 1
[1666] 2 1 1 1 2 2 2 1 1 1 2 1 2 2 1 2 2 1 2 1 2 1 1 1 1 2 2 1 2 2 1 2 2 2 2 1 2
[1703] 2 1 2 1 1 1 2 2 2 1 2 2 2 1 1 1 2 2 1 1 2 2 1 1 2 2 1 2 1 1 2 1 2 2 2 2 2
[1740] 2 1 2 2 2 2 2 1 1 1 2 2 2 2 2 2 2 2 2 1 2 1 1 1 2 1 2 1 2 2 2 2 2 1 1 2 2
[1777] 2 1 2 2 2 2 2 1 2 1 2 2 2 1 1 1 1 1 2 2 2 1 1 2 1 2 1 2 2 2 1 1 1 2 2 1 1
[1814] 2 2 1 2 2 1 1 2 2 2 2 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2 1 2 1 1 1 1 2 1 2 2 2
[1851] 1 1 1 2 2 2 2 1 2 1 2 2 1 2 2 1 1 2 2 2 2 1 2 2 2 2 1 2 2 2 2 2 1 2 2 1 2
[1888] 1 2 2 1 1 2 2 1 1 1 2 2 1 1 2 2 1 2 1 2 2 1 1 2 2 2 2 1 2 2 2 2 2 2 1 1 2
[1925] 2 1 1 1 2 2 1 1 2 2 1 2 1 1 1 2 2 2 2 1 1 2 2 2 2 2 2 1 2 1 2 2 1 2 2 1 1
[1962] 2 2 2 2 1 1 2 2 2 1 2 2 2 2 2 2 2 2 2 2 1 1 2 2 2 2 2 2 2 2 1 2 2 2 1 1 1
[1999] 2 2
Within cluster sum of squares by cluster:
[1] 7461.27 11124.97
(between_SS / total_SS = 37.0 %)
Available components:
[1] "cluster" "centers" "totss" "withinss" "tot.withinss"
[6] "betweenss" "size" "iter" "ifault" Berdasarkan output di atas, dapat disimpulkan bahwa 2 merupakan jumlah cluster yang tepat karena memiliki rata-rata Silhouette coefficient tertinggi, yang menunjukkan cluster yang optimal. Rata-rata Study_Hours_Per_Day, Extracurricular_Hours_Per_Day, Sleep_Hours_Per_Day,Social_Hours_Per_Day, Physical_Activity_Hours_Per_Day, dan GPA untuk masing-masing cluster dapat dilihat pada bagian cluster means, yang memberikan gambaran lebih jelas mengenai karakteristik tiap cluster berdasarkan variabel-variabel tersebut.
3.4.2 Visualisasi Hasil Clustering
Berdasarkan grafik visualisasi di atas, berdasarkan variabel
Study_Hours_Per_Day dan GPA cluster tersebut memiliki
karakteristik sebagai berikut:
Cluster 1: Mahasiswa yang memiliki waktu belajar cenderung lebih singkat per harinya dan memperoleh GPA yang cenderung lebih rendah.
Cluster 2: Mahasiswa yang memiliki waktu belajar cenderung lebih lama per harinya dan memperoleh GPA yang cenderung lebih tinggi.
Berdasarkan grafik visualisasi di atas, berdasarkan variabel
Extracurricular_Hours_Per_Day dan GPA cluster tersebut memiliki
karakteristik sebagai berikut:
Cluster 1: Mahasiswa yang memiliki waktu ekstrakulikuler cenderung lebih singkat per harinya dan memperoleh GPA yang cenderung lebih rendah.
Cluster 2: Mahasiswa yang memiliki waktu ekstrakulikuler cenderung lebih lama per harinya dan memperoleh GPA yang cenderung lebih tinggi.
Namun, jika tidak dilihat secara detail, cluster 1 dan cluster 2 hampir memiliki kesamaan karakteristik.
Berdasarkan grafik visualisasi di atas, berdasarkan variabel
Sleep_Hours_Per_Day dan GPA cluster tersebut memiliki
karakteristik sebagai berikut:
Cluster 1: Mahasiswa yang memiliki waktu tidur cenderung lebih singkat per harinya dan memperoleh GPA yang cenderung lebih rendah.
Cluster 2: Mahasiswa yang memiliki waktu ekstrakulikuler cenderung lebih lama per harinya dan memperoleh GPA yang cenderung lebih tinggi.
Namun, jika tidak dilihat secara detail, cluster 1 dan cluster 2 hampir memiliki kesamaan karakteristik.
Berdasarkan grafik visualisasi di atas, berdasarkan variabel
Social_Hours_Per_Day dan GPA cluster tersebut memiliki
karakteristik sebagai berikut:
Cluster 1: Mahasiswa yang memiliki waktu bersosialisasi cenderung lebih singkat per harinya dan memperoleh GPA yang cenderung lebih rendah.
Cluster 2: Mahasiswa yang memiliki waktu bersosialisasi cenderung lebih lama per harinya dan memperoleh GPA yang cenderung lebih tinggi.
Namun, jika tidak dilihat secara detail, cluster 1 dan cluster 2 hampir memiliki kesamaan karakteristik.
Berdasarkan grafik visualisasi di atas, berdasarkan variabel
Physical_Activity_Hours_Per_Day dan GPA cluster tersebut
memiliki karakteristik sebagai berikut:
Cluster 1: Mahasiswa yang memiliki waktu melakukan aktivitas fisik lebih lama per harinya dan memperoleh GPA yang cenderung lebih rendah.
Cluster 2: Mahasiswa yang memiliki waktu melakukan aktivitas fisik lebih singkat per harinya dan memperoleh GPA yang cenderung lebih tinggi.
3.5 Analisis Faktor
3.5.1 Input Data yang telah Diperbaiki
# A tibble: 6 × 7
Student_ID Study_Hours_Per_Day Extracurricular_Hours_Per…¹ Sleep_Hours_Per_Day
<dbl> <chr> <chr> <chr>
1 1 6.9 3.8 8.7
2 2 5.3 3.5 8
3 3 5.1 3.9 9.2
4 4 6.5 2.1 7.2
5 5 8.1 0.6 6.5
6 6 6 2.1 8
# ℹ abbreviated name: ¹Extracurricular_Hours_Per_Day
# ℹ 3 more variables: Social_Hours_Per_Day <chr>, GPA <chr>, Stress_Level <chr>Pada argumen Data diketahui bahwa data bersifat
singular, dimana determinannya sama dengan 0. Hal ini akan berdampak
untuk analisis berikutnya yaitu uji KMO. Dimana hasil output
KMO tidak akan keluar. Dengan demikian, diputuskan untuk menghapus salah
satu variabel sebagai solusi untuk mengatasi permasalahan ini. Variabel
Physical_Activity_Hours_Per_Day dihapus, lalu input
kembali data baru dimana variabel sudah terhapus.
3.5.2 Uji KMO
Kaiser-Meyer-Olkin Statistics
Call: KMOS(x = Data6[2:6])
Measures of Sampling Adequacy (MSA):
Study_Hours_Per_Day Extracurricular_Hours_Per_Day
0.5053057 0.4778397
Sleep_Hours_Per_Day Social_Hours_Per_Day
0.4963371 0.5534276
GPA
0.5038985
KMO-Criterion: 0.5068608Berdasarkan output di atas, diperoleh statistik KMO-Criterion sebesar 0.51 > 0.5, sehingga dapat disimpulkan analisis faktor dapat diterapkan menggunakan matriks korelasi antar variabel data. Nilai MSA untuk variabel Extracurricular_Hours_Per_Day dan Sleep_Hours_Per_Day < 0.5 sehingga variabel tersebut tidak dapat dianalisis lebih lanjut.
3.5.3 Input Data Tanpa Variabel dengan MSA < 0.5
Student_ID Study_Hours_Per_Day Social_Hours_Per_Day GPA Stress_Level
1 1 6.9 2.8 2.99 Moderate
2 2 5.3 4.2 2.75 Low
3 3 5.1 1.2 2.67 Low
4 4 6.5 1.7 2.88 Moderate
5 5 8.1 2.2 3.51 High
6 6 6 0.3 2.85 Moderate
7 7 8 5.7 3.08 High
8 8 8.4 3 3.2 High
9 9 5.2 4 2.82 Low
10 10 7.7 4.5 2.76 Moderate
11 11 9.7 2.5 3.43 High
12 12 6.9 2.1 2.97 Moderate
13 13 6.4 4.8 2.82 High
14 14 5 4.4 2.87 Low
15 15 8.9 0.7 3.4 High
16 16 6.7 2 3.2 Moderate
17 17 8.6 1.6 3.36 High
18 18 8.8 2 3.19 High
19 19 7.6 0.6 3.16 High
20 20 6.6 1.5 2.93 Moderate
21 21 7.1 0.5 2.9 Moderate
22 22 9.6 2.6 3.34 High
23 23 9 2.6 3.21 High
24 24 6.1 5.2 2.85 Moderate
25 25 5 1.3 2.68 Low
26 26 9.7 3.8 3.29 High
27 27 6.8 0.9 2.64 Moderate
28 28 6.4 3 2.95 Moderate
29 29 5.3 1.4 2.79 Low
30 30 9.9 3.6 3.25 High
31 31 6.2 3.8 3.05 Moderate
32 32 5.5 1.1 3.26 Low
33 33 5.2 0.1 2.47 Low
34 34 8.2 2.3 3.24 High
35 35 9.7 0.7 3.62 High
36 36 9.1 1.2 3.12 High
37 37 5.5 3.4 2.58 Low
38 38 8.6 1.9 3.03 High
39 39 8.2 5.4 3.29 High
40 40 5 4.2 2.61 Low
41 41 8.3 1.4 3.48 High
42 42 8.2 2.3 3.07 High
43 43 5.5 1.5 2.81 Low
44 44 9.5 1.5 3.49 High
45 45 7.9 4.3 2.97 Moderate
46 46 9.7 0.3 3.55 High
47 47 5.1 5.8 2.6 Low
48 48 9.3 3.3 3.09 High
49 49 9.7 0.3 3.47 High
50 50 9.4 2.2 3.18 High
51 51 6.8 4.9 3.12 Moderate
52 52 9 3.1 4 High
53 53 9.5 0.6 3.33 High
54 54 7.3 3.5 3.11 Moderate
55 55 5.2 2.2 2.79 Low
56 56 8.8 0.5 3.1 High
57 57 5.3 3.8 2.39 Low
58 58 9 0.5 3.39 High
59 59 5.1 4 2.52 Low
60 60 5.8 4.3 2.8 Low
61 61 8.3 3.5 3.67 High
62 62 7.1 4.5 3.27 Moderate
63 63 5.1 0.2 2.85 High
64 64 7.4 2.8 3.26 Moderate
65 65 5.9 3.7 2.8 Low
66 66 6.9 5.1 3.06 Moderate
67 67 8.3 3.9 3.26 High
68 68 9.7 3.3 3.38 High
69 69 7.3 1.9 3.15 Moderate
70 70 5.4 0.1 2.69 Low
71 71 9.1 4.2 3.54 High
72 72 8.7 1 3.37 High
73 73 8.8 0.8 3.58 High
74 74 6.7 1.8 2.89 Moderate
75 75 8.8 5.4 3.58 High
76 76 6.6 0.1 2.99 Moderate
77 77 9.5 5.7 3.44 High
78 78 8.2 2 3.35 High
79 79 8.4 2.8 3.26 High
80 80 5.3 5.3 2.62 Low
81 81 6.8 4.6 2.86 High
82 82 5.4 4.9 2.86 High
83 83 8.5 5.9 2.99 High
84 84 9.1 0.9 3.08 High
85 85 6.9 3.4 3.21 Moderate
86 86 5.6 2.8 2.81 High
87 87 5.3 3.9 2.9 High
88 88 9.8 5.2 3.78 High
89 89 6.1 5.8 2.96 High
90 90 5.4 3.1 2.81 Low
91 91 8.1 3.6 3.39 High
92 92 5.2 5.3 2.82 Low
93 93 7.3 1.1 2.67 Moderate
94 94 7.9 5.8 3.08 Moderate
95 95 8.5 4.9 3.1 High
96 96 9.6 1 3.54 High
97 97 8.1 3.9 3.45 High
98 98 9.9 2.1 3.39 High
99 99 9.8 0.7 3.15 High
100 100 5.3 6 2.68 Low
101 101 10 2.8 3.41 High
102 102 7.3 3.6 3.22 Moderate
103 103 6.1 2.1 2.98 Moderate
104 104 7.6 4.9 2.99 Moderate
105 105 7.8 1.9 3.3 Moderate
106 106 8.5 0.1 3.55 High
107 107 6.8 2.6 2.97 Moderate
108 108 7.6 3.7 3.28 Moderate
109 109 9.3 4.8 3.05 High
110 110 7.3 1.7 3.08 Moderate
111 111 8.2 0.8 3.3 High
112 112 5.9 2.8 2.7 Low
113 113 8.3 0.2 3.14 High
114 114 5.9 2.8 2.73 High
115 115 6 4.1 2.76 Moderate
116 116 9.4 1.6 3.45 High
117 117 9 1.3 3.29 High
118 118 8.1 4.5 2.75 High
119 119 5.2 5.4 3.4 Low
120 120 5.5 1.5 3.05 Low
121 121 6.3 1.9 2.82 High
122 122 5.6 4.1 2.62 Low
123 123 8.9 3.2 3.64 High
124 124 7.2 2.2 3.19 Moderate
125 125 8.2 5.9 2.95 High
126 126 5.5 1 2.94 Low
127 127 5.9 5.4 2.89 High
128 128 7.1 2.4 2.9 High
129 129 9.8 0.4 3.57 High
130 130 9.6 1.2 3.24 High
131 131 8.8 2.6 2.99 High
132 132 5.6 3.5 2.85 Low
133 133 5.8 0.3 2.7 Low
134 134 5.3 4.9 2.72 Low
135 135 6.3 3.6 2.45 Moderate
136 136 6.7 4.2 2.85 Moderate
137 137 5.6 1.1 2.86 Low
138 138 7.9 4.8 3.15 High
139 139 6 4.9 2.96 High
140 140 6.8 4.9 3.44 Moderate
141 141 7.2 1.8 3.08 Moderate
142 142 6.2 3.3 3.27 Moderate
143 143 9.5 5.6 3.13 High
144 144 5.7 3.2 3.08 Low
145 145 9.5 1.7 3.59 High
146 146 5.2 5.3 2.61 Low
147 147 6.7 5.1 2.95 High
148 148 9 4.3 3.15 High
149 149 8.6 2.3 3.14 High
150 150 5.9 2.4 2.64 Low
151 151 7.3 3.5 3.58 High
152 152 5.1 3.2 2.79 Low
153 153 8.5 0.5 3.65 High
154 154 7.1 4 3.33 Moderate
155 155 7.3 5 3.11 Moderate
156 156 8 2 3.42 High
157 157 9 2.4 3.51 High
158 158 6.7 2.7 3.02 Moderate
159 159 6.1 1.1 2.38 High
160 160 9.6 3.2 3.43 High
161 161 5.1 5.8 2.71 High
162 162 5.4 3.6 2.59 Low
163 163 5.3 5.4 2.68 Low
164 164 5.2 0.4 2.67 Low
165 165 8.9 2.5 3.58 High
166 166 5.8 5.7 2.46 Low
167 167 6.9 2.5 3.22 Moderate
168 168 5.1 4 3.02 Low
169 169 5.1 1.1 2.52 Low
170 170 5.5 5.1 2.97 Low
171 171 9.2 1 3.5 High
172 172 5.1 0.2 2.7 Low
173 173 5.9 5.4 2.78 Low
174 174 6.3 1.6 3.15 Moderate
175 175 8.1 2.6 3.26 High
176 176 8.8 2.6 3.3 High
177 177 9.5 0.1 3.49 High
178 178 8.2 5 2.89 High
179 179 9.9 1.6 3.52 High
180 180 6.4 5 2.76 Moderate
181 181 6 5.4 2.95 High
182 182 8 5.5 3.4 High
183 183 7.1 0.3 3.17 Moderate
184 184 5.4 2.3 2.66 Low
