Bulan 2: Statistik Deskriptif

import statistics

# Dua kelas dengan mean sama tapi sebaran berbeda
kelas_A = [70, 72, 74, 75, 76, 78, 78, 79, 80, 78]
kelas_B = [40, 50, 60, 70, 80, 90, 95, 98, 82, 75]

for nama, data in [("Kelas A", kelas_A), ("Kelas B", kelas_B)]:
    print(f"\n{nama}:")
    print(f"  Data  : {sorted(data)}")
    print(f"  Mean  : {statistics.mean(data):.2f}")
    print(f"  Range : {max(data) - min(data)}")

print("\n=> Mean sama, range berbeda. Tapi apakah range cukup?")

import statistics
import math

data = [70, 72, 74, 75, 76, 78, 78, 79, 80, 78]
mean = statistics.mean(data)

# Varians manual (populasi)
deviasi_kuadrat = [(x - mean) ** 2 for x in data]
varians_manual = sum(deviasi_kuadrat) / len(data)
stdev_manual = math.sqrt(varians_manual)

print(f"Mean: {mean:.2f}")
print(f"\nDeviasi tiap titik: {[round(x - mean, 2) for x in data]}")
print(f"Deviasi kuadrat: {[round(d, 2) for d in deviasi_kuadrat]}")
print(f"\nVarians (populasi) manual : {varians_manual:.4f}")
print(f"Stdev  (populasi) manual  : {stdev_manual:.4f}")
print(f"\nVarians (sampel) library  : {statistics.variance(data):.4f}")
print(f"Stdev  (sampel) library   : {statistics.stdev(data):.4f}")

import statistics

def hitung_kuartil(data):
    data_sorted = sorted(data)
    n = len(data_sorted)
    q1 = statistics.median(data_sorted[:n//2])
    q2 = statistics.median(data_sorted)
    q3 = statistics.median(data_sorted[(n+1)//2:])
    return q1, q2, q3

harga = [3, 5, 5, 7, 3, 10, 5, 8, 3, 5,
         6, 4, 5, 3, 7, 5, 4, 6, 5, 25]

q1, q2, q3 = hitung_kuartil(harga)
iqr = q3 - q1

batas_bawah = q1 - 1.5 * iqr
batas_atas  = q3 + 1.5 * iqr

print(f"Q1: {q1}, Q2: {q2}, Q3: {q3}")
print(f"IQR: {iqr}")
print(f"Batas normal: [{batas_bawah:.2f}, {batas_atas:.2f}]")

outlier = [x for x in harga if x < batas_bawah or x > batas_atas]
print(f"\nOutlier terdeteksi: {outlier}")

import matplotlib.pyplot as plt

kelas_A = [70, 72, 74, 75, 76, 78, 78, 79, 80, 78]
kelas_B = [40, 50, 60, 70, 80, 90, 95, 98, 82, 75]

# Boxplot perbandingan
fig, ax = plt.subplots(figsize=(7, 5))
ax.boxplot(
    [kelas_A, kelas_B],
    labels=["Kelas A", "Kelas B"],
    patch_artist=True,
    boxprops=dict(facecolor="#e8f4f8")
)
ax.set_title("Perbandingan Sebaran Nilai Dua Kelas")
ax.set_ylabel("Nilai")
ax.grid(axis="y", alpha=0.4)
plt.tight_layout()
plt.savefig("boxplot_kelas.png", dpi=100)
plt.show()
print("Boxplot disimpan sebagai boxplot_kelas.png")

import statistics
import math

def laporan_sebaran(data, label="Dataset"):
    data_s = sorted(data)
    n = len(data_s)
    q1 = statistics.median(data_s[:n//2])
    q3 = statistics.median(data_s[(n+1)//2:])
    iqr = q3 - q1

    print(f"\n{'='*40}")
    print(f"Laporan Sebaran: {label}")
    print(f"{'='*40}")
    print(f"N              : {n}")
    print(f"Range          : {max(data) - min(data)}")
    print(f"Varians (samp) : {statistics.variance(data):.4f}")
    print(f"Stdev   (samp) : {statistics.stdev(data):.4f}")
    print(f"Q1             : {q1}")
    print(f"Median (Q2)    : {statistics.median(data)}")
    print(f"Q3             : {q3}")
    print(f"IQR            : {iqr}")