185 185 6.4 1.8 2.77 Moderate
186 186 8.3 0.7 3.77 High
187 187 5.2 0.3 3.29 Low
188 188 6.7 0.5 2.99 High
189 189 8.6 2.2 3.43 High
190 190 9 3.4 3.24 High
191 191 6.8 0.8 3.01 Moderate
192 192 5.9 4.4 3.01 Low
193 193 9 3.3 3.34 High
194 194 6.1 0.2 2.58 Moderate
195 195 8.5 3.4 3.06 High
196 196 9.6 0.4 3.65 High
197 197 9.3 3.9 3.41 High
198 198 6.2 4.2 2.84 High
199 199 6.7 1.9 3.29 High
200 200 6.6 3.4 3.03 Moderate
201 201 6.2 0.1 2.95 Moderate
202 202 9.7 0.4 3.67 High
203 203 8.8 3.8 3.23 High
204 204 7.1 0.7 3.36 Moderate
205 205 6.8 1.4 3.01 Moderate
206 206 6.9 5.7 3.08 Moderate
207 207 8.9 5.3 3.33 High
208 208 8.1 1.2 3.35 High
209 209 9.7 1.9 3.33 High
210 210 6.1 2.1 2.81 Moderate
211 211 5.3 5 3.08 Low
212 212 8.5 1.3 3.16 High
213 213 6.1 3.5 2.79 Moderate
214 214 9.4 1.2 3.33 High
215 215 8.3 2.5 2.97 High
216 216 8.6 1.3 3.14 High
217 217 8.4 3 3.77 High
218 218 9.1 2.9 3.26 High
219 219 6.3 3.4 3.11 Moderate
220 220 8.5 3.5 3.38 High
221 221 5.8 0.2 2.84 Low
222 222 6.7 4.9 3.03 High
223 223 9.2 1.7 3.42 High
224 224 8.2 3.2 3.24 High
225 225 8.9 3.2 3.32 High
226 226 8.2 5.6 3.29 High
227 227 8.4 4.2 3.29 High
228 228 6.7 5.2 2.86 High
229 229 9.9 0.2 3.8 High
230 230 8.4 6 3.25 High
231 231 7.1 4.8 2.86 High
232 232 5.4 1.6 2.95 Low
233 233 9.8 5.7 3.84 High
234 234 7 2.5 2.84 Moderate
235 235 9.5 0.2 3.17 High
236 236 9.3 0.6 3.68 High
237 237 9.3 2.3 3.52 High
238 238 5.7 3.7 3.12 Low
239 239 9 0.8 3.21 High
240 240 6 3.5 3.26 High
241 241 6.3 2.7 2.85 Moderate
242 242 8 5.2 2.72 Moderate
243 243 8.9 0.6 3.33 High
244 244 8.1 0.1 2.98 High
245 245 6.6 2.1 2.79 High
246 246 7.8 0.9 3.11 Moderate
247 247 5.8 4.2 2.88 Low
248 248 8.5 2.7 3.15 High
249 249 6.7 5.4 3.27 High
250 250 7.3 4.4 3.13 Moderate
251 251 5.4 1 2.75 Low
252 252 9.9 4.7 3.57 High
253 253 8.3 4.2 3.25 High
254 254 6 0.2 2.92 High
255 255 6.3 4.4 2.85 Moderate
256 256 6.7 0.2 3.32 Moderate
257 257 9.3 3.8 3.61 High
258 258 9.5 3.6 3.42 High
259 259 9.6 3.5 3.31 High
260 260 8.6 1.4 3.37 High
261 261 9.9 2 3.59 High
262 262 7.2 3.6 3.01 Moderate
263 263 5.8 1.5 2.61 Low
264 264 8.9 2.2 3.57 High
265 265 6.2 3.1 3.13 High
266 266 7.2 4.2 2.98 Moderate
267 267 5.8 4.4 2.81 High
268 268 9.2 3.2 3.8 High
269 269 6 0.3 3.05 Moderate
270 270 5.2 5.6 2.79 Low
271 271 6.6 0.9 3.17 High
272 272 7.3 4 3.16 Moderate
273 273 5.7 4.2 2.98 Low
274 274 9.8 2.4 3.26 High
275 275 9.3 4.6 3.43 High
276 276 7.5 2.7 3.2 Moderate
277 277 5.2 4.1 2.91 Low
278 278 7 3.9 2.82 High
279 279 10 1.6 3.56 High
280 280 7.8 1.5 3.25 Moderate
281 281 6.6 4.3 2.93 Moderate
282 282 9.1 0 3.64 High
283 283 6.6 4.2 2.79 Moderate
284 284 6.2 0.4 2.5 Moderate
285 285 8 2.4 3.4 Moderate
286 286 7.6 2 3.08 High
287 287 6.9 3.2 3.31 Moderate
288 288 9.1 0 3.48 High
289 289 6 5.4 2.92 Moderate
290 290 5.3 0.6 2.78 Low
291 291 7.4 1.8 3.15 Moderate
292 292 6.6 1.6 3.27 Moderate
293 293 7.1 4.1 3.09 High
294 294 8.5 1.5 3.01 High
295 295 9.8 1.8 3.5 High
296 296 9.8 2.4 3.38 High
297 297 6.1 2.5 2.67 Moderate
298 298 8.9 2.1 3.2 High
299 299 8.3 2.7 3.01 High
300 300 8.7 0.5 3.37 High
301 301 5.1 3.3 2.65 Low
302 302 8 0.6 2.93 Moderate
303 303 8.5 4.3 3.39 High
304 304 6.4 5.4 3.29 High
305 305 6 2.1 2.91 Moderate
306 306 5.3 3.2 2.64 Low
307 307 9.4 1.4 3.66 High
308 308 10 2.6 3.26 High
309 309 7.3 5.7 3.2 Moderate
310 310 6 2.7 2.72 High
311 311 8.8 4.4 3.1 High
312 312 10 2.2 3.44 High
313 313 7.6 4.5 3.17 High
314 314 8.9 1.8 3.3 High
315 315 6.3 5.6 2.58 High
316 316 7 3.1 3.15 Moderate
317 317 9.6 2 3.46 High
318 318 6.9 3.5 3.02 Moderate
319 319 9 2.4 3.46 High
320 320 6.7 4.3 2.83 Moderate
321 321 5.7 2.6 3.13 Low
322 322 5.9 0.9 3 Low
323 323 6.8 0.8 3.03 High
324 324 9.8 3 3.58 High
325 325 7.2 1.3 3.39 Moderate
326 326 5.8 2.8 3.11 Low
327 327 5.3 4.2 2.67 High
328 328 8.5 6 3.21 High
329 329 9.1 3.7 2.85 High
330 330 5.7 1.2 3.04 Low
331 331 6.8 4.4 2.9 Moderate
332 332 5.1 0.8 2.25 Low
333 333 7.7 1.4 3.15 High
334 334 5.5 5.1 2.94 Low
335 335 5.5 1.9 2.79 High
336 336 6.9 4.7 3.38 Moderate
337 337 9.5 0.6 3.3 High
338 338 6.3 5.4 3.31 High
339 339 7.3 5.3 3.36 Moderate
340 340 5.3 4.4 3.11 High
341 341 7.6 0.5 3.07 High
342 342 5.4 4.4 2.85 Low
343 343 6.8 4.6 2.81 Moderate
344 344 5.9 4.6 2.99 Low
345 345 6.3 5.8 3.14 High
346 346 8.1 0.7 3.06 High
347 347 8.1 2.4 3.09 High
348 348 9.9 2.8 3.67 High
349 349 9.1 2 3.16 High
350 350 7.2 1.1 2.69 Moderate
351 351 5.6 3.9 3.01 Low
352 352 9.3 0.4 3.28 High
353 353 8.8 3.1 3.45 High
354 354 5.9 5.6 3.12 Low
355 355 5.2 4.4 2.67 High
356 356 7.5 2.4 3.24 Moderate
357 357 8 3.4 3.35 Moderate
358 358 8.2 2.8 3.31 High
359 359 5 6 2.71 Low
360 360 9 4.4 3.18 High
361 361 8.1 4.7 3.19 High
362 362 8.8 1.2 3.32 High
363 363 9.8 0.9 3.61 High
364 364 10 3.8 3.43 High
365 365 9.4 2.1 3.1 High
366 366 9 2.9 3.6 High
367 367 8.2 3.4 3.64 High
368 368 8.4 5 3.5 High
369 369 7.5 2 3.26 High
370 370 8.9 0.9 3.53 High
371 371 6.6 1.3 3.29 High
372 372 10 0.9 3.63 High
373 373 8.2 2.5 3.16 High
374 374 7.9 2.2 3.4 High
375 375 6.7 5 3.03 High
376 376 6.5 2.6 2.62 Moderate
377 377 9.4 4.3 3.18 High
378 378 5.7 3 2.28 Low
379 379 7.2 0.8 2.88 Moderate
380 380 8.8 0.2 3.38 High
381 381 5.3 1.5 2.75 Low
382 382 6.4 4.7 3.03 Moderate
383 383 5.3 0.4 2.31 High
384 384 6.7 4 3.18 High
385 385 9.8 0.1 3.16 High
386 386 8.7 3.3 3.2 High
387 387 6 5.8 2.58 Moderate
388 388 5.6 1.5 3.07 Low
389 389 9.3 0.6 3.48 High
390 390 5.3 0.5 2.93 Low
391 391 7.3 5.7 3.18 High
392 392 7.3 3.3 3.28 Moderate
393 393 5.3 2.8 2.67 Low
394 394 5.8 3.1 2.71 Low
395 395 6 4.4 2.84 Moderate
396 396 9.9 0.4 3.17 High
397 397 7.3 2.7 3.15 Moderate
398 398 9.1 4.3 3.21 High
399 399 6 4.8 3.27 Moderate
400 400 9 2 3.57 High
401 401 5.8 1.9 2.8 Low
402 402 5.9 0.9 2.8 Low
403 403 7.2 5.3 3.08 High
404 404 9.8 1.7 3.75 High
405 405 8.2 2.4 3.47 High
406 406 8.6 4.4 3.81 High
407 407 5.9 3 2.68 Low
408 408 7.4 2.1 3.34 Moderate
409 409 7.5 1 3.29 Moderate
410 410 5.5 2.1 2.93 High
411 411 7.3 3.4 3.29 Moderate
412 412 7.4 0.6 3.02 Moderate
413 413 8.4 0.4 3.32 High
414 414 6.1 3.9 2.86 Moderate
415 415 8.5 1.3 3.38 High
416 416 9.4 1.7 3.45 High
417 417 6.3 3.2 2.92 Moderate
418 418 7.3 2.1 2.9 Moderate
419 419 9.9 0.3 3.55 High
420 420 6.4 5.5 3 Moderate
421 421 8.8 5.8 3.13 High
422 422 9.1 0.5 3.58 High
423 423 8.5 1.5 3.27 High
424 424 6.5 1.8 2.91 Moderate
425 425 8.7 4.8 3.5 High
426 426 9.5 1.4 3.77 High
427 427 9 6 3.36 High
428 428 5.3 0.7 3.03 Low
429 429 5.5 5 2.86 Low
430 430 7.6 2.6 3.34 Moderate
431 431 8.9 1.4 3.66 High
432 432 8 3.1 3.46 Moderate
433 433 5.5 3.9 2.85 Low
434 434 8.7 0.4 3.42 High
435 435 6.3 0.3 2.86 Moderate
436 436 9.9 1.1 3.35 High
437 437 8.1 3.1 3.15 High
438 438 9 1.5 3.2 High
439 439 9.7 3.7 3.39 High
440 440 7.5 3.9 2.97 Moderate
441 441 10 2.4 3.14 High
442 442 9.5 1.1 3.11 High
443 443 9.1 0 3.27 High
444 444 6.3 4.1 2.97 Moderate
445 445 5.6 2.2 2.8 Low
446 446 8.8 2.1 3.12 High
447 447 9.7 1.2 3.44 High
448 448 8.2 3.5 3.23 High
449 449 8.6 0.4 3.06 High
450 450 5.1 2.9 2.79 Low
451 451 7.9 3.4 3.44 High
452 452 8.3 1.6 3.07 High
453 453 8.4 4.7 3.54 High
454 454 9.1 0.8 3.31 High
455 455 6.6 3.6 3.2 Moderate
456 456 6.9 0.9 3.26 Moderate
457 457 9.5 0.2 3.69 High
458 458 6.7 1.9 3.28 Moderate
459 459 8.4 1.3 3.25 High
460 460 8.4 0.2 3.32 High
461 461 7.5 0.2 3.11 Moderate
462 462 5.8 2.8 2.8 Low
463 463 6.4 5.7 3.1 High
464 464 8 0.8 3.13 Moderate
465 465 9.8 1.9 3.23 High
466 466 7.8 4.3 3.2 Moderate
467 467 7.2 1.2 3.05 Moderate
468 468 6.4 2 2.87 Moderate
469 469 7.9 3.8 2.68 Moderate
470 470 10 1.4 3.44 High
471 471 8.1 0.2 3.74 High
472 472 6.1 3.2 2.61 Moderate
473 473 7.7 4.7 3.21 High
474 474 6.4 0.7 2.89 Moderate
475 475 7.3 4.7 2.74 Moderate
476 476 6.4 2.3 3.21 Moderate
477 477 7.6 5.2 3.13 Moderate
478 478 9.1 3.7 3.42 High
479 479 6 2.6 3.02 Moderate
480 480 8.1 2.9 2.63 High
481 481 8.4 0.6 3.07 High
482 482 9 5.7 3.07 High
483 483 5 5 2.9 Low
484 484 8.7 4.5 3.41 High
485 485 8.3 2.1 3.24 High
486 486 5.4 0.4 2.63 Low
487 487 6.7 5.7 2.63 Moderate
488 488 5.4 4.2 2.85 Low
489 489 5.3 1.8 2.64 Low
490 490 9 3.9 3.16 High
491 491 6.6 3.8 3 Moderate
492 492 6.8 5.3 3.08 High
493 493 7.5 3.7 3.17 Moderate
494 494 6.2 4.7 2.83 Moderate
495 495 7.8 4.7 3.16 High
496 496 6.4 0.3 2.61 Moderate
497 497 8.7 3.1 3.24 High
498 498 9.2 4.4 3.41 High
499 499 6.6 1.6 2.89 Moderate
500 500 8.9 3.9 3.51 High
501 501 6.9 4.1 2.55 High
502 502 8.2 1 3.2 High
503 503 8.6 2.2 3.31 High
504 504 6.5 3.9 3.01 Moderate
505 505 5.2 3 2.49 Low
506 506 6 0.3 2.44 Moderate
507 507 8.8 3.7 3.44 High
508 508 7.4 4.1 2.88 Moderate
509 509 8 3.9 3.03 High
510 510 8.2 1.1 3.46 High
511 511 9.5 0.3 3.43 High
512 512 6.3 2.3 2.74 High
513 513 6.7 2.5 2.49 High
514 514 7.7 2.8 3.12 High
515 515 9.8 1.2 3.61 High
516 516 7.6 0.1 3.28 High
517 517 8.9 1.6 3.36 High
518 518 6.7 1.8 3.31 High
519 519 8.2 0.1 3.26 High
520 520 8.9 1.8 3.38 High
521 521 5.3 2.9 2.76 Low
522 522 5.3 4.1 3.13 Low
523 523 10 0.2 3.25 High
524 524 6 4.7 2.78 Moderate
525 525 7.9 3.8 3.5 Moderate
526 526 6.3 1.7 2.74 Moderate
527 527 7.3 0.3 2.93 Moderate
528 528 5.7 1.5 3.27 Low
529 529 7.5 1.6 3.09 High
530 530 7.7 1 3.43 High
531 531 7.2 5.1 2.99 High
532 532 9.8 3.6 3.59 High
533 533 8.6 1 3.69 High
534 534 5.4 1.1 2.54 Low
535 535 9 1.6 3.44 High
536 536 8.8 4.3 3.32 High
537 537 6.2 1 3.23 Moderate
538 538 7.5 2.1 3.2 High
539 539 9.6 1.2 3.62 High
540 540 8.6 0.2 3.2 High
541 541 7.4 1.7 3.26 High
542 542 10 1.9 3.46 High
543 543 7 4.2 2.68 Moderate
544 544 8.9 2.2 3.54 High
545 545 8.3 5.5 3.25 High
546 546 6.1 1.1 2.72 High
547 547 9.3 2.2 3.79 High
548 548 7.6 2.1 2.83 Moderate
549 549 9.5 0.5 3.4 High
550 550 7.5 1.1 3.43 Moderate
551 551 6.7 0.4 2.48 Moderate
552 552 9.1 3.4 3.4 High
553 553 8.9 5.3 2.98 High
554 554 7.1 4.6 3.13 High
555 555 5 0.3 2.53 Low
556 556 6.2 5.8 2.71 Moderate
557 557 6.1 5.1 2.57 Moderate
558 558 6.6 1 2.85 Moderate
559 559 6.9 4.4 2.92 Moderate
560 560 8 3.3 3.48 High
561 561 8.5 3.4 3.41 High
562 562 6.5 0.6 2.67 Moderate
563 563 7.6 5.6 3.25 Moderate
564 564 8.6 2.8 3.47 High
565 565 8.6 4.5 3.18 High
566 566 6.7 2.9 2.78 Moderate
567 567 8.6 1.7 3.07 High
568 568 8.9 0.1 3.26 High
569 569 9.2 5.8 3.08 High
570 570 8.5 4.4 3.2 High
571 571 5.3 5.2 2.89 Low
572 572 7.8 5.2 3.06 High
573 573 5.8 5.2 3.08 Low
574 574 6.3 5 3.07 Moderate
575 575 9.5 0.3 3.07 High
576 576 6.5 2 3.06 Moderate
577 577 8.7 4.7 3.2 High
578 578 8.4 2.2 2.92 High
579 579 9.7 2.1 3.38 High
580 580 8.8 1.7 3.05 High
581 581 7.2 3.1 3.03 High
582 582 8.7 1.9 3.12 High
583 583 7 2.9 2.87 Moderate
584 584 8.3 0.3 3.04 High
585 585 5.4 0.6 3.14 Low
586 586 8.2 5.8 3.5 High
587 587 6.3 3.7 2.72 Moderate
588 588 8 0.3 3.02 Moderate
589 589 5.8 5 2.95 Low
590 590 8.5 0.1 3.01 High
591 591 7.9 5.7 3.41 Moderate
592 592 5.9 0.6 2.82 Low
593 593 5 4.5 2.72 High
594 594 6.3 0.1 3.11 Moderate
595 595 7.9 1.3 3.08 Moderate
596 596 6 0.1 2.95 Moderate
597 597 6.2 1.5 2.92 Moderate
598 598 8.7 0.4 3.16 High
599 599 9.8 1.5 3.49 High
600 600 6 4.4 3.05 Moderate
601 601 7.6 5.5 3.35 Moderate
602 602 6.3 4 3.42 Moderate
603 603 8.9 1.3 3.21 High
604 604 7.2 0.3 3.42 Moderate
605 605 9.1 0.1 3.37 High
606 606 5.3 0.6 2.41 Low
607 607 8.8 1.4 3.26 High
608 608 5.4 5.3 2.75 Low
609 609 9.1 1.7 3.15 High
610 610 7.6 2.1 2.97 Moderate
611 611 9.1 4.8 3.34 High
612 612 6.8 4.8 3.09 Moderate
613 613 5.5 3 2.9 Low
614 614 8.4 2.2 3.2 High
615 615 7.2 4 3.19 High
616 616 6.1 0 2.73 High
617 617 8.6 0.8 3.15 High
618 618 5.5 3.6 2.57 Low
619 619 6.2 1.4 3.13 Moderate
620 620 7.1 4.2 3.48 Moderate
621 621 8.5 2.5 3.34 High
622 622 7.7 0.7 3.34 Moderate
623 623 6.7 1.2 3.18 Moderate
624 624 6 0.1 2.85 Moderate
625 625 9 3.4 3.64 High
626 626 6.3 3.2 3.14 Moderate
627 627 9.2 1.1 3.6 High
628 628 7.3 5.3 2.97 Moderate
629 629 7.3 4.8 3.09 Moderate
630 630 5.3 3.3 2.81 Low
631 631 9.8 0.4 3.71 High
632 632 7.2 1.6 2.98 Moderate
633 633 6.1 0.4 2.79 Moderate
634 634 8.6 0 2.85 High
635 635 6.1 0 3.03 Moderate
636 636 7.7 3.5 3.29 Moderate
637 637 8.9 1.6 3.42 High
638 638 8.1 1.2 3.04 High
639 639 9.5 1.5 3.76 High
640 640 9.4 0.6 3.62 High
641 641 7.4 1.2 2.89 Moderate
642 642 6.1 3 2.76 Moderate
643 643 5.9 2.3 2.99 Low
644 644 9 1.4 3.56 High
645 645 9.7 1.4 3.57 High
646 646 6.1 4 2.73 Moderate
647 647 9.1 0.6 3.42 High
648 648 6.8 1.6 2.91 High
649 649 5.2 3.3 2.8 Low
650 650 6.6 1.4 2.94 Moderate
651 651 9.5 1.2 3.52 High
652 652 5.3 2 2.49 Low
653 653 8.7 2.8 3.1 High
654 654 5.1 3.4 2.72 Low
655 655 6.6 0.1 3.11 Moderate
656 656 8.8 3.6 3.59 High
657 657 7.8 2.6 3.2 Moderate
658 658 8.8 1.9 3.32 High
659 659 6.1 5.5 2.99 Moderate
660 660 6.1 2.6 2.49 Moderate
661 661 9 0.2 3.74 High
662 662 8.4 0.7 3.16 High
663 663 7.5 3.4 2.98 Moderate
664 664 8.2 4.2 3.13 High
665 665 7.3 0.8 3.41 Moderate
666 666 9.2 1 3.55 High
667 667 7.4 3.4 3.19 High
668 668 7.9 0.3 3.14 Moderate
669 669 5.2 3.6 2.55 High
670 670 9.3 3.8 3.52 High
671 671 6.4 0.1 3.01 Moderate
672 672 9.9 0.2 3.65 High
673 673 6.7 4.9 2.96 Moderate
674 674 5.4 2.8 2.66 Low
675 675 6.3 0.9 2.93 High
676 676 6.4 1.8 3.06 Moderate
677 677 6.2 2.1 2.84 Moderate
678 678 6.1 2.1 2.95 High
679 679 9.3 2.9 3.51 High
680 680 9 3 2.99 High
681 681 5.7 2.9 2.84 Low
682 682 6.5 2.5 2.81 High
683 683 7.3 3.9 3.19 Moderate
684 684 6.6 5.9 3.06 Moderate
685 685 9.4 2.9 3.4 High
686 686 7.4 5.2 3.45 Moderate
687 687 7 4.6 3 Moderate
688 688 9.8 3.3 3.41 High
689 689 7.4 3.3 3.41 Moderate
690 690 5.7 1.7 2.62 High
691 691 6.1 1.7 2.69 Moderate
692 692 6.9 0.5 3.08 Moderate
693 693 7.8 0.7 2.95 Moderate
694 694 6.1 1.6 3.05 Moderate
695 695 5 3.8 2.62 Low
696 696 7.3 2.9 3.19 High
697 697 9.6 1.2 3.5 High
698 698 7.3 0.9 3.06 Moderate
699 699 7.4 6 3 Moderate
700 700 9.1 1.2 3.64 High
701 701 6.7 5.5 3.11 Moderate
702 702 5.3 2.6 3.58 Low
703 703 9.2 2.7 3.27 High
704 704 8 2.3 3.27 High
705 705 9 0.6 3.2 High
706 706 8.4 1.4 3.41 High
707 707 5.3 3.1 2.65 Low
708 708 6.7 4 3.15 Moderate
709 709 9.7 3.5 3.63 High
710 710 9.7 0.6 3.36 High
711 711 6.4 4.3 3.14 High
712 712 10 0.5 3.79 High
713 713 7.2 5 3.21 Moderate
714 714 5.3 3.6 2.76 High
715 715 5.8 3 2.83 Low
716 716 8.3 2.3 2.92 High
717 717 9.5 0.1 3.24 High
718 718 7 3.6 3.4 Moderate
719 719 6.6 1.1 2.88 Moderate
720 720 7.1 4.4 2.85 High
721 721 7.7 1.8 2.88 Moderate
722 722 5.8 5.4 3.27 Low
723 723 6.7 5.6 3.06 Moderate
724 724 8.3 1.7 3.5 High
725 725 5.1 0.6 2.54 Low
726 726 5.6 0.4 3.32 Low
727 727 8 4 3.01 High
728 728 5.9 5.6 2.9 Low
729 729 9.1 4.6 2.98 High
730 730 5.3 2.7 2.62 Low
731 731 6.1 3.5 3.13 Moderate
732 732 9.1 2.2 3.27 High
733 733 9 0.5 3.05 High
734 734 9.2 4.9 2.81 High
735 735 9.1 1.8 3.4 High
736 736 7.7 2.4 3.24 Moderate
737 737 5.8 3.2 2.8 Low
738 738 7.1 5.6 3.1 Moderate
739 739 8.3 3.3 3.48 High
740 740 9.1 1.4 3.3 High
741 741 5.5 4.1 2.71 Low
742 742 7 5 3.09 High
743 743 5.2 5.3 2.48 High
744 744 7.5 0.8 3.19 Moderate
745 745 9.4 0.8 3.16 High
746 746 8.8 2 3.66 High
747 747 8.7 5 3.55 High
748 748 7.2 1.4 3.11 Moderate
749 749 9.2 1.6 3.02 High
750 750 5.2 5.9 2.93 High
751 751 9.8 1.4 3.68 High
752 752 9.7 1 3.57 High
753 753 10 3.9 3.35 High
754 754 9.7 1.2 3.53 High
755 755 6.5 1 2.91 Moderate
756 756 8.8 2.4 3.08 High
757 757 5.5 3.6 2.73 Low
758 758 5.1 0.8 2.64 Low
759 759 6.2 2 2.55 Moderate
760 760 5.1 4.7 3.03 High
761 761 6.5 2.4 2.98 Moderate
762 762 6.4 5.4 3.05 Moderate
763 763 6.9 2.6 3.12 High
764 764 6.1 5.6 3.01 Moderate
765 765 5.5 5.2 2.24 Low
766 766 6.7 1 2.93 Moderate
767 767 7.9 0.6 3.09 Moderate
768 768 6.8 0.3 2.88 Moderate
769 769 5.2 1.2 2.52 Low
770 770 9.4 1 3.57 High
771 771 7.1 5 3.26 Moderate
772 772 7 2.2 3.15 Moderate
773 773 8.4 6 3.44 High
774 774 9.3 3.7 3.76 High
775 775 7.1 3.4 2.99 Moderate
776 776 9.7 1.3 3.54 High
777 777 8.9 0.2 3.14 High
778 778 8.8 0.5 3.18 High
779 779 5.6 5.1 3.08 Low
780 780 8 3 2.96 Moderate
781 781 7.1 1.4 2.85 Moderate
782 782 8.1 0.4 3.37 High
783 783 5.7 0.3 2.69 Low
784 784 7.4 2.8 3.36 Moderate
785 785 5.1 0.2 2.55 Low
786 786 7.7 4 3.54 Moderate
787 787 9.8 2.6 3.47 High
788 788 5.3 2 3.37 Low
789 789 9.6 4.3 3.49 High
790 790 8.5 2 2.9 High
791 791 8.4 0 3.08 High
792 792 8.3 0.1 3.1 High
793 793 8.6 3.2 3.11 High
794 794 5.5 3.6 2.75 Low
795 795 5.5 4.7 2.64 Low
796 796 6.3 5.1 3.22 Moderate
797 797 9.8 0.4 3.04 High
798 798 8 0.6 3.38 Moderate
799 799 6.6 5.9 3.27 High
800 800 9.4 2.2 2.91 High
801 801 6.6 4 2.76 Moderate
802 802 5.7 3.5 3.08 High
803 803 8.5 3.4 3.63 High
804 804 8.1 1.7 3.21 High
805 805 7.2 1 3.07 Moderate
806 806 7.9 0.3 2.99 High
807 807 5.5 1.4 3.05 Low
808 808 5.2 1.5 2.88 Low
809 809 6.5 4.5 3.29 Moderate
810 810 6.8 1.1 3.13 Moderate
811 811 8.5 5.5 3.07 High
812 812 8.4 2.4 3.51 High
813 813 7.1 0.7 3.15 Moderate
814 814 6.9 3 3.05 Moderate
815 815 5.8 2.5 2.64 Low
816 816 5.6 2.3 2.5 Low
817 817 8.3 0.4 3.33 High
818 818 5.8 3.5 2.9 Low
819 819 7 2.4 3.15 High
820 820 6 3.6 2.75 Moderate
821 821 9.4 4.5 3.25 High
822 822 7.8 1.7 3.19 Moderate
823 823 5.7 3.6 2.96 Low
824 824 9.1 3.8 3.66 High
825 825 9.1 5.1 3.45 High
826 826 7.9 1.9 2.68 Moderate
827 827 9 5.9 3.22 High
828 828 7.2 0.3 3 Moderate
829 829 8 1.2 3.08 Moderate
830 830 9.1 1.3 3.35 High
831 831 9 2.3 3.44 High
832 832 7.1 3.3 3.33 Moderate
833 833 6.1 1.1 2.94 Moderate
834 834 8.8 4 3.2 High
835 835 5.4 1.1 2.71 High
836 836 5.3 2.9 2.54 Low
837 837 6.9 3 3.37 High
838 838 9.4 0.1 3.79 High
839 839 5.5 3.2 2.74 High
840 840 9.7 0.3 3.48 High
841 841 5.8 3.1 2.73 High
842 842 9.9 0.2 3.23 High
843 843 6.9 0 3.27 High
844 844 7.1 0.1 3.34 Moderate
845 845 6.7 5.7 3.31 High
846 846 6.6 0.6 3.12 Moderate
847 847 5.9 3.3 3.05 Low
848 848 5.4 4.7 2.8 Low
849 849 10 3 3.4 High
850 850 7 4.8 2.78 High
851 851 6.7 3.5 3.09 Moderate
852 852 9.3 0.8 3.49 High
853 853 7.1 3.1 2.87 Moderate
854 854 5.7 5.3 3.18 Low
855 855 9.5 1.6 3.39 High
856 856 7.1 3.5 3.48 Moderate
857 857 5.8 3.4 3.01 Low
858 858 5.1 3.4 2.6 Low
859 859 6.3 4.8 3.08 Moderate
860 860 7.4 2.5 3.4 Moderate
861 861 8 0 2.83 Moderate
862 862 8.7 5.6 3.3 High
863 863 5.9 3.8 2.45 High
864 864 7.6 0.1 3.38 Moderate
865 865 9.3 1.7 3.34 High
866 866 8.7 0.7 3.57 High
867 867 6.3 1.8 3.37 Moderate
868 868 7.1 0.5 3.33 High
869 869 5.9 1.7 3.13 Low
870 870 5.1 4.8 2.69 Low
871 871 9.7 5.3 3.92 High
872 872 8.3 3.3 2.95 High
873 873 8.6 0.2 3.36 High
874 874 6.3 5.2 2.9 High
875 875 7.5 4.5 2.97 Moderate
876 876 7.1 3.9 3.28 Moderate
877 877 9.7 3.2 3.26 High
878 878 8.4 1 3.2 High
879 879 7.5 3.9 2.96 Moderate
880 880 7 4.7 3.2 Moderate
881 881 9.5 3.8 3.36 High
882 882 9.8 0.4 3.54 High
883 883 7.4 1.9 3.16 Moderate
884 884 6.5 3.7 3.02 Moderate
885 885 5.7 4 2.86 Low
886 886 8.2 1.9 2.81 High
887 887 9 5.6 3.59 High
888 888 8.8 0.7 3.07 High
889 889 9.3 4.2 3.17 High
890 890 9.4 4.3 3.57 High
891 891 7.4 1 3.15 Moderate
892 892 5.9 3.6 3.13 Low
893 893 7.3 3.1 3.34 Moderate
894 894 6.8 0.6 3.23 Moderate
895 895 7 5.8 3.26 High
896 896 6.4 1.8 3.25 Moderate
897 897 8.9 0.1 3.42 High
898 898 6.8 3.1 3.03 Moderate
899 899 7.2 2.9 3.04 Moderate
900 900 8.3 2.5 3.22 High
901 901 8.9 3.4 3.1 High
902 902 6.3 1.9 2.48 Moderate
903 903 8 4.4 3.11 Moderate
904 904 6 0 2.87 Moderate
905 905 9.6 0.4 3.5 High
906 906 7.2 2.1 3.11 Moderate
907 907 6.9 0.7 3.29 Moderate
908 908 6.7 3.1 2.92 Moderate
909 909 9.3 4.7 3.45 High
910 910 7.4 1 3.11 Moderate
911 911 7.7 0.3 2.96 Moderate
912 912 5 4.9 3.01 Low
913 913 6.7 1.8 2.85 High
914 914 5.3 1.7 2.64 Low
915 915 7.1 5.4 2.65 Moderate
916 916 5.5 1.8 2.81 High
917 917 6.5 4.7 3.04 High
918 918 5.6 1.1 2.66 Low
919 919 9.1 5.7 3.48 High
920 920 5.8 3.1 2.79 High
921 921 8.8 1.6 3.29 High
922 922 6.4 2.4 3 Moderate
923 923 7.7 1 3.16 Moderate
924 924 9 0.6 2.96 High
925 925 7.7 2.5 3.08 Moderate
926 926 5.9 0.8 2.86 Low
927 927 8.8 1.9 3.19 High
928 928 8.1 5.6 3.45 High
929 929 9.6 2.3 3.4 High
930 930 6.6 0.9 2.88 Moderate
931 931 8 0.8 3.08 Moderate
932 932 6.5 4.2 2.83 Moderate
933 933 6.8 2.8 2.92 Moderate
934 934 5.5 4.1 2.92 Low
935 935 5.1 0.3 2.49 Low
936 936 6.9 1.8 3.15 High
937 937 5.4 2.9 2.74 High
938 938 9.6 0.8 3.29 High
939 939 8.7 2 3.03 High
940 940 5.8 2.2 2.9 Low
941 941 8.8 3 3.51 High
942 942 5.2 1.4 2.83 Low
943 943 8.7 1.5 3.29 High
944 944 7.4 3.2 3.22 Moderate
945 945 8.8 2.3 3.24 High
946 946 9 2.3 3.55 High
947 947 9 0.2 3.68 High