    batas_atas = q3 + 1.5 * iqr
    batas_bawah = q1 - 1.5 * iqr
    outlier = [x for x in data if x < batas_bawah or x > batas_atas]
    print(f"Outlier        : {outlier if outlier else 'Tidak ada'}")

nilai = [72, 85, 60, 91, 78, 85, 55, 88, 70, 85]
harga = [3, 5, 5, 7, 3, 10, 5, 8, 3, 5, 6, 4, 5, 3, 7, 5, 4, 6, 5, 25]

laporan_sebaran(nilai, "Nilai Ujian")
laporan_sebaran(harga, "Harga Jajanan Kantin")

import matplotlib.pyplot as plt
import random

# Buat dataset nilai 30 siswa
random.seed(42)
nilai_30 = [random.gauss(75, 10) for _ in range(30)]
nilai_30 = [max(0, min(100, round(n))) for n in nilai_30]

fig, axes = plt.subplots(1, 3, figsize=(14, 4))
for ax, bins, title in zip(axes, [5, 10, 20],
                            ["5 Bin", "10 Bin", "20 Bin"]):
    ax.hist(nilai_30, bins=bins, color="#4a90d9", edgecolor="white", alpha=0.85)
    ax.set_title(f"Histogram {title}")
    ax.set_xlabel("Nilai")
    ax.set_ylabel("Frekuensi")
    ax.grid(axis="y", alpha=0.3)

plt.tight_layout()
plt.savefig("histogram_bins.png", dpi=100)
plt.show()
print("Disimpan: histogram_bins.png")

import numpy as np
import matplotlib.pyplot as plt

# Parameter distribusi normal
mu, sigma = 75, 10
x = np.linspace(mu - 4*sigma, mu + 4*sigma, 300)
y = (1 / (sigma * np.sqrt(2 * np.pi))) * np.exp(-0.5 * ((x - mu) / sigma)**2)

fig, ax = plt.subplots(figsize=(9, 5))
ax.plot(x, y, color="#2c6fad", linewidth=2)

# Zona 68%, 95%, 99.7%
for n_std, color, label in [(1, "#cce5ff", "68% (1 stdev)"),
                             (2, "#e8f4f8", "95% (2 stdev)"),
                             (3, "#f5fafe", "99.7% (3 stdev)")]:
    ax.fill_between(x, y,
                    where=(x >= mu - n_std*sigma) & (x <= mu + n_std*sigma),
                    color=color, label=label)

ax.axvline(mu, color="#555", linestyle="--", linewidth=1, label=f"Mean = {mu}")
ax.set_title("Distribusi Normal: Aturan 68-95-99.7")
ax.set_xlabel("Nilai")
ax.set_ylabel("Densitas")
ax.legend(fontsize=9)
ax.grid(alpha=0.3)
plt.tight_layout()
plt.savefig("distribusi_normal.png", dpi=100)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

# Distribusi condong kanan (positif skew): banyak nilai kecil, sedikit sangat besar
data_skew_kanan = np.random.exponential(scale=5, size=300)

# Distribusi condong kiri (negatif skew): banyak nilai besar, sedikit sangat kecil
data_skew_kiri = 10 - np.random.exponential(scale=2, size=300)

fig, axes = plt.subplots(1, 2, figsize=(11, 4))

for ax, data, title, color in zip(
    axes,
    [data_skew_kanan, data_skew_kiri],
    ["Skew Positif (condong kanan)", "Skew Negatif (condong kiri)"],
    ["#f4a261", "#457b9d"]
):
    ax.hist(data, bins=30, color=color, edgecolor="white", alpha=0.85)
    ax.axvline(np.mean(data), color="red", linestyle="--", label=f"Mean={np.mean(data):.1f}")
    ax.axvline(np.median(data), color="green", linestyle="-.", label=f"Median={np.median(data):.1f}")
    ax.set_title(title)
    ax.legend(fontsize=8)
    ax.grid(axis="y", alpha=0.3)

plt.tight_layout()
plt.savefig("skewness_compare.png", dpi=100)
plt.show()