948 948 7.3 3.8 2.91 Moderate
949 949 5.9 1.2 3.09 Low
950 950 7.4 4.2 2.94 Moderate
951 951 9.8 4.6 3.84 High
952 952 6.3 1.2 2.99 High
953 953 5 1.5 2.94 Low
954 954 7.8 3.6 3.26 Moderate
955 955 8.7 1.3 3.55 High
956 956 5.3 2 2.53 High
957 957 9.1 2.6 3.28 High
958 958 9.3 4.6 3.58 High
959 959 5.9 4.2 3.32 High
960 960 6 1.9 3.01 High
961 961 7.8 2 3.18 Moderate
962 962 6.3 0.7 3.1 Moderate
963 963 6.5 3.6 2.9 Moderate
964 964 8.1 3.1 2.99 High
965 965 8.8 4 3.59 High
966 966 7.4 3.1 3.44 Moderate
967 967 6 0.5 3.28 High
968 968 7.2 2.1 2.93 Moderate
969 969 9.7 1 3.04 High
970 970 9.6 4.4 3.38 High
971 971 7.4 3.1 2.84 Moderate
972 972 7.4 0.6 3.23 High
973 973 9.2 3 3.18 High
974 974 5.2 0.7 2.82 Low
975 975 5.2 1.6 2.68 Low
976 976 7.2 2 3.06 Moderate
977 977 6.6 0.6 3.31 Moderate
978 978 6.4 2.9 3.12 Moderate
979 979 8.8 5.5 3.32 High
980 980 9.6 1.1 3.3 High
981 981 6.7 3.1 2.98 Moderate
982 982 8 5.6 3.28 Moderate
983 983 7.9 4.6 3.38 Moderate
984 984 8.2 0.5 3.18 High
985 985 6.5 4.6 2.74 Moderate
986 986 5.9 1.4 3.1 Low
987 987 8.1 2.8 3.14 High
988 988 6 3.4 2.84 Moderate
989 989 5.1 4.9 2.94 Low
990 990 6.9 5.6 3.05 High
991 991 8.8 1.1 3.3 High
992 992 6.7 5.3 3.05 Moderate
993 993 6 0.4 2.6 Moderate
994 994 6.5 3.9 2.82 Moderate
995 995 9.9 1.6 3.44 High
996 996 9.7 4.5 3.5 High
997 997 7 4.5 3 Moderate
998 998 5.8 0.3 2.49 Low
999 999 7.9 3.6 3.01 High
1000 1000 8.5 0 3.63 High
1001 1001 8.5 2.2 3.26 High
1002 1002 7.5 2.5 3.19 Moderate
1003 1003 9 2 3.43 High
1004 1004 8.5 2.8 3.26 High
1005 1005 5.3 5.3 2.9 Low
1006 1006 7.8 3.1 3.07 Moderate
1007 1007 6.5 1.8 2.92 Moderate
1008 1008 6.5 1.4 2.93 Moderate
1009 1009 5.8 2.1 2.87 Low
1010 1010 5.9 4.3 2.95 Low
1011 1011 9.9 4.3 3.32 High
1012 1012 5.2 0.8 2.66 Low
1013 1013 7.3 0.2 3.17 Moderate
1014 1014 7.2 2.3 3.13 Moderate
1015 1015 9.4 2.6 2.81 High
1016 1016 6.4 3.5 3.07 High
1017 1017 6.4 6 2.98 Moderate
1018 1018 5.4 3.1 2.68 Low
1019 1019 6.3 1.8 3.1 High
1020 1020 9.5 5.1 3.05 High
1021 1021 5.5 3.2 3.2 Low
1022 1022 8.7 2.6 3.21 High
1023 1023 7.7 5.9 3.15 High
1024 1024 8 4.2 3.44 Moderate
1025 1025 7.6 1.6 3.02 Moderate
1026 1026 6.9 4.7 3.14 Moderate
1027 1027 7.4 1.9 2.99 Moderate
1028 1028 6.1 0.1 3.05 Moderate
1029 1029 8.2 5.8 3.45 High
1030 1030 5.7 3.5 2.9 Low
1031 1031 8.6 2.7 3.49 High
1032 1032 6.2 1 2.87 Moderate
1033 1033 6.7 1.7 3.06 Moderate
1034 1034 8.4 0.3 2.86 High
1035 1035 5.2 1.9 2.38 Low
1036 1036 6 2.5 2.88 Moderate
1037 1037 7.2 1.1 3.31 Moderate
1038 1038 8 0.4 3.39 Moderate
1039 1039 8 5 2.9 Moderate
1040 1040 5.7 1.2 2.81 High
1041 1041 6.7 0.8 2.81 Moderate
1042 1042 6.1 1.3 2.76 Moderate
1043 1043 6.8 5.4 2.89 Moderate
1044 1044 5.3 3 2.94 Low
1045 1045 8.9 0.9 3.11 High
1046 1046 5.3 2.5 3.02 High
1047 1047 6.6 1.8 2.87 Moderate
1048 1048 7.2 5.3 3.26 Moderate
1049 1049 6.1 5.5 3.01 Moderate
1050 1050 7.8 2 3.04 Moderate
1051 1051 7.9 3 3.05 Moderate
1052 1052 6.2 1.1 2.94 Moderate
1053 1053 7.5 2.8 3.21 Moderate
1054 1054 5.5 4.6 2.97 Low
1055 1055 5.6 3.5 2.72 Low
1056 1056 7.6 0.5 3.56 Moderate
1057 1057 7.2 0.8 3.28 Moderate
1058 1058 5.8 5.3 3 High
1059 1059 7.6 3.9 3.17 High
1060 1060 8.1 0.3 3.25 High
1061 1061 6.3 2.3 2.97 Moderate
1062 1062 9.2 4.4 3.5 High
1063 1063 6.4 6 2.78 Moderate
1064 1064 9.7 4.1 3.26 High
1065 1065 9.2 0.1 3.48 High
1066 1066 8.2 5.9 3.44 High
1067 1067 9.6 2.2 3.55 High
1068 1068 7.3 2.8 3.27 Moderate
1069 1069 9.6 2.7 3.37 High
1070 1070 6.8 4.3 2.58 Moderate
1071 1071 6.3 3.9 2.97 High
1072 1072 9.3 1.8 3.3 High
1073 1073 5.4 1.5 3.04 Low
1074 1074 9.4 1.1 3.36 High
1075 1075 5.5 4.4 2.44 Low
1076 1076 7.7 3.5 3 Moderate
1077 1077 5.9 0.3 3.11 High
1078 1078 5.1 4.9 2.82 Low
1079 1079 6.6 1.6 3.23 Moderate
1080 1080 8.7 2.1 3.67 High
1081 1081 5.6 5.2 2.86 Low
1082 1082 5.9 3.5 2.96 Low
1083 1083 8.1 3.6 3.12 High
1084 1084 8.2 0.2 3.38 High
1085 1085 7.9 1.1 3.18 Moderate
1086 1086 9.6 4.6 3.12 High
1087 1087 7.5 3.9 3.01 High
1088 1088 8.4 1.4 3.17 High
1089 1089 7.2 1.5 2.89 Moderate
1090 1090 7.8 1.5 2.9 Moderate
1091 1091 9.5 5.3 3.56 High
1092 1092 8.4 1.8 3.25 High
1093 1093 5.5 2.5 2.74 High
1094 1094 5.7 0.6 2.92 Low
1095 1095 5.8 1.9 2.97 Low
1096 1096 8.4 1.3 3.68 High
1097 1097 5.2 3.4 2.88 Low
1098 1098 8.4 1.2 3.08 High
1099 1099 6.2 4.7 2.89 High
1100 1100 8.2 1.9 3.38 High
1101 1101 8.8 0.3 3.39 High
1102 1102 6.3 5.5 3.06 Moderate
1103 1103 8.2 4.3 3.21 High
1104 1104 8.1 1.4 3.15 High
1105 1105 8.3 0.6 3.4 High
1106 1106 10 4 3.39 High
1107 1107 9.8 2.4 3.35 High
1108 1108 6.4 0.5 2.9 Moderate
1109 1109 6.7 2.2 2.89 Moderate
1110 1110 5.4 3.5 2.98 High
1111 1111 9.6 1.4 3.64 High
1112 1112 5.9 0.4 3.07 Low
1113 1113 6.8 2.4 2.96 Moderate
1114 1114 9 1.3 3.61 High
1115 1115 8 0.9 3.13 Moderate
1116 1116 6.1 3 3.01 Moderate
1117 1117 5.8 4.4 3.16 Low
1118 1118 5.7 3.5 2.75 High
1119 1119 7.3 2.5 3 High
1120 1120 8.7 3.1 3.65 High
1121 1121 6.1 5 2.61 Moderate
1122 1122 9.3 2.3 3.29 High
1123 1123 6 6 2.5 Moderate
1124 1124 7.8 1.6 2.6 Moderate
1125 1125 8.6 1.1 2.97 High
1126 1126 9.6 1.8 3.39 High
1127 1127 8.2 2.3 3.28 High
1128 1128 7.2 1 2.92 Moderate
1129 1129 7 2 2.88 Moderate
1130 1130 9.9 2.9 3.49 High
1131 1131 5.5 3.9 2.99 High
1132 1132 8.9 3.8 3.14 High
1133 1133 5.4 5.9 3.1 Low
1134 1134 8.4 1.7 3.12 High
1135 1135 7.1 5.3 3.05 High
1136 1136 6.7 5 3.05 Moderate
1137 1137 5.7 3.7 2.88 Low
1138 1138 6.5 4.9 2.88 Moderate
1139 1139 7 4.6 3.24 Moderate
1140 1140 9.5 3.7 3.42 High
1141 1141 9 4.4 3.37 High
1142 1142 5.2 3.5 2.77 Low
1143 1143 8.2 0.3 3.37 High
1144 1144 8.3 3.3 3.27 High
1145 1145 7.3 4.1 3.41 High
1146 1146 7.4 1 3.11 Moderate
1147 1147 9.4 5.8 3.25 High
1148 1148 8.1 4.8 3.22 High
1149 1149 9.9 3.6 3.51 High
1150 1150 7.7 5.1 2.94 Moderate
1151 1151 9.3 1.6 3.29 High
1152 1152 8.1 1.3 3.27 High
1153 1153 8.2 5.2 3.03 High
1154 1154 7.1 4.6 3.11 Moderate
1155 1155 5.5 5.6 2.95 Low
1156 1156 7.7 2.7 3.16 Moderate
1157 1157 7.4 4.5 2.77 Moderate
1158 1158 7.5 4.8 2.93 Moderate
1159 1159 7.4 1.4 3.09 High
1160 1160 5.8 0.2 2.92 Low
1161 1161 6.6 2.1 3.09 Moderate
1162 1162 6.9 2.7 3.02 Moderate
1163 1163 8.8 0.1 3.61 High
1164 1164 6.7 4.5 2.92 Moderate
1165 1165 5.3 5.2 3.06 Low
1166 1166 6.6 1.1 2.82 High
1167 1167 7.5 5.7 3.11 Moderate
1168 1168 9.6 4.1 3.49 High
1169 1169 8.3 3.6 3.36 High
1170 1170 7.5 1.7 3.25 High
1171 1171 6.9 0.5 3.17 Moderate
1172 1172 8.2 1.9 3.2 High
1173 1173 5.5 1 2.3 Low
1174 1174 8.1 2.3 3.72 High
1175 1175 5.3 0.2 2.97 High
1176 1176 5.7 1.8 3.04 High
1177 1177 6.2 2.9 2.97 Moderate
1178 1178 5.3 3.8 2.79 High
1179 1179 5.3 3.2 2.55 Low
1180 1180 6 1.3 2.95 Moderate
1181 1181 5.6 4.8 2.7 High
1182 1182 5.7 3.9 3.05 Low
1183 1183 8.9 0.5 3.46 High
1184 1184 6 2.4 2.81 Moderate
1185 1185 9.6 5.7 3.28 High
1186 1186 7 0.7 2.81 Moderate
1187 1187 7 4.8 2.97 Moderate
1188 1188 6.9 4.2 2.83 Moderate
1189 1189 6 3.8 2.8 High
1190 1190 7.9 0.8 3.45 Moderate
1191 1191 6.9 3.6 3.37 Moderate
1192 1192 8.8 4.8 3.38 High
1193 1193 8.6 1 3.51 High
1194 1194 8.1 0.9 2.78 High
1195 1195 5.6 5.8 2.84 High
1196 1196 9.9 3.8 3.73 High
1197 1197 6.8 3.3 3.37 High
1198 1198 6.3 1.2 3.15 Moderate
1199 1199 9.1 2.7 3.43 High
1200 1200 9.3 1.4 3.12 High
1201 1201 5.7 1.9 2.72 Low
1202 1202 8.6 2.3 3.24 High
1203 1203 7.4 2.7 2.82 Moderate
1204 1204 7.6 3.1 2.87 Moderate
1205 1205 8.4 5.6 3.37 High
1206 1206 8.2 3.1 3.56 High
1207 1207 7.7 1.2 3.13 High
1208 1208 7.3 4.3 3.21 High
1209 1209 7.7 4.5 3.07 Moderate
1210 1210 7.3 3.9 3 Moderate
1211 1211 8.3 0.4 3.56 High
1212 1212 5 3.4 2.9 High
1213 1213 9.6 1.3 3.47 High
1214 1214 5.3 2.8 2.87 Low
1215 1215 7.3 1 3.35 Moderate
1216 1216 8.3 4.2 3.25 High
1217 1217 6 5 3.04 Moderate
1218 1218 5.1 3.8 3.24 Low
1219 1219 7.8 3.4 3.14 Moderate
1220 1220 8.8 0.7 3.16 High
1221 1221 7.5 5.1 3.27 Moderate
1222 1222 6 5.3 2.97 High
1223 1223 6.6 5.6 2.96 Moderate
1224 1224 9.3 2.5 3.31 High
1225 1225 6.9 2.3 3.03 Moderate
1226 1226 5.3 0.9 2.85 Low
1227 1227 9.7 2.9 3.5 High
1228 1228 9.4 2 3.43 High
1229 1229 7.8 3.2 3.36 High
1230 1230 9.8 3.3 3.93 High
1231 1231 9.2 1.2 3.1 High
1232 1232 5.2 4.1 3.12 Low
1233 1233 9.7 0.2 3.3 High
1234 1234 9.7 2.4 3.47 High
1235 1235 8.2 3.1 3.02 High
1236 1236 7.6 2.9 3.15 Moderate
1237 1237 8.1 2.9 3.19 High
1238 1238 7.1 1.8 3.17 Moderate
1239 1239 7.1 0.8 3.01 Moderate
1240 1240 6.1 0.5 2.9 Moderate
1241 1241 7.4 4.8 3.14 High
1242 1242 9.3 4.7 3.41 High
1243 1243 9.2 1 3.48 High
1244 1244 9.9 1.3 3.63 High
1245 1245 9 6 3.56 High
1246 1246 7.8 0 3.09 Moderate
1247 1247 5.4 5.3 3.27 Low
1248 1248 5.9 1.4 2.71 Low
1249 1249 7.5 3 3.09 Moderate
1250 1250 6.7 0.1 2.78 Moderate
1251 1251 8.8 0.3 3.17 High
1252 1252 5.9 2.1 2.66 High
1253 1253 8.5 2.5 3.25 High
1254 1254 5.4 4.2 2.81 High
1255 1255 9.6 3.4 3.37 High
1256 1256 8.3 0.3 3.29 High
1257 1257 6 1.8 3.11 Moderate
1258 1258 8.7 1.5 3.24 High
1259 1259 9.6 2.6 3.67 High
1260 1260 7.6 3.2 3.04 High
1261 1261 9.7 2.9 3.51 High
1262 1262 6 4.4 2.8 Moderate
1263 1263 5.6 3.2 2.75 Low
1264 1264 9 3.1 3.43 High
1265 1265 7.1 3.1 2.79 Moderate
1266 1266 9.5 2.7 3.67 High
1267 1267 8.8 4 3.2 High
1268 1268 9.5 2.1 3.11 High
1269 1269 5.4 1.1 2.72 Low
1270 1270 6.3 1.8 2.65 High
1271 1271 7.9 4.5 3.24 Moderate
1272 1272 6.7 0.8 3.09 High
1273 1273 6.7 0.4 2.74 Moderate
1274 1274 7.8 1.4 3.31 Moderate
1275 1275 9.3 0.1 3.1 High
1276 1276 7.7 1.3 2.9 Moderate
1277 1277 8.4 3.4 3.02 High
1278 1278 9 4.4 3.62 High
1279 1279 6.9 4.9 3.05 Moderate
1280 1280 8.3 1.4 3.4 High
1281 1281 8.5 1.2 3.25 High
1282 1282 6.2 0.5 2.76 Moderate
1283 1283 9.1 0.5 3.23 High
1284 1284 9.8 0.8 3.42 High
1285 1285 9.7 3.4 3.67 High
1286 1286 7.4 0.3 3.15 Moderate
1287 1287 10 3.3 3.39 High
1288 1288 5 3.4 2.7 Low
1289 1289 10 1 3.4 High
1290 1290 9 0.3 3.19 High
1291 1291 9.4 5.2 3.2 High
1292 1292 7.1 2.7 3.15 Moderate
1293 1293 9.9 2.9 3.09 High
1294 1294 8.6 1.8 3.3 High
1295 1295 9.3 1 3.07 High
1296 1296 7.3 1.8 2.91 Moderate
1297 1297 5.5 0.7 2.79 High
1298 1298 7.2 2.1 2.78 Moderate
1299 1299 6.7 0.3 2.79 Moderate
1300 1300 6.2 3.4 2.92 Moderate
1301 1301 7.4 4.4 3.23 Moderate
1302 1302 6 2.4 2.79 Moderate
1303 1303 9.7 5.5 3.73 High
1304 1304 5.8 0.8 2.57 Low
1305 1305 5.6 1.2 2.57 Low
1306 1306 8 5.6 2.76 Moderate
1307 1307 9.5 2 3.14 High
1308 1308 9.5 5.5 3.59 High
1309 1309 9.8 1.8 3.23 High
1310 1310 7.9 0.4 3.11 Moderate
1311 1311 8.1 2.3 3.11 High
1312 1312 8.8 1.1 3.22 High
1313 1313 6.6 5.4 2.88 Moderate
1314 1314 5.7 5.4 2.62 Low
1315 1315 9.7 4.8 3.3 High
1316 1316 6.1 3.9 2.56 High
1317 1317 6.3 4.8 2.93 Moderate
1318 1318 7.1 1.3 3.3 Moderate
1319 1319 6.5 5.5 2.71 Moderate
1320 1320 9.7 3.4 3.81 High
1321 1321 8.1 3.9 3.41 High
1322 1322 6.6 0.8 3.27 Moderate
1323 1323 9.7 4.4 3.06 High
1324 1324 9.1 0.1 3.36 High
1325 1325 7.8 1.4 3.14 High
1326 1326 7.2 2.2 3.41 Moderate
1327 1327 7.7 4.9 2.68 Moderate
1328 1328 9.8 0.6 3.62 High
1329 1329 9.2 2.4 3.47 High
1330 1330 5.3 2.6 2.54 Low
1331 1331 8.7 0.1 3.34 High
1332 1332 9.7 0.5 3.18 High
1333 1333 7.1 0.2 3.02 High
1334 1334 9.6 3.5 3.41 High
1335 1335 6.7 1.1 3.11 High
1336 1336 6.8 0 3.15 Moderate
1337 1337 6.2 4.8 2.76 Moderate
1338 1338 7.5 0.5 3 Moderate
1339 1339 6.8 4.9 2.99 Moderate
1340 1340 5.2 0.9 3.28 Low
1341 1341 8.3 3.5 3.49 High
1342 1342 6.3 4 2.91 Moderate
1343 1343 8.9 0.8 3.3 High
1344 1344 8.6 2.9 3.42 High
1345 1345 9.4 1.4 3.2 High
1346 1346 9 4.1 3.34 High
1347 1347 9.7 3 3.51 High
1348 1348 5.2 1.1 2.58 High
1349 1349 6.8 0.4 2.75 Moderate
1350 1350 7.9 4.3 3.19 Moderate
1351 1351 6.7 2.9 3.24 Moderate
1352 1352 6.5 5.5 3.28 High
1353 1353 8.4 3.6 3.34 High
1354 1354 5 3.7 2.69 Low
1355 1355 6.3 1.5 2.9 High
1356 1356 8.6 0.9 3.48 High
1357 1357 7.6 0.3 3.27 Moderate
1358 1358 9.5 5.3 3.78 High
1359 1359 7.1 1.7 3.14 High
1360 1360 6.4 1 2.93 Moderate
1361 1361 7.4 2.7 2.95 Moderate
1362 1362 9.7 1.3 3.53 High
1363 1363 9.7 0.6 3.87 High
1364 1364 8.8 4.5 3.12 High
1365 1365 6.4 2.8 3.11 Moderate
1366 1366 8.6 4 2.94 High
1367 1367 8.6 2.7 3.11 High
1368 1368 7.4 4.5 2.95 Moderate
1369 1369 5.5 4.6 2.83 Low
1370 1370 6.2 4.6 2.63 Moderate
1371 1371 6.1 2.3 3.41 Moderate
1372 1372 7.8 1.1 3.18 Moderate
1373 1373 9.1 4.6 3.17 High
1374 1374 7.6 1.1 3.07 Moderate
1375 1375 6.3 4.8 2.69 Moderate
1376 1376 6.3 3.7 2.91 High
1377 1377 7.8 3.3 3.09 Moderate
1378 1378 9.5 0.1 3.28 High
1379 1379 9.7 2.7 3.47 High
1380 1380 6.7 5.2 2.88 Moderate
1381 1381 8.3 4.4 3.26 High
1382 1382 6.4 5.2 2.75 Moderate
1383 1383 5.8 5.2 2.96 Low
1384 1384 6.6 0.3 2.94 High
1385 1385 8.6 1.1 3.2 High
1386 1386 7.4 5.8 2.87 Moderate
1387 1387 8.2 5.3 3.12 High
1388 1388 8.8 0.3 3.26 High
1389 1389 5.7 3 2.5 Low
1390 1390 9.7 0.2 3.53 High
1391 1391 5.5 5.9 2.82 High
1392 1392 6.8 2.6 2.77 High
1393 1393 6.4 5.5 2.86 High
1394 1394 8.9 2.7 3.51 High
1395 1395 6.5 5.9 3 High
1396 1396 7.2 3.2 3.02 Moderate
1397 1397 7.4 1.2 3.25 High
1398 1398 5.9 5.2 2.72 Low
1399 1399 9.7 1.3 3.55 High
1400 1400 9.7 2.1 3.87 High
1401 1401 7.6 2.8 3.2 Moderate
1402 1402 6.6 3.3 3.02 Moderate
1403 1403 5.8 5 2.93 Low
1404 1404 8.5 5.1 3.23 High
1405 1405 7.4 3.5 3.33 Moderate
1406 1406 5.5 0.5 3.11 High
1407 1407 7.6 0.5 2.89 Moderate
1408 1408 9.9 1.1 3.43 High
1409 1409 5.5 2.5 2.8 Low
1410 1410 9.1 0.4 3.37 High
1411 1411 5.8 1.5 2.91 Low
1412 1412 7.9 1.3 3.1 Moderate
1413 1413 8.3 3.5 2.95 High
1414 1414 6.3 3.3 2.52 Moderate
1415 1415 6.5 4.3 3.1 High
1416 1416 7 2.2 2.85 Moderate
1417 1417 5.5 4 2.76 Low
1418 1418 6 5.4 2.54 High
1419 1419 6.1 0.8 2.79 Moderate
1420 1420 5.7 4.2 2.9 Low
1421 1421 7.8 0.7 2.69 Moderate
1422 1422 6.3 5.9 3.22 Moderate
1423 1423 8 1.5 2.75 Moderate
1424 1424 9.6 2.3 3.37 High
1425 1425 9.2 1.4 3.39 High
1426 1426 7 0.1 3.27 Moderate
1427 1427 9.4 0.8 3.37 High
1428 1428 7 5.8 2.47 Moderate
1429 1429 8 2.4 3.36 High
1430 1430 9.3 5.1 3.02 High
1431 1431 6.1 5 3.04 Moderate
1432 1432 7.1 5.6 3.08 Moderate
1433 1433 9.2 0.6 3.62 High
1434 1434 6.4 0.3 3.04 Moderate
1435 1435 6.8 4.4 3.27 Moderate
1436 1436 7.9 1.5 2.87 Moderate
1437 1437 5.4 3 2.91 Low
1438 1438 8.9 2.4 3.5 High
1439 1439 6 5.9 2.76 Moderate
1440 1440 9.1 1 3.53 High
1441 1441 8.3 2.5 3.25 High
1442 1442 7.6 3 2.86 Moderate
1443 1443 5.6 5.4 2.98 Low
1444 1444 5.7 1 2.42 High
1445 1445 7.9 2.5 2.99 Moderate
1446 1446 5 4.1 3.07 Low
1447 1447 7.1 0.4 3.15 Moderate
1448 1448 8.6 5.9 3.21 High
1449 1449 6 1.7 2.7 Moderate
1450 1450 7.9 4 3.29 Moderate
1451 1451 5.9 1.3 2.7 Low
1452 1452 9.5 2.7 3.32 High
1453 1453 6.6 2 3.03 Moderate
1454 1454 5.8 3.9 3.09 Low
1455 1455 9.4 2.5 3.91 High
1456 1456 6.6 0.2 2.93 Moderate
1457 1457 6.6 0.1 2.6 High
1458 1458 7.2 4.1 2.96 High
1459 1459 7.6 3.6 3.3 Moderate
1460 1460 8.6 3.4 3.45 High
1461 1461 5.2 3 2.51 Low
1462 1462 8.2 1.2 3.06 High
1463 1463 8.8 1.9 3.85 High
1464 1464 8.7 0.3 3.42 High
1465 1465 9.7 2 3.49 High
1466 1466 8.7 5 3.25 High
1467 1467 5.4 3.6 2.85 Low
1468 1468 7.1 1.7 3.16 Moderate
1469 1469 10 1 3.81 High
1470 1470 7.7 2.2 3.22 High
1471 1471 6.2 5.7 2.98 High
1472 1472 7.8 4.5 3.16 High
1473 1473 9.5 2.5 3.44 High
1474 1474 7.6 4.9 3.29 High
1475 1475 5.4 1.3 2.69 Low
1476 1476 8 1.8 3.25 Moderate
1477 1477 7.3 4.4 3.3 High
1478 1478 5.8 2.6 2.62 High
1479 1479 6.2 5.3 2.91 Moderate
1480 1480 8.1 4.3 3.24 High
1481 1481 8.3 2.3 3.31 High
1482 1482 7.5 1.9 3.02 Moderate
1483 1483 6.7 4.8 3.01 Moderate
1484 1484 7.3 0.1 3.29 Moderate
1485 1485 9.7 5.2 3.47 High
1486 1486 7.7 3.9 3.08 Moderate
1487 1487 8.6 0.9 3.14 High
1488 1488 5.2 1.9 2.61 Low
1489 1489 5.6 5.1 2.81 Low
1490 1490 6.1 5.6 2.73 Moderate
1491 1491 9.5 2.1 3.74 High
1492 1492 7.3 1.2 2.97 Moderate
1493 1493 7.8 2.5 3.24 Moderate
1494 1494 5.8 4.2 2.82 Low
1495 1495 6.9 2.5 3.02 Moderate
1496 1496 9.1 1.6 3.22 High
1497 1497 5.6 4.2 2.71 Low
1498 1498 7.5 1.4 3.32 Moderate
1499 1499 5.7 1.2 2.74 High
1500 1500 6.8 0.2 2.8 Moderate
1501 1501 9.7 1.6 3.22 High
1502 1502 8.2 2.7 3.55 High
1503 1503 7.7 4.9 3.06 High
1504 1504 6.6 3 2.73 High
1505 1505 9 4.8 3.33 High
1506 1506 6.3 1.3 3.32 Moderate
1507 1507 6.3 6 3 High
1508 1508 7 2.3 2.98 Moderate
1509 1509 7.7 1.5 3 High
1510 1510 5.6 4.3 2.91 Low
1511 1511 5.9 1.6 2.76 Low
1512 1512 9.1 1.3 3.46 High
1513 1513 6.3 3.5 2.87 Moderate
1514 1514 6.8 0.8 2.58 Moderate
1515 1515 6.9 4.4 3 Moderate
1516 1516 9.8 0.3 3.53 High
1517 1517 9.5 3.4 3.35 High
1518 1518 5.6 0.2 2.99 Low
1519 1519 6.4 5.4 2.72 Moderate
1520 1520 9.6 1.9 3.45 High
1521 1521 6 1.3 3.19 Moderate
1522 1522 8.9 3.6 3.27 High
1523 1523 8.3 1.9 3.18 High
1524 1524 6 0.6 2.78 Moderate
1525 1525 8.6 5.5 2.9 High
1526 1526 8.8 0.2 3.52 High
1527 1527 9.2 1.1 3.6 High
1528 1528 7.4 5.4 3.25 Moderate
1529 1529 7.5 1.7 3.03 Moderate
1530 1530 9.1 4.8 3.08 High
1531 1531 6.8 0.7 3 Moderate
1532 1532 6.4 5.8 2.98 Moderate
1533 1533 9.9 4.2 3.3 High
1534 1534 8.5 0.8 3.25 High
1535 1535 5.6 4.2 2.75 Low
1536 1536 6.7 3.3 3.26 Moderate
1537 1537 8.2 0.9 2.81 High
1538 1538 7.2 2.5 3.03 Moderate
1539 1539 8.7 0.8 3.58 High
1540 1540 6.8 0.9 3.38 Moderate
1541 1541 6.1 4 2.88 Moderate
1542 1542 7.9 4.1 3.4 Moderate
1543 1543 5.4 3.5 2.71 Low
1544 1544 9 3 3.32 High
1545 1545 8.7 1.4 2.99 High
1546 1546 8.7 3.2 3.17 High
1547 1547 6.4 3.9 2.89 High
1548 1548 8.9 1 3.44 High
1549 1549 8.4 5.8 2.9 High
1550 1550 5.6 2.2 2.91 Low
1551 1551 9.8 5.5 3.24 High
1552 1552 7.7 5.7 2.99 High
1553 1553 9 2.2 3.62 High
1554 1554 9.8 2.9 3.38 High
1555 1555 6.9 4.1 2.77 High
1556 1556 9.1 1.3 3.31 High
1557 1557 8.2 4.4 3.08 High
1558 1558 6.2 2.2 2.84 Moderate
1559 1559 9.4 1.2 3.11 High
1560 1560 8.4 0.4 3.06 High
1561 1561 6 2.3 2.66 Moderate
1562 1562 5.6 0.9 3 Low
1563 1563 5.9 3.3 2.91 High
1564 1564 7.8 4.4 2.69 Moderate
1565 1565 9.4 5.4 3.55 High
1566 1566 6.8 3.2 2.61 Moderate
1567 1567 6 3.5 2.92 Moderate
1568 1568 8.5 0.6 3.09 High
1569 1569 8.6 2.5 3.54 High
1570 1570 6.1 4.7 2.67 Moderate
1571 1571 9.6 3.4 3.65 High
1572 1572 6.3 4.5 2.89 Moderate
1573 1573 5.4 0.1 2.57 Low
1574 1574 8.1 0.6 3.5 High
1575 1575 5.3 4.5 2.25 Low
1576 1576 7 5.9 3.12 Moderate
1577 1577 9.4 4.6 3.34 High
1578 1578 5.4 4.2 2.8 Low
1579 1579 5.4 5 3.12 Low
1580 1580 5.1 1.6 3.02 Low
1581 1581 8.5 1.2 3.17 High
1582 1582 7.1 1.6 2.86 Moderate
1583 1583 5.1 1.8 2.84 Low
1584 1584 9.3 4.2 3.51 High
1585 1585 6 5 2.88 High
1586 1586 8.7 1.6 3.53 High
1587 1587 8.9 1.8 3.63 High
1588 1588 9.1 2.8 3.33 High
1589 1589 9.8 0.1 3.9 High
1590 1590 9.2 2.9 3.29 High
1591 1591 6 3.2 2.84 Moderate
1592 1592 6.2 4.6 2.98 Moderate
1593 1593 7.2 0.6 3.04 Moderate
1594 1594 7 2.3 2.81 Moderate
1595 1595 5.9 2.5 2.63 Low
1596 1596 8.3 2.9 3.26 High
1597 1597 9.9 0.4 3.33 High
1598 1598 9.8 1.7 3.51 High
1599 1599 7.9 4.8 3.34 Moderate
1600 1600 7.9 1.3 3.18 Moderate
1601 1601 5 2.3 2.82 Low
1602 1602 6.8 0.3 3.01 High
1603 1603 8.9 0.6 3.02 High
1604 1604 6.1 1.9 3.01 High
1605 1605 5.6 0.8 2.92 Low
1606 1606 5.3 1.4 2.53 Low
1607 1607 7.3 2.5 2.92 Moderate
1608 1608 8.3 4.1 3.11 High
1609 1609 7.8 4.7 3.02 High
1610 1610 8.5 0.6 3.34 High
1611 1611 8.2 1.5 3.4 High
1612 1612 10 3 3.37 High
1613 1613 6.5 1.7 2.72 Moderate
1614 1614 9.4 3.9 3.37 High
1615 1615 8.3 1.5 3.43 High
1616 1616 5.5 1.1 2.98 Low
1617 1617 9.1 5.6 3.37 High
1618 1618 5.9 1.9 2.91 Low
1619 1619 6.4 4.4 2.91 High
1620 1620 5.9 4.1 2.98 Low
1621 1621 6.6 5.5 3.23 Moderate
1622 1622 7.3 2.6 3.07 High
1623 1623 7.7 4.7 3.37 Moderate
1624 1624 9.5 4.7 3.71 High
1625 1625 7.7 5.3 3.36 High
1626 1626 7.9 2.9 3.17 Moderate
1627 1627 7 2.2 2.89 Moderate
1628 1628 9.6 2.4 3.4 High
1629 1629 7 3.6 3.17 High
1630 1630 5.9 5.3 2.99 Low
1631 1631 7.4 4.3 2.77 Moderate
1632 1632 7 1.6 2.97 Moderate
1633 1633 9.3 1 3.29 High
1634 1634 6.3 1.2 2.91 Moderate
1635 1635 7.3 4.1 3.51 Moderate
1636 1636 9.2 5.4 3.41 High
1637 1637 8.7 1.9 3.73 High
1638 1638 7.1 0.7 3.12 Moderate
1639 1639 7.3 1.5 3.27 Moderate
1640 1640 7.1 0.4 3.13 Moderate
1641 1641 6.2 0.4 3.17 Moderate
1642 1642 5.1 5.2 2.77 Low
1643 1643 6.4 1.3 2.94 Moderate
1644 1644 6.4 2.5 2.87 Moderate
1645 1645 8 1.9 3.01 Moderate
1646 1646 7.7 0.5 2.97 Moderate
1647 1647 8.6 2.5 3.38 High
1648 1648 6.9 1.2 2.75 Moderate
1649 1649 5.8 2.5 2.87 Low
1650 1650 7.8 1 3.06 Moderate
1651 1651 6.3 0.7 2.65 Moderate
1652 1652 5.3 4.3 2.79 High
1653 1653 7.7 4.8 3.23 High
1654 1654 6.7 0.5 2.77 Moderate
1655 1655 6.1 3.9 3.4 Moderate