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(7)
n = 50

# Empat skenario korelasi
jam_belajar = np.random.uniform(1, 10, n)
nilai_tinggi  = jam_belajar * 8 + np.random.normal(0, 5, n)   # r ~ +0.95
nilai_sedang  = jam_belajar * 4 + np.random.normal(0, 15, n)  # r ~ +0.5
nilai_tidak   = np.random.uniform(50, 100, n)                  # r ~ 0
nilai_negatif = -jam_belajar * 6 + 90 + np.random.normal(0, 8, n)  # r ~ -0.9

skenario = [
    (nilai_tinggi,  "Korelasi Kuat Positif"),
    (nilai_sedang,  "Korelasi Sedang Positif"),
    (nilai_tidak,   "Hampir Tidak Berkorelasi"),
    (nilai_negatif, "Korelasi Kuat Negatif"),
]

fig, axes = plt.subplots(2, 2, figsize=(10, 8))
for ax, (y, title) in zip(axes.flat, skenario):
    r = np.corrcoef(jam_belajar, y)[0, 1]
    ax.scatter(jam_belajar, y, alpha=0.6, color="#4a90d9", s=30)
    ax.set_title(f"{title}
r = {r:.2f}", fontsize=10)
    ax.set_xlabel("Jam Belajar")
    ax.set_ylabel("Nilai")
    ax.grid(alpha=0.3)

plt.tight_layout()
plt.savefig("korelasi_scatter.png", dpi=100)
plt.show()

import numpy as np
import matplotlib.pyplot as plt
import statistics

random_seed = 42
np.random.seed(random_seed)
nilai = sorted([max(40, min(100, round(np.random.normal(75, 12))))
                for _ in range(30)])

mean_val   = statistics.mean(nilai)
median_val = statistics.median(nilai)
stdev_val  = statistics.stdev(nilai)

fig = plt.figure(figsize=(14, 5))
fig.suptitle("Dashboard Distribusi: Nilai Ujian 30 Siswa", fontsize=13, fontweight="bold")

# Subplot 1: Histogram
ax1 = fig.add_subplot(1, 3, 1)
ax1.hist(nilai, bins=8, color="#4a90d9", edgecolor="white", alpha=0.85)
ax1.axvline(mean_val, color="red", linestyle="--", linewidth=1.5, label=f"Mean={mean_val:.1f}")
ax1.axvline(median_val, color="green", linestyle="-.", linewidth=1.5, label=f"Median={median_val:.1f}")
ax1.set_title("Histogram")
ax1.legend(fontsize=8)
ax1.grid(axis="y", alpha=0.3)

# Subplot 2: Boxplot
ax2 = fig.add_subplot(1, 3, 2)
ax2.boxplot(nilai, vert=False, patch_artist=True,
            boxprops=dict(facecolor="#cce5ff"))
ax2.set_title("Boxplot")
ax2.set_xlabel("Nilai")
ax2.grid(axis="x", alpha=0.3)
ax2.annotate(f"Stdev = {stdev_val:.1f}", xy=(0.5, 0.1),
             xycoords="axes fraction", ha="center", fontsize=9)

# Subplot 3: Scatter
ax3 = fig.add_subplot(1, 3, 3)
ax3.scatter(range(1, 31), nilai, color="#e07b39", s=40, alpha=0.8)
ax3.axhline(mean_val, color="red", linestyle="--", linewidth=1, label=f"Mean")
ax3.set_title("Nilai per Urutan Rank")
ax3.set_xlabel("Rank Siswa")
ax3.set_ylabel("Nilai")
ax3.legend(fontsize=8)
ax3.grid(alpha=0.3)

plt.tight_layout()
plt.savefig("dashboard_distribusi.png", dpi=150)
plt.show()
print("Disimpan: dashboard_distribusi.png")

import numpy as np

# Konversi ke NumPy array
nilai = np.array([72, 85, 60, 91, 78, 85, 55, 88, 70, 85])

# Statistik dasar NumPy
print("=== NumPy Statistics ===")
print(f"Mean        : {np.mean(nilai):.2f}")
print(f"Median      : {np.median(nilai):.2f}")
print(f"Stdev (pop) : {np.std(nilai):.2f}")
print(f"Stdev (samp): {np.std(nilai, ddof=1):.2f}")
print(f"Varians     : {np.var(nilai, ddof=1):.2f}")
print(f"Min / Max   : {np.min(nilai)} / {np.max(nilai)}")
print(f"Q1  / Q3    : {np.percentile(nilai, 25)} / {np.percentile(nilai, 75)}")