1656 1656 9.8 2.5 3.56 High
1657 1657 9.6 1.1 3.45 High
1658 1658 8.7 4.3 2.96 High
1659 1659 9 2.3 3.74 High
1660 1660 9.4 4.5 3.46 High
1661 1661 6.1 4.3 2.65 Moderate
1662 1662 6.8 4.5 2.93 High
1663 1663 6.3 5.9 2.8 Moderate
1664 1664 5.4 2.5 2.77 Low
1665 1665 7.2 3 2.83 Moderate
1666 1666 6.3 5.1 3.03 Moderate
1667 1667 6.2 3 3.12 Moderate
1668 1668 5.8 0.8 2.88 Low
1669 1669 7 1.6 2.63 Moderate
1670 1670 9.7 0.9 3.72 High
1671 1671 8.9 5.1 3.55 High
1672 1672 7.8 4.4 2.91 Moderate
1673 1673 5.4 0.3 2.9 Low
1674 1674 7.2 3 2.94 High
1675 1675 5.6 1.5 2.89 Low
1676 1676 8.4 3.9 3.26 High
1677 1677 5.2 2.4 2.69 Low
1678 1678 8.3 1.8 3.44 High
1679 1679 7.9 6 3.11 High
1680 1680 8.3 1.6 3.39 High
1681 1681 6.7 3.1 3.54 Moderate
1682 1682 9.6 5.1 3.46 High
1683 1683 6.2 4.4 2.97 High
1684 1684 7.2 2.8 3.19 Moderate
1685 1685 5.1 0.7 3.15 Low
1686 1686 7.5 2.4 3.36 Moderate
1687 1687 5.1 3.4 2.5 Low
1688 1688 9.6 0 3.7 High
1689 1689 9.6 2 3.19 High
1690 1690 7.9 1.9 3.07 High
1691 1691 9.1 2.4 3.24 High
1692 1692 6.4 2.2 2.9 Moderate
1693 1693 5 3.5 2.89 Low
1694 1694 8.9 2.6 3.29 High
1695 1695 8.2 5.4 3.4 High
1696 1696 9.4 2.9 3.56 High
1697 1697 7.6 3.4 3.03 Moderate
1698 1698 8.1 3.6 3.22 High
1699 1699 8 5.9 2.93 High
1700 1700 5.9 3.9 2.78 Low
1701 1701 5.4 1.9 2.35 Low
1702 1702 9.3 3.9 3.24 High
1703 1703 8.9 3 3.49 High
1704 1704 5 1.9 2.64 Low
1705 1705 8.2 3.8 3.26 High
1706 1706 7.3 4 2.75 Moderate
1707 1707 8 0.3 3.25 Moderate
1708 1708 6.4 3.3 2.77 Moderate
1709 1709 6.6 2 3.14 Moderate
1710 1710 8.2 4.2 3.48 High
1711 1711 6.9 2.5 2.88 Moderate
1712 1712 6.7 3.1 2.87 Moderate
1713 1713 5.8 5.6 2.88 High
1714 1714 8.6 1.9 3.46 High
1715 1715 6.5 3.5 2.8 Moderate
1716 1716 5.4 0.4 2.53 Low
1717 1717 5.5 4 2.95 High
1718 1718 7.4 2.3 3.03 Moderate
1719 1719 7.8 2.4 3.43 Moderate
1720 1720 6.8 4.3 2.83 Moderate
1721 1721 5.5 1.3 2.74 Low
1722 1722 9.9 0.2 3.32 High
1723 1723 7 2.5 3.09 Moderate
1724 1724 5.2 5.7 3.04 Low
1725 1725 9.3 1.2 3.29 High
1726 1726 7.5 3 3.4 Moderate
1727 1727 5.2 4.5 2.9 Low
1728 1728 6.8 4 2.49 Moderate
1729 1729 8.6 2.8 3.52 High
1730 1730 6.6 5.9 2.83 Moderate
1731 1731 6.1 0.4 3.29 Moderate
1732 1732 7.6 0.5 3.12 Moderate
1733 1733 9.7 3.9 3.69 High
1734 1734 5.7 4.9 2.66 High
1735 1735 6.1 2.8 2.5 Moderate
1736 1736 6.6 5.9 3.41 Moderate
1737 1737 9.7 0.7 3.6 High
1738 1738 7 3.6 3.11 Moderate
1739 1739 8.5 4.1 2.93 High
1740 1740 8.9 1.5 3.37 High
1741 1741 7.4 1.2 3.11 Moderate
1742 1742 8.7 2.4 3.42 High
1743 1743 7 3 3.05 Moderate
1744 1744 5.8 3.6 2.65 Low
1745 1745 8.3 2.9 3.12 High
1746 1746 9.8 0.3 3.73 High
1747 1747 6.4 5.1 2.79 Moderate
1748 1748 6.2 0 2.8 Moderate
1749 1749 7.1 4.1 3.28 High
1750 1750 8.6 4.6 3.48 High
1751 1751 9.5 3.8 3.57 High
1752 1752 9.7 2.1 3.82 High
1753 1753 9.7 4.1 3.53 High
1754 1754 9.5 2.2 3.7 High
1755 1755 5.1 5.5 2.8 Low
1756 1756 7.7 5.3 2.99 Moderate
1757 1757 7 4.4 2.84 Moderate
1758 1758 9.6 1.4 3.43 High
1759 1759 5.1 4.1 3.01 Low
1760 1760 8.5 4.8 3.06 High
1761 1761 7.4 1.8 3.13 Moderate
1762 1762 6.2 2 2.85 Moderate
1763 1763 6.2 0.8 3.02 Moderate
1764 1764 8.4 0.2 2.65 High
1765 1765 6.5 1.7 3.02 High
1766 1766 8.3 1.9 2.86 High
1767 1767 6.5 0.5 3.1 High
1768 1768 8.6 4.2 3.27 High
1769 1769 8.1 1.2 3.8 High
1770 1770 7.7 3.9 3.3 Moderate
1771 1771 9.4 0.2 3.4 High
1772 1772 9.2 0.6 3.03 High
1773 1773 6.3 2.1 2.78 High
1774 1774 8.5 0.6 3.02 High
1775 1775 9 3.8 3.41 High
1776 1776 8.1 5.7 3.17 High
1777 1777 8.8 4.9 3.53 High
1778 1778 7.2 1.3 3.23 Moderate
1779 1779 8.2 2.4 3.32 High
1780 1780 9.2 6 3.77 High
1781 1781 8.8 2.9 3.26 High
1782 1782 9.4 4.7 3.25 High
1783 1783 9.2 3.3 3.64 High
1784 1784 7.1 3 3.08 Moderate
1785 1785 7.7 2.4 3.03 Moderate
1786 1786 5.3 1.5 2.91 High
1787 1787 8.5 1.3 2.9 High
1788 1788 7.7 0.9 3.39 Moderate
1789 1789 7.3 4.8 3.25 Moderate
1790 1790 6.9 2.8 2.85 High
1791 1791 7.4 1.8 2.75 Moderate
1792 1792 5.8 3.2 2.98 Low
1793 1793 6 4 2.81 High
1794 1794 7.3 1.6 3.15 Moderate
1795 1795 7.6 2.6 2.78 Moderate
1796 1796 9.7 4.1 3.76 High
1797 1797 7.2 0.8 2.87 Moderate
1798 1798 6.5 1.4 3.18 Moderate
1799 1799 5 0.6 2.63 High
1800 1800 7.4 4.3 2.84 Moderate
1801 1801 6.6 3.8 2.93 Moderate
1802 1802 9.3 5.2 3.32 High
1803 1803 8.3 0.1 3.23 High
1804 1804 9.1 1.4 3.71 High
1805 1805 7.6 4.1 3.09 Moderate
1806 1806 9.6 1.1 3.5 High
1807 1807 6.4 1.7 3.21 High
1808 1808 9.5 1.1 3.52 High
1809 1809 5.5 0.3 2.61 Low
1810 1810 8.2 2.6 3.13 High
1811 1811 9.8 4.2 3.62 High
1812 1812 6.9 3.7 3.05 High
1813 1813 6.4 3.2 3 Moderate
1814 1814 8.1 4.3 3.12 High
1815 1815 7.6 3.7 3.18 Moderate
1816 1816 5.4 0.6 2.9 Low
1817 1817 9.9 4.6 3.69 High
1818 1818 7.1 4.6 2.98 Moderate
1819 1819 5.2 2.1 2.69 High
1820 1820 6.7 0.2 2.58 High
1821 1821 7.4 5.7 3.27 High
1822 1822 8.2 3 3.5 High
1823 1823 8.5 0.3 3.34 High
1824 1824 6.9 5.3 3.42 Moderate
1825 1825 7.5 0.6 2.82 Moderate
1826 1826 8.6 1.3 3.06 High
1827 1827 6.4 0.8 2.89 High
1828 1828 6.8 0.8 2.78 Moderate
1829 1829 8.7 2.3 2.87 High
1830 1830 6.2 4.6 2.92 High
1831 1831 5.9 4.6 2.88 Low
1832 1832 6 1.5 3.17 Moderate
1833 1833 9.5 3.6 3.5 High
1834 1834 8.6 0.4 3.32 High
1835 1835 6.9 2 3.23 Moderate
1836 1836 5.2 4.2 2.83 High
1837 1837 5.5 1.2 2.75 Low
1838 1838 7.4 0.8 3.1 Moderate
1839 1839 6.9 2.9 2.77 Moderate
1840 1840 6.4 1.3 3.04 Moderate
1841 1841 7.6 0.9 3.36 Moderate
1842 1842 7.8 0.2 3.27 Moderate
1843 1843 6.5 1.3 3.17 High
1844 1844 7.3 1 3.24 Moderate
1845 1845 7 0.3 2.64 Moderate
1846 1846 7.5 4.9 3.14 Moderate
1847 1847 7.8 1.8 3.39 Moderate
1848 1848 9.8 4.6 3.39 High
1849 1849 7.1 4.5 3.34 Moderate
1850 1850 8.9 4.5 3.36 High
1851 1851 6.7 2.7 3.15 High
1852 1852 5.9 1.4 2.48 Low
1853 1853 7.4 1.6 3.22 Moderate
1854 1854 8.9 3.5 3.17 High
1855 1855 5.7 1.1 2.81 Low
1856 1856 5.7 4.7 2.82 Low
1857 1857 8.7 0.1 3.51 High
1858 1858 5 4.1 2.47 Low
1859 1859 6.3 1.8 2.79 Moderate
1860 1860 7.5 1.6 3.07 Moderate
1861 1861 6.2 3.5 2.88 Moderate
1862 1862 8.2 0.5 3.3 High
1863 1863 8.8 2.5 3.29 High
1864 1864 7.7 3.3 3.44 Moderate
1865 1865 6.8 5.6 3.33 Moderate
1866 1866 5.4 0.1 2.86 Low
1867 1867 6.3 1.6 3.49 High
1868 1868 9.6 4.4 3.54 High
1869 1869 8.9 4.1 3.62 High
1870 1870 8.7 1.5 3.36 High
1871 1871 5.6 4.7 2.85 Low
1872 1872 8 2.1 3.23 Moderate
1873 1873 8.2 0.9 3.52 High
1874 1874 7.4 3.7 3.1 Moderate
1875 1875 7 6 2.99 High
1876 1876 8.6 2.9 3.38 High
1877 1877 6.5 2.4 2.9 High
1878 1878 8 5.6 3.4 Moderate
1879 1879 5.3 2.9 2.82 Low
1880 1880 6.7 3.1 2.85 Moderate
1881 1881 9.7 0 3.19 High
1882 1882 9.6 3.1 3.38 High
1883 1883 6.3 0.5 2.91 Moderate
1884 1884 8.6 3.4 3.18 High
1885 1885 6.8 4 3.04 High
1886 1886 5.9 0.5 2.92 Low
1887 1887 9.1 0.3 3.56 High
1888 1888 9.2 2.2 3.61 High
1889 1889 5.7 2.3 2.98 Low
1890 1890 5.5 5.6 2.77 Low
1891 1891 5.6 2.6 2.97 High
1892 1892 8 0.5 3.12 Moderate
1893 1893 7.2 3.2 2.95 Moderate
1894 1894 7.4 3.4 3.04 Moderate
1895 1895 9.7 4 3.25 High
1896 1896 6.7 2.6 2.83 Moderate
1897 1897 7.6 1.5 3.05 Moderate
1898 1898 8.7 0.9 3.1 High
1899 1899 7.9 3.3 3.75 Moderate
1900 1900 8.6 0.8 3.22 High
1901 1901 5.3 0.3 2.53 Low
1902 1902 7.4 2.2 3.06 Moderate
1903 1903 6.8 5.5 3.05 Moderate
1904 1904 6.6 4.9 3.2 High
1905 1905 7.3 4.9 2.83 Moderate
1906 1906 6.9 3.6 2.94 High
1907 1907 7.4 3.9 3.26 High
1908 1908 9.4 0.7 3.57 High
1909 1909 7.5 1.4 3.07 High
1910 1910 5.9 0 2.79 Low
1911 1911 8.7 4.6 3.57 High
1912 1912 5 5.1 2.98 Low
1913 1913 8.3 1.4 3.06 High
1914 1914 9.6 3.3 3.47 High
1915 1915 8.8 0 3.29 High
1916 1916 9.5 2.5 3.7 High
1917 1917 7.2 1.9 2.79 Moderate
1918 1918 5.7 4.5 2.94 Low
1919 1919 7.5 2.6 2.84 Moderate
1920 1920 9.9 2.7 3.68 High
1921 1921 5.3 3.3 2.79 Low
1922 1922 5.2 2.5 3.21 Low
1923 1923 8.8 1 3.53 High
1924 1924 9.1 4 3.29 High
1925 1925 6.2 3.7 2.73 Moderate
1926 1926 6.3 1 3.09 Moderate
1927 1927 6.3 3 2.99 Moderate
1928 1928 5.5 1.5 2.66 High
1929 1929 9.5 4.9 3.67 High
1930 1930 8 0.8 3.2 Moderate
1931 1931 8.1 0.1 3.28 High
1932 1932 7.8 2.8 3 High
1933 1933 9.6 3.7 3.46 High
1934 1934 9.3 0.9 3.69 High
1935 1935 6.4 2.5 2.81 Moderate
1936 1936 9.5 2.1 3.28 High
1937 1937 7.4 0.7 2.75 Moderate
1938 1938 7.5 0.8 2.93 Moderate
1939 1939 7.6 0.4 3.06 High
1940 1940 8.1 2.2 3.52 High
1941 1941 6.9 3 2.99 Moderate
1942 1942 8.2 5.3 3.19 High
1943 1943 6.1 4.3 2.79 Moderate
1944 1944 7.6 1.9 3 Moderate
1945 1945 5.8 1 2.89 Low
1946 1946 9.9 3.4 3.43 High
1947 1947 9.5 1 3.25 High
1948 1948 8.4 0.8 3.28 High
1949 1949 5.1 4.7 2.64 Low
1950 1950 7.6 2.7 2.98 Moderate
1951 1951 9 1.3 3.24 High
1952 1952 7.7 1.9 3.31 Moderate
1953 1953 6.3 4.7 2.73 High
1954 1954 5.2 0.1 2.95 Low
1955 1955 7.9 0.4 3.42 Moderate
1956 1956 8.3 0.1 3.48 High
1957 1957 7.4 2.7 2.88 Moderate
1958 1958 9.9 2 3.09 High
1959 1959 8.1 2.5 2.86 High
1960 1960 9.6 0.6 3.19 High
1961 1961 7.1 0.9 3.02 Moderate
1962 1962 6.6 5.9 2.99 Moderate
1963 1963 9.4 5.1 3.59 High
1964 1964 9.5 1.3 3.24 High
1965 1965 9.9 0.2 3.54 High
1966 1966 7.4 1.1 3.48 Moderate
1967 1967 7.8 0.2 3.04 Moderate
1968 1968 7.1 5.2 2.92 Moderate
1969 1969 8.6 4.2 3.38 High
1970 1970 6.3 5.2 2.84 Moderate
1971 1971 5.6 4.2 2.81 High
1972 1972 7.4 2.6 3.29 Moderate
1973 1973 7.9 4.9 3.03 High
1974 1974 8.9 3.8 3.31 High
1975 1975 9.4 1 3.32 High
1976 1976 8 5.4 3.42 Moderate
1977 1977 7.5 2.3 3.14 Moderate
1978 1978 9.1 2.1 3.1 High
1979 1979 6.3 0.9 2.97 Moderate
1980 1980 8.2 4.5 3.43 High
1981 1981 7.5 3 3.02 Moderate
1982 1982 5.8 0.9 2.71 Low
1983 1983 6.3 0.9 3.07 Moderate
1984 1984 6.2 5 3.06 Moderate
1985 1985 8.5 3.4 3.23 High
1986 1986 8 4.9 2.93 Moderate
1987 1987 6.6 5 2.9 High
1988 1988 9.5 3 3.86 High
1989 1989 7.3 4.6 3.28 Moderate
1990 1990 7 6 2.85 Moderate
1991 1991 8.6 2.2 3.26 High
1992 1992 6.3 3.1 3.21 High
1993 1993 7.5 4.1 3.04 Moderate
1994 1994 7.1 3.1 2.85 Moderate
1995 1995 7.9 0.5 3.08 Moderate
1996 1996 6.5 2.1 3.32 Moderate
1997 1997 6.3 1.5 2.65 Moderate
1998 1998 6.2 0.8 3.14 Moderate
1999 1999 8.1 3.5 3.04 High
2000 2000 9 3.1 3.58 HighSetelah diketahui variabel Extracurricular_Hours_Per_Day dan Sleep_Hours_Per_Day tidak dapat dianalisis lebih lanjut, maka dilakukan input data kembali dengan membuang variabel tersebut.
3.5.4 Scree Plot
Berdasarkan scree plot di atas, terdapat 1 faktor yang memiliki nilai
eigen lebih dari 1 sehingga banyak faktor bermakna yang akan diekstrak
sebanyak 1 faktor.
3.5.5 Ekstraksi Faktor dengan PCA
Study_Hours_Per_Day Social_Hours_Per_Day GPA
0.85606082 0.07211438 0.83889113 Berdasarkan output di atas, dapat dilihat komunalitas setiap variabel memiliki nilai < 1. Hal tersebut mengindikasikan hilangnya informasi sehingga kurang representatif. Oleh karena itu, PCA kurang tepat digunakan sebagai metode ekstraksi faktor pada kasus ini.
3.5.6 Ekstraksi Faktor dengan PFA
Factor Analysis using method = pa
Call: fa(r = Data6_baru[2:4], nfactors = 1, rotate = "varimax", fm = "pa")
Standardized loadings (pattern matrix) based upon correlation matrix
PA1 h2 u2 com
Study_Hours_Per_Day 0.88 0.773 0.23 1
Social_Hours_Per_Day -0.13 0.017 0.98 1
GPA 0.83 0.697 0.30 1
PA1
SS loadings 1.49
Proportion Var 0.50
Mean item complexity = 1
Test of the hypothesis that 1 factor is sufficient.
df null model = 3 with the objective function = 0.8 with Chi Square = 1587.81
df of the model are 0 and the objective function was 0
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is NA
The harmonic n.obs is 2000 with the empirical chi square 4.29 with prob < NA
The total n.obs was 2000 with Likelihood Chi Square = 8.21 with prob < NA
Tucker Lewis Index of factoring reliability = -Inf
Fit based upon off diagonal values = 1
Measures of factor score adequacy
PA1
Correlation of (regression) scores with factors 0.92
Multiple R square of scores with factors 0.85
Minimum correlation of possible factor scores 0.70
PA1
[1,] -0.4068110875
[2,] -1.3971343893
[3,] -1.5584384643
[4,] -0.7088764341
[5,] 0.7990033978
[6,] -0.9383999191
[7,] 0.1329113946
[8,] 0.4861117979
[9,] -1.3393263218
[10,] -0.4165102321
[11,] 1.3326025580
[12,] -0.4274243145
[13,] -0.8622840477
[14,] -1.3553305155
[15,] 0.9857861402
[16,] -0.1910098477
[17,] 0.8007036701
[18,] 0.6438376066
[19,] 0.1314483114
[20,] -0.5977495160
[21,] -0.4271142582
[22,] 1.1674595532
[23,] 0.7462534543
[24,] -0.9461975951
[25,] -1.5860817075
[26,] 1.1273132589
[27,] -0.9097771144
[28,] -0.6658256352
[29,] -1.3145875832
[30,] 1.1550997930
[31,] -0.6170152197
[32,] -0.5845684469
[33,] -1.7822065891
[34,] 0.4671796250
[35,] 1.6115608274
[36,] 0.6766865210
[37,] -1.5421946379
[38,] 0.3440030029
[39,] 0.5054311507
[40,] -1.7108622599
[41,] 0.8464428155
[42,] 0.2334300302
[43,] -1.2072526459
[44,] 1.3441217911
[45,] -0.0449742939
[46,] 1.5192462659
[47,] -1.6999439211
[48,] 0.6955952194
[49,] 1.4092464566
[50,] 0.8705764033
[51,] -0.2891309749
[52,] 1.8275824817
[53,] 1.1329765337
[54,] -0.0870604931
[55,] -1.3628675279
[56,] 0.5348450897
[57,] -1.8881982596
[58,] 1.0144132013
[59,] -1.7922350081
[60,] -1.1273213191
[61,] 1.0870321866
[62,] 0.0422821436
[63,] -1.3011007143
[64,] 0.1664852762
[65,] -1.0810090102
[66,] -0.3331890662
[67,] 0.5193478922
[68,] 1.2559821339
[69,] -0.0163195019
[70,] -1.3988883106
[71,] 1.2246709827
[72,] 0.8607659551
[73,] 1.1918924918
[74,] -0.6152914730
[75,] 1.1466368681
[76,] -0.5014762084
[77,] 1.2340515582
[78,] 0.6213808165
[79,] 0.5705792907
[80,] -1.5867060764
[81,] -0.6436789014
[82,] -1.2123619754
[83,] 0.2092409806
[84,] 0.6246380701
[85,] -0.1102145193
[86,] -1.1796328773
[87,] -1.1879332931
[88,] 1.8276980417
[89,] -0.8008507648
[90,] -1.2634031339
[91,] 0.6202302332
[92,] -1.3521159546
[93,] -0.6684478145
[94,] 0.0915181753
[95,] 0.3703288974
[96,] 1.4582001629
[97,] 0.6997786364
[98,] 1.3623567279
[99,] 1.0057213492
[100,] -1.5110929448
[101,] 1.4233793563
[102,] 0.0632054268
[103,] -0.7369495499
[104,] -0.1446054534
[105,] 0.3919771478
[106,] 1.0363010843
[107,] -0.4727528055
[108,] 0.2659496702
[109,] 0.6258380461
[110,] -0.1106016992
[111,] 0.5644367506
[112,] -1.2096544107
[113,] 0.3907494408
[114,] -1.1684044822
[115,] -1.0995347851
[116,] 1.2477286671
[117,] 0.8690428964
[118,] -0.2686226025
[119,] -0.5556011550
[120,] -0.8772532180
[121,] -0.8741627298
[122,] -1.4536720572
[123,] 1.2911901205
[124,] -0.0046804524
[125,] 0.0330128716
[126,] -1.0235838663
[127,] -0.9739841292
[128,] -0.4458067984
[129,] 1.5861718018
[130,] 1.0437332422
[131,] 0.3629351758
[132,] -1.1315196980
[133,] -1.2254683645
[134,] -1.4452710431
[135,] -1.3996367523
[136,] -0.6939030074
[137,] -1.0941580920
[138,] 0.1976061875
[139,] -0.8324058051
[140,] 0.1508682624
[141,] -0.1519949186
[142,] -0.3095966546
[143,] 0.8087861151
[144,] -0.7719093910
[145,] 1.4796539170
[146,] -1.6408654540
[147,] -0.5652576069
[148,] 0.6470286929
[149,] 0.4913174691
[150,] -1.2882189961
[151,] 0.5591883866
[152,] -1.4131151085
[153,] 1.1698655743
[154,] 0.1297010901
[155,] -0.1018177617
[156,] 0.6368118467
[157,] 1.1607203751
[158,] -0.4453961440
[159,] -1.5521099407
[160,] 1.2853064312
[161,] -1.5486941833
[162,] -1.5708216990
[163,] -1.5051900373
[164,] -1.5101585196
[165,] 1.2155769889
[166,] -1.6085939594
[167,] -0.0876101820
[168,] -1.1047361999
[169,] -1.7637042888
[170,] -1.0226704719
[171,] 1.2415626525
[172,] -1.5073503567
[173,] -1.1252338670
[174,] -0.4174620627
[175,] 0.4513187221
[176,] 0.7891844369
[177,] 1.3578952418
[178,] -0.0406326243
[179,] 1.5460255075
[180,] -0.9467515404
[181,] -0.8510748708
[182,] 0.5748782677
[183,] -0.0538972660
[184,] -1.4617822331
[185,] -0.9015193913
[186,] 1.2520788496
[187,] -0.6566761795
[188,] -0.4650020785
[189,] 0.8910505958
[190,] 0.7796328396
[191,] -0.4000441785
[192,] -0.7991462362
[193,] 0.9181164191
[194,] -1.2682560562
[195,] 0.3300862614
[196,] 1.6153528082
[197,] 1.1296915492
[198,] -0.9096999908
[199,] -0.0662762443
[200,] -0.4789422946
[201,] -0.7181137188
[202,] 1.6832621620
[203,] 0.6811287889
[204,] 0.2034170095
[205,] -0.4059470859
[206,] -0.3115920213
[207,] 0.8442806834
[208,] 0.5888419583
[209,] 1.2010057038
[210,] -0.9706991447
[211,] -0.9512557191
[212,] 0.4882461991
[213,] -1.0119725477
[214,] 1.0866642248
[215,] 0.1343720342
[216,] 0.5011556482
[217,] 1.2698604392
[218,] 0.8524612829
[219,] -0.4901706897
[220,] 0.7691016807
[221,] -1.0319848803
[222,] -0.4532901618
[223,] 1.1246761178
[224,] 0.4583252638
[225,] 0.8511908833
[226,] 0.5034635149
[227,] 0.5980557685
[228,] -0.6899912103
[229,] 1.9447982908
[230,] 0.5253471415
[231,] -0.5244183329
[232,] -1.0561461990
[233,] 1.9052788091
[234,] -0.5696998748
[235,] 0.9169121867
[236,] 1.5334068965
[237,] 1.2966823735
[238,] -0.7218285759
[239,] 0.7639621767
[240,] -0.4061330694
[241,] -0.8407833446
[242,] -0.3571686577
[243,] 0.8905201250
[244,] 0.0909148372
[245,] -0.7961520898
[246,] 0.1405657797
[247,] -1.0163376919
[248,] 0.4607227722
[249,] -0.1282098234
[250,] -0.0684149019
[251,] -1.3252428148
[252,] 1.5842770332
[253,] 0.5026464623
[254,] -0.8411662681
[255,] -0.8575082490
[256,] -0.0083014114
[257,] 1.4056748904
[258,] 1.2272117820
[259,] 1.1173552635
[260,] 0.8164212820
[261,] 1.6383400690
[262,] -0.2659534741
[263,] -1.3610239649
[264,] 1.2047784664
[265,] -0.5001286851
[266,] -0.3131063100
[267,] -1.1145551609
[268,] 1.6324179435
[269,] -0.6634003959
[270,] -1.3963173368
[271,] -0.2618471807
[272,] -0.0232297018
[273,] -0.9192473317
[274,] 1.1402461826
[275,] 1.1503047762
[276,] 0.1253786385
[277,] -1.2165603542
[278,] -0.6109732778
[279,] 1.6414348136
[280,] 0.3271625386
[281,] -0.6252964174
[282,] 1.4034910964
[283,] -0.8168122658
[284,] -1.3398140999
[285,] 0.6053766228
[286,] 0.0076750514
[287,] 0.0292528781
[288,] 1.1834914778
[289,] -0.8923247992
[290,] -1.3204670162
[291,] 0.0250737174
[292,] -0.1312341444
[293,] -0.2012821557
[294,] 0.2800289208
[295,] 1.4761485179
[296,] 1.3052458965
[297,] -1.1671340826
[298,] 0.6970131663
[299,] 0.1874043030
[300,] 0.8656850447
[301,] -1.6065985927
[302,] -0.0231635346
[303,] 0.7749811136
[304,] -0.2219380754
[305,] -0.8736087845
[306,] -1.5385459480
[307,] 1.5384458024
[308,] 1.2190973496
[309,] 0.0150452984
[310,] -1.1407612390
[311,] 0.4964761913
[312,] 1.4705321922
[313,] 0.1068293892
[314,] 0.8374643816
[315,] -1.2405634203
[316,] -0.1493535211
[317,] 1.3383621746
[318,] -0.3724478843
[319,] 1.0919704942
[320,] -0.7223867776
[321,] -0.6972566028
[322,] -0.7784625856
[323,] -0.3725442261
[324,] 1.5743425124
[325,] 0.2791734320
[326,] -0.6863147895
[327,] -1.5071341986
[328,] 0.5107566383
[329,] 0.2808417169
[330,] -0.8072329375
[331,] -0.5867113609
[332,] -2.1320021915
[333,] 0.1502371934
[334,] -1.0639204004
[335,] -1.2386878699
[336,] 0.1107454427
[337,] 1.0917266052
[338,] -0.2348475245
[339,] 0.2389801887
[340,] -0.9041028832
[341,] 0.0086823438
[342,] -1.2211928620
[343,] -0.7124287822
[344,] -0.8286138243
[345,] -0.4725323909
[346,] 0.1950117391
[347,] 0.2195367632
[348,] 1.7404693351
[349,] 0.7238158824
[350,] -0.6813572636
[351,] -0.9154553510
[352,] 0.9853754858
[353,] 0.9905149898
[354,] -0.6597023132
[355,] -1.5495112359
[356,] 0.1833299969
[357,] 0.5267885629
[358,] 0.5585103686
[359,] -1.5910712206
[360,] 0.6872948035
[361,] 0.3344087129
[362,] 0.8304578399
[363,] 1.6362526169
[364,] 1.4410411295
[365,] 0.7615604119
[366,] 1.2795510710
[367,] 1.0063566745
[368,] 0.8789347246
[369,] 0.2147652209
[370,] 1.1625681945
[371,] -0.1007827383
[372,] 1.7445713721
[373,] 0.3552121798
[374,] 0.5669348571
[375,] -0.4542739797
[376,] -1.0752301755
[377,] 0.8499162272
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[1600,] 0.2732897427
[1601,] -1.4034202203
[1602,] -0.3951250889
[1603,] 0.4642708639
[1604,] -0.6937319856
[1605,] -1.0087067813
[1606,] -1.6720869635
[1607,] -0.3384718611
[1608,] 0.3111306140
[1609,] -0.0205690862
[1610,] 0.7426324954
[1611,] 0.6950497868
[1612,] 1.3664118158
[1613,] -0.9288760527
[1614,] 1.1151010460
[1615,] 0.7767091168
[1616,] -0.9695677795
[1617,] 0.9771479372
[1618,] -0.9120505501
[1619,] -0.7345989906
[1620,] -0.8374447109
[1621,] -0.2246029474
[1622,] -0.1332060366
[1623,] 0.4202706781
[1624,] 1.6151390937
[1625,] 0.4006177945
[1626,] 0.2437986800
[1627,] -0.4979985402
[1628,] 1.2519270460
[1629,] -0.1267726583
[1630,] -0.8355005496
[1631,] -0.5220208245
[1632,] -0.3820958235
[1633,] 0.9932225545
[1634,] -0.7435262190
[1635,] 0.4570356460
[1636,] 1.0745248791
[1637,] 1.3469107359
[1638,] -0.1265824184
[1639,] 0.1526154837
[1640,] -0.1098809885
[1641,] -0.4185656969
[1642,] -1.4602914189
[1643,] -0.6628507069
[1644,] -0.7709063550
[1645,] 0.0740466419
[1646,] -0.0884080164
[1647,] 0.8193492612
[1648,] -0.7210694289
[1649,] -1.0133627637
[1650,] 0.0708320810
[1651,] -1.0961065097
[1652,] -1.3431183025
[1653,] 0.2267871939
[1654,] -0.7675015541
[1655,] -0.1771592733
[1656,] 1.5517616496
[1657,] 1.3334665596