# Operasi vektor: normalisasi nilai ke skala 0-1
nilai_norm = (nilai - np.min(nilai)) / (np.max(nilai) - np.min(nilai))
print(f"
Nilai ternormalisasi:")
print(np.round(nilai_norm, 3))

import numpy as np

np.random.seed(21)
nilai_100 = np.random.normal(loc=75, scale=10, size=100).clip(0, 100).round().astype(int)

mean_val  = np.mean(nilai_100)
stdev_val = np.std(nilai_100, ddof=1)

# Boolean masking
di_atas_mean = nilai_100[nilai_100 > mean_val]
di_bawah_mean = nilai_100[nilai_100 <= mean_val]

print(f"Mean: {mean_val:.2f}, Stdev: {stdev_val:.2f}")
print(f"Di atas mean: {len(di_atas_mean)} siswa ({len(di_atas_mean)}%)")
print(f"Di bawah mean: {len(di_bawah_mean)} siswa")

# Zona 1 stdev
dalam_1_stdev = nilai_100[
    (nilai_100 >= mean_val - stdev_val) &
    (nilai_100 <= mean_val + stdev_val)
]
print(f"
Dalam 1 stdev: {len(dalam_1_stdev)} siswa ({len(dalam_1_stdev):.0f}%)")

# Label lulus/tidak
label = np.where(nilai_100 >= 70, "Lulus", "Tidak Lulus")
lulus = np.sum(label == "Lulus")
print(f"
Lulus (>=70): {lulus} dari 100 siswa")

import numpy as np
import matplotlib.pyplot as plt

np.random.seed(5)
n = 50

jam_belajar = np.random.uniform(1, 8, n)
nilai       = jam_belajar * 7 + np.random.normal(0, 8, n)
jam_tidur   = np.random.uniform(5, 9, n)
jarak_km    = np.random.uniform(1, 20, n)

# Stack jadi matriks (4 x n), lalu transpose ke (n x 4)
data_matrix = np.column_stack([jam_belajar, nilai, jam_tidur, jarak_km])
labels = ["Jam Belajar", "Nilai", "Jam Tidur", "Jarak (km)"]

# Correlation matrix: np.corrcoef butuh (n_vars x n_obs)
corr_matrix = np.corrcoef(data_matrix.T)

fig, ax = plt.subplots(figsize=(6, 5))
im = ax.imshow(corr_matrix, cmap="coolwarm", vmin=-1, vmax=1)
plt.colorbar(im, ax=ax)
ax.set_xticks(range(4))
ax.set_yticks(range(4))
ax.set_xticklabels(labels, rotation=30, ha="right", fontsize=9)
ax.set_yticklabels(labels, fontsize=9)

for i in range(4):
    for j in range(4):
        ax.text(j, i, f"{corr_matrix[i, j]:.2f}",
                ha="center", va="center", fontsize=9,
                color="white" if abs(corr_matrix[i, j]) > 0.6 else "black")

ax.set_title("Correlation Matrix: Variabel Siswa")
plt.tight_layout()
plt.savefig("heatmap_korelasi.png", dpi=120)
plt.show()

import numpy as np

nilai_mat   = np.array([85, 72, 90, 65, 78])
nilai_ing   = np.array([75, 80, 70, 90, 65])
nama_siswa  = ["Ari", "Budi", "Citra", "Dian", "Eka"]

def z_score(data):
    return (data - np.mean(data)) / np.std(data, ddof=1)

z_mat = z_score(nilai_mat)
z_ing = z_score(nilai_ing)

print(f"{'Nama':<8} {'Mat':>6} {'z-Mat':>7} {'Ing':>6} {'z-Ing':>7} {'Total z':>8}")
print("-" * 48)
for i, nama in enumerate(nama_siswa):
    total_z = z_mat[i] + z_ing[i]
    print(f"{nama:<8} {nilai_mat[i]:>6} {z_mat[i]:>+7.2f} "
          f"{nilai_ing[i]:>6} {z_ing[i]:>+7.2f} {total_z:>+8.2f}")