[1658,] 0.2645509415
[1659,] 1.4779536447
[1660,] 1.2329479240
[1661,] -1.2123427572
[1662,] -0.5464452503
[1663,] -0.9410153984
[1664,] -1.3125001311
[1665,] -0.5075501376
[1666,] -0.6168954034
[1667,] -0.5128948433
[1668,] -0.9828878831
[1669,] -0.8495950131
[1670,] 1.7470929532
[1671,] 1.1487477948
[1672,] -0.1688673703
[1673,] -1.1121064470
[1674,] -0.3563003998
[1675,] -1.0568434352
[1676,] 0.5597572937
[1677,] -1.5023349254
[1678,] 0.7875076392
[1679,] 0.1308004679
[1680,] 0.7207253942
[1681,] 0.2656673449
[1682,] 1.3078638195
[1683,] -0.7329179365
[1684,] -0.0105833599
[1685,] -0.8935205189
[1686,] 0.3483297109
[1687,] -1.8138320530
[1688,] 1.6880379606
[1689,] 0.9671128181
[1690,] 0.1161370975
[1691,] 0.8298804201
[1692,] -0.7267049728
[1693,] -1.3189762020
[1694,] 0.8158438622
[1695,] 0.6566808885
[1696,] 1.3861887721
[1697,] -0.0748482801
[1698,] 0.3864806384
[1699,] -0.0753058837
[1700,] -1.1104765984
[1701,] -1.8840962225
[1702,] 0.8959419544
[1703,] 1.0869081139
[1704,] -1.6469845196
[1705,] 0.4799223087
[1706,] -0.5869787245
[1707,] 0.4197871564
[1708,] -0.9162766599
[1709,] -0.3139191061
[1710,] 0.7784865127
[1711,] -0.5551093716
[1712,] -0.6555810581
[1713,] -1.0301111426
[1714,] 0.9352519780
[1715,] -0.8365849657
[1716,] -1.6218393830
[1717,] -1.0393484273
[1718,] -0.1448450861
[1719,] 0.5658077484
[1720,] -0.6819773762
[1721,] -1.3015348433
[1722,] 1.2847994349
[1723,] -0.2259504707
[1724,] -1.0535517506
[1725,] 0.9912549187
[1726,] 0.3974267081
[1727,] -1.2342456020
[1728,] -1.1465251120
[1729,] 1.0088974738
[1730,] -0.7785372656
[1731,] -0.2939753844
[1732,] 0.0774322246
[1733,] 1.6763284876
[1734,] -1.3661332943
[1735,] -1.4038351311
[1736,] 0.0189613519
[1737,] 1.5840608751
[1738,] -0.2092725153
[1739,] 0.1444498459
[1740,] 0.9366656685
[1741,] -0.0240232798
[1742,] 0.9157423852
[1743,] -0.2858694649
[1744,] -1.3266842362
[1745,] 0.3366864050
[1746,] 1.8071552383
[1747,] -0.9064854299
[1748,] -0.9233795434
[1749,] 0.0599673914
[1750,] 0.9361888468
[1751,] 1.4314937886
[1752,] 1.8727869000
[1753,] 1.4543612331
[1754,] 1.6259845652
[1755,] -1.4219929442
[1756,] -0.1081313236
[1757,] -0.5883924150
[1758,] 1.3030151535
[1759,] -1.1194699940
[1760,] 0.3163128107
[1761,] -0.0024262349
[1762,] -0.8743060207
[1763,] -0.6287506110
[1764,] -0.2425899898
[1765,] -0.5163767678
[1766,] -0.0109747962
[1767,] -0.3945711436
[1768,] 0.6513746190
[1769,] 1.2075908857
[1770,] 0.3318913882
[1771,] 1.1927522370
[1772,] 0.5992490444
[1773,] -0.9311302703
[1774,] 0.3026332581
[1775,] 1.0094471627
[1776,] 0.2970705815
[1777,] 1.0828060768
[1778,] 0.0591738134
[1779,] 0.5761956164
[1780,] 1.5636211136
[1781,] 0.7312330785
[1782,] 0.9422307887
[1783,] 1.4114345069
[1784,] -0.2042101349
[1785,] -0.0246006996
[1786,] -1.1505716872
[1787,] 0.1307468188
[1788,] 0.4851557109
[1789,] 0.0926495404
[1790,] -0.5993107538
[1791,] -0.5249253291
[1792,] -0.8689997512
[1793,] -1.0298010864
[1794,] -0.0133680482
[1795,] -0.4107271410
[1796,] 1.7706106849
[1797,] -0.4309062390
[1798,] -0.2934256955
[1799,] -1.6479448630
[1800,] -0.4257709913
[1801,] -0.6203773279
[1802,] 0.9931521309
[1803,] 0.5154830442
[1804,] 1.4859674789
[1805,] 0.0007648515
[1806,] 1.4022164404
[1807,] -0.2955366222
[1808,] 1.3893069913
[1809,] -1.4704463543
[1810,] 0.3129784334
[1811,] 1.6175366021
[1812,] -0.3331655917
[1813,] -0.5990433902
[1814,] 0.2420941514
[1815,] 0.1284499086
[1816,] -1.1150579007
[1817,] 1.7502605651
[1818,] -0.3574509831
[1819,] -1.4993834717
[1820,] -1.0257996475
[1821,] 0.1517045330
[1822,] 0.8177922799
[1823,] 0.7455839491
[1824,] 0.1598424399
[1825,] -0.3764602796
[1826,] 0.3911558389
[1827,] -0.7266814982
[1828,] -0.7162936302
[1829,] 0.1604775141
[1830,] -0.8036354531
[1831,] -0.9798635621
[1832,] -0.5102064968
[1833,] 1.3372115913
[1834,] 0.7575095803
[1835,] -0.0689411163
[1836,] -1.3275439814
[1837,] -1.2868010492
[1838,] -0.0338379843
[1839,] -0.7102943810
[1840,] -0.5253509453
[1841,] 0.4034963809
[1842,] 0.3674521237
[1843,] -0.3061918537
[1844,] 0.1162846447
[1845,] -0.8230554041
[1846,] 0.0212347876
[1847,] 0.5167107512
[1848,] 1.2973518787
[1849,] 0.1385319767
[1850,] 0.8934011551
[1851,] -0.2666464539
[1852,] -1.4983804356
[1853,] 0.1232911864
[1854,] 0.6419897871
[1855,] -1.1224985714
[1856,] -1.1441660399
[1857,] 1.0621199826
[1858,] -1.9023781083
[1859,] -0.9144288404
[1860,] -0.0425490546
[1861,] -0.8478133608
[1862,] 0.5673882043
[1863,] 0.7764182787
[1864,] 0.5302939619
[1865,] -0.0072682008
[1866,] -1.1651387159
[1867,] 0.0500371269
[1868,] 1.4247503541
[1869,] 1.2548358070
[1870,] 0.8420968894
[1871,] -1.1433255128
[1872,] 0.3745784817
[1873,] 0.8659524082
[1874,] -0.0623687036
[1875,] -0.3978838591
[1876,] 0.8154139896
[1877,] -0.6882632071
[1878,] 0.5738944498
[1879,] -1.2880949234
[1880,] -0.6830810104
[1881,] 1.0271985777
[1882,] 1.2175403683
[1883,] -0.7366394936
[1884,] 0.5354953768
[1885,] -0.3902764230
[1886,] -0.8845271233
[1887,] 1.2905398334
[1888,] 1.3810065754
[1889,] -0.9005547915
[1890,] -1.3025890848
[1891,] -0.9576656228
[1892,] 0.2390698304
[1893,] -0.3445180594
[1894,] -0.1419171069
[1895,] 1.0703457184
[1896,] -0.7056618732
[1897,] -0.0286557876
[1898,] 0.4905004166
[1899,] 1.0373620259
[1900,] 0.6160745470
[1901,] -1.6612649665
[1902,] -0.1026113397
[1903,] -0.3912837154
[1904,] -0.2599499684
[1905,] -0.4858332764
[1906,] -0.4834315116
[1907,] 0.1556632792
[1908,] 1.4215827423
[1909,] -0.0405814188
[1910,] -1.0583577239
[1911,] 1.1003480338
[1912,] -1.2109675030
[1913,] 0.2689438166
[1914,] 1.3393225179
[1915,] 0.8010137263
[1916,] 1.6230331115
[1917,] -0.5517280453
[1918,] -0.9771986901
[1919,] -0.3686366854
[1920,] 1.7552031292
[1921,] -1.3332801235
[1922,] -0.7883199828
[1923,] 1.1211749752
[1924,] 0.8828892144
[1925,] -1.0560306390
[1926,] -0.4940590122
[1927,] -0.6512351320
[1928,] -1.4135022884
[1929,] 1.5581715533
[1930,] 0.3461181860
[1931,] 0.5034141221
[1932,] -0.0293764983
[1933,] 1.3216372702
[1934,] 1.5442054190
[1935,] -0.8534062120
[1936,] 1.0494693843
[1937,] -0.5141033321
[1938,] -0.2271781776
[1939,] -0.0040838144
[1940,] 0.8127533740
[1941,] -0.4087787233
[1942,] 0.3689152069
[1943,] -1.0198430910
[1944,] -0.1013409400
[1945,] -0.9711055427
[1946,] 1.4045669997
[1947,] 1.0190414527
[1948,] 0.6177556011
[1949,] -1.6341220195
[1950,] -0.1367114356
[1951,] 0.8002930156
[1952,] 0.3653177225
[1953,] -1.0254594167
[1954,] -1.1222077333
[1955,] 0.6121435318
[1956,] 0.8592324483
[1957,] -0.3550300001
[1958,] 0.9508412608
[1959,] -0.0976965065
[1960,] 0.9808862688
[1961,] -0.2660498159
[1962,] -0.5585376469
[1963,] 1.4057947067
[1964,] 1.0023400229
[1965,] 1.5872989105
[1966,] 0.4857096562
[1967,] 0.0512026719
[1968,] -0.4458537475
[1969,] 0.8026243568
[1970,] -0.8791287684
[1971,] -1.1934063280
[1972,] 0.2097028405
[1973,] 0.0316226556
[1974,] 0.8315379996
[1975,] 1.0748818844
[1976,] 0.6033620379
[1977,] 0.0468140532
[1978,] 0.6403322075
[1979,] -0.6580749083
[1980,] 0.7067851781
[1981,] -0.1250723861
[1982,] -1.2176212958
[1983,] -0.5205751466
[1984,] -0.6150710584
[1985,] 0.5638358562
[1986,] -0.0654677046
[1987,] -0.6734330713
[1988,] 1.8381136406
[1989,] 0.1358671047
[1990,] -0.5903835254
[1991,] 0.6573010010
[1992,] -0.3497194743
[1993,] -0.1083944308
[1994,] -0.5214434046
[1995,] 0.1436605243
[1996,] -0.1078127545
[1997,] -1.1039770530
[1998,] -0.4637508970
[1999,] 0.1399648854
[2000,] 1.2500834829Berdasarkan output di atas, dapat dilihat bahwa komunalitas setiap variabel memiliki nilai < 1. Pada metode PFA sudah seharusnya komunalitas tiap variabel bernilai < 1, sehingga sudah tepat bahwa PFA menjadi metode ekstraksi faktor pada kasus ini.
Model analisis faktor yang terbentuk sebagai berikut:
\[ \text{Study_Hours_Per_Day} = 0.88PA_{1} + u_1 \\ \text{Social_Hours_Per_Day} = -0.13PA_{1} + u_2 \\ \text{GPA} = 0.83PA_{1} + u_3 \\ \] Pada output bagian Proportion Variance, faktor \(PA_1\) dapat menjelaskan variansi sebesar 50%, sehingga dapat disimpulkan bahwa faktor dapat menjelaskan variansi sebesar 50%.
3.5.7 Visualisasi
Study_Hours_Per_Day Social_Hours_Per_Day GPA
0.8793 -0.1311 0.8347 Berdasarkan visualisasi di atas, dapat dilihat bahwa faktor \(PA_1\) memiliki korelasi signifikan terhadap Study_Hours_Per_Day, dan GPA. Variabel Social_Hours_Per_Day tidak memiliki tanda panah menuju atau keluar dari variabel lain. Hal ini disebabkan karena faktor tersebut memiliki loading yang sangat rendah atau negatif (dalam hal ini, -0.1311) pada faktor yang diekstraksi \(PA_1\), sehingga tidak dianggap cukup signifikan untuk ditampilkan sebagai hubungan langsung dalam model.
Hasil dari 3 variabel menghasilkan 1 buah faktor yang dapat dikelompokkan berdasarkan indikator.
Faktor tersebut diinterpretasikan sebagai faktor GPA terhadap kebiasaan belajar.
4 PENUTUP
4.1 Kesimpulan
Berdasarkan hasil analisis, dapat ditarik kesimpulan bahwa:
Terdapat pengaruh antara gaya hidup terhadap performa akademik mahasiswa, dimana variabel yang berpengaruh terhadap GPA adalah variabel Study_Hours_Per_Day (Waktu belajar per hari).
Dengan menggunakan K-Means Clustering diperoleh wawasan yang lebih mendalam mengenai karakteristik mahasiswa, sehingga dapat membuat keputusan yang lebih tepat dalam intervensi strategis dalam meningkatkan performa akademik melalui keseimbangan gaya hidup.
Dengan menggunakan Analisis Faktor dapat disimpulkan bahwa dari beberapa variabel yang ada, variabel Study_Hours_Per_Day lah yang berpengaruh paling signifikan terhadap performa akademik (GPA)
Integrasi K-Means Clustering dan Analisis Faktor menghasilkan wawasan komprehensif dengan mengelompokkan individu berdasarkan pola dominan dan memahami hubungan antar variabel. Pendekatan ini mendukung pengambilan keputusan yang lebih tepat dan strategis
4.2 Saran
Saran yang dapat diberikan yaitu memperbanyak indikator-indikator gaya hidup dan performa akademik.
4.3 Daftar Pustaka
Sumber Data : https://www.kaggle.com/datasets/steve1215rogg/student-lifestyle-dataset
Tifani Amalina, D. B. (2022). Metode K-Means Clustering Dalam Pengelompokkan Penjualan Produk Frozen Food. Jurnal Ilmuah Wahana Pendidikan, 576-577.
Jain, A. K. (2010). Data Clustering: 50 Years Beyond K-Means. Pattern Recognition Letters, 31(8), 651-666.
Rousseeuw, P. J. (1987). Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis. Journal of Computational and Graphical Statistics, 1(1), 53-65.
Purwanto. 2004. Analisis Faktor: Konsep, Prosedur Uji, dan Interpretasi. Jurnal Teknodik, No. 15 Vol. VIII
Xie, Yihui. 2016. bookdown Authoring Books and Technical Documents with R Markdown. Chapman and Hall