# Siapa yang performanya paling baik secara keseluruhan?
terbaik = nama_siswa[np.argmax(z_mat + z_ing)]
print(f"
Performa terbaik keseluruhan: {terbaik}")

import numpy as np
import matplotlib.pyplot as plt
import statistics

# ============================================================
# Mini Project Bulan 2: Analisis Deskriptif Dataset Nilai SMA
# Ganti dataset di bawah dengan data nyata pilihan Abbas
# ============================================================

np.random.seed(99)
# Dataset contoh: nilai 60 siswa, 3 mata pelajaran
mat = np.random.normal(72, 12, 60).clip(30, 100).round().astype(int)
ing = np.random.normal(68, 15, 60).clip(30, 100).round().astype(int)
bio = np.random.normal(75, 10, 60).clip(30, 100).round().astype(int)

variabel  = {"Matematika": mat, "Inggris": ing, "Biologi": bio}

# --- Ringkasan statistik ---
print("=" * 55)
print(f"{'Variabel':<14} {'Mean':>7} {'Median':>8} {'Stdev':>7} {'IQR':>7}")
print("-" * 55)
for nama, data in variabel.items():
    q1, q3 = np.percentile(data, [25, 75])
    print(f"{nama:<14} {np.mean(data):>7.2f} {np.median(data):>8.2f} "
          f"{np.std(data, ddof=1):>7.2f} {q3-q1:>7.2f}")

# --- Figure empat panel ---
fig, axes = plt.subplots(2, 2, figsize=(13, 9))
fig.suptitle("Laporan Statistik Deskriptif: Nilai 60 Siswa", fontsize=13, fontweight="bold")

# Panel 1: Histogram
ax = axes[0, 0]
for nama, data, color in zip(variabel.keys(), variabel.values(),
                              ["#4a90d9", "#e07b39", "#5cb85c"]):
    ax.hist(data, bins=12, alpha=0.55, label=nama, color=color, edgecolor="white")
ax.set_title("Distribusi Nilai per Mata Pelajaran")
ax.legend(fontsize=8)
ax.grid(axis="y", alpha=0.3)

# Panel 2: Boxplot
ax = axes[0, 1]
ax.boxplot(list(variabel.values()), labels=list(variabel.keys()),
           patch_artist=True,
           boxprops=dict(facecolor="#e8f4f8"))
ax.set_title("Boxplot Perbandingan")
ax.grid(axis="y", alpha=0.3)

# Panel 3: Scatter Mat vs Ing
ax = axes[1, 0]
ax.scatter(mat, ing, alpha=0.6, color="#4a90d9", s=35)
r = np.corrcoef(mat, ing)[0, 1]
ax.set_title(f"Scatter: Matematika vs Inggris (r={r:.2f})")
ax.set_xlabel("Matematika")
ax.set_ylabel("Inggris")
ax.grid(alpha=0.3)

# Panel 4: Heatmap korelasi
ax = axes[1, 1]
corr = np.corrcoef(np.column_stack([mat, ing, bio]).T)
nama_list = list(variabel.keys())
im = ax.imshow(corr, cmap="coolwarm", vmin=-1, vmax=1)
plt.colorbar(im, ax=ax)
ax.set_xticks(range(3))
ax.set_yticks(range(3))
ax.set_xticklabels(nama_list, rotation=20, ha="right", fontsize=8)
ax.set_yticklabels(nama_list, fontsize=8)
for i in range(3):
    for j in range(3):
        ax.text(j, i, f"{corr[i,j]:.2f}", ha="center", va="center", fontsize=9,
                color="white" if abs(corr[i, j]) > 0.6 else "black")
ax.set_title("Heatmap Korelasi")

plt.tight_layout()
plt.savefig("laporan_bulan02.png", dpi=150)
plt.show()
print("Selesai. Simpan juga laporan_bulan02.md dengan interpretasi teks.")