082111733078_Ayub Nasrurohman Fahri_Praktikum 9.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 939
        },
        "id": "RP9ekYBabA6_",
        "outputId": "06059670-0f0b-4ed5-e1be-eab14cdc4247"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "y = bX + a\n",
            "Bentuk Persamaan Regresi Liniernya adalah: f(x) =  2.926724137931035 + 0.643103448275862 x\n",
            "Nilai a: 2.926724137931035\n",
            "Nilai b: 0.643103448275862\n",
            "\n",
            "\n",
            "Tabel regresi linear dari data:\n",
            "   No.    x    y    xy   x^2    s ke i\n",
            "0    1  0.0  2.9   0.0  0.00  0.000714\n",
            "1    2  1.0  3.7   3.7  1.00  0.016945\n",
            "2    3  2.0  4.1   8.2  4.00  0.012753\n",
            "3    4  2.5  4.4  11.0  6.25  0.018086\n",
            "4    5  3.0  5.0  15.0  9.00  0.020726\n",
            "Jumlah X: 8.5\n",
            "Jumlah Y: 20.1\n",
            "Jumlah XY: 37.9\n",
            "Jumlah X^2: 20.25\n",
            "\n",
            "\n",
            "Maka, \n",
            "Nilai a: 2.926724137931035\n",
            "Nilai b: 0.643103448275862\n",
            "Nilai S: 0.06922413793103449\n",
            "\n",
            "\n",
            "Grafik plot data:\n",
            "Keterangan : Garis kuning = hasil regresi linear\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Contoh Soal\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from numpy import *\n",
        "import pylab\n",
        "\n",
        "# Input Data\n",
        "x = np.array([0.0, 1.0, 2.0, 2.5, 3.0])\n",
        "y = np.array([2.9, 3.7, 4.1, 4.4, 5.0])\n",
        "n = len (x)\n",
        "\n",
        "# Menghitung nilai xy dan x^2\n",
        "xy = x * y\n",
        "x_squared = x ** 2\n",
        "\n",
        "# Menghitung nilai a dan b menggunakan regresi linier\n",
        "mean_x = np.mean(x)\n",
        "mean_y = np.mean(y)\n",
        "b = np.sum((x - mean_x) * (y - mean_y)) / np.sum((x - mean_x) ** 2)\n",
        "a = mean_y - b * mean_x\n",
        "nilai_s = (y - a - b * x) ** 2\n",
        "\n",
        "# Mengatur tampilan dataframe\n",
        "data = {'No.': range(1, len(x) + 1), 'x': x, 'y': y, 'xy': xy, 'x^2': x_squared,'s ke i' : nilai_s}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Menghitung jumlah nilai s\n",
        "S = np.sum(nilai_s)\n",
        "\n",
        "# Menghitung jumlah x, jumlah y, jumlah xy, dan jumlah x^2\n",
        "jumx=np.sum(x)\n",
        "jumy=np.sum(y)\n",
        "jumxy=np.sum(xy)\n",
        "jumx_squared=np.sum(x_squared)\n",
        "\n",
        "#C^-1.D\n",
        "M = np.array([[n,jumx],[jumx,jumx_squared]])\n",
        "N = np.array([[jumy],[jumxy]])\n",
        "ab = np.dot(np.linalg.inv(M),N)\n",
        "ybaru = zeros(n,float)\n",
        "for i in range (0,n):\n",
        "  ybaru[i] = ab[1]*x[i]+ab[0]\n",
        "\n",
        "\n",
        "# Menampilkan nilai a dan b\n",
        "print ('y = bX + a')\n",
        "print(\"Bentuk Persamaan Regresi Liniernya adalah: f(x) = \",a,\"+\",b,\"x\" )\n",
        "print(\"Nilai a:\", a)\n",
        "print(\"Nilai b:\", b)\n",
        "print('\\n')\n",
        "\n",
        "# Mendampilkan DataFrame\n",
        "print('Tabel regresi linear dari data:')\n",
        "print(df)\n",
        "print(\"Jumlah X:\", jumx)\n",
        "print(\"Jumlah Y:\", jumy)\n",
        "print(\"Jumlah XY:\", jumxy)\n",
        "print(\"Jumlah X^2:\", jumx_squared)\n",
        "print('\\n')\n",
        "\n",
        "# Menampilkan nilai a, b dan S\n",
        "print('Maka, ')\n",
        "print(\"Nilai a:\", a)\n",
        "print(\"Nilai b:\", b)\n",
        "print(\"Nilai S:\", S)\n",
        "print('\\n')\n",
        "\n",
        "# Menampilkan grafik hasil regresi\n",
        "print('Grafik plot data:')\n",
        "pylab.plot(x,y,'og')\n",
        "pylab.plot(x,ybaru,'-y')\n",
        "print('Keterangan : Garis kuning = hasil regresi linear')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "oJ2sI-gGbMGf",
        "outputId": "321538dd-93b7-4ff2-c7f3-41df9d819d2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Y = bX+A\n",
            "ln(f(x))= b(ln(x))+ln(a)\n",
            "Bentuk Persamaan Regresi Liniernya adalah: ln(f(x)) =  0.532473481198709 + 1.8817501813721564 ln(x)\n",
            "Nilai a: 1.7031397871561753\n",
            "Nilai ln(a): 0.532473481198709\n",
            "Nilai b: 1.8817501813721564\n",
            "\n",
            "\n",
            "Tabel regresi linear dari data:\n",
            "   No.    x      y     ln(x)     ln(y)  ln(x)*ln(y)  (ln(x))^2  (ln(y))^2  \\\n",
            "0    1  0.5   0.49 -0.693147 -0.713350     0.494456   0.480453   0.508868   \n",
            "1    2  1.0   1.60  0.000000  0.470004     0.000000   0.000000   0.220903   \n",
            "2    3  1.5   3.36  0.405465  1.211941     0.491400   0.164402   1.468801   \n",
            "3    4  2.0   6.44  0.693147  1.862529     1.291006   0.480453   3.469013   \n",
            "4    5  2.5  10.16  0.916291  2.318458     2.124382   0.839589   5.375250   \n",
            "\n",
            "     s ke i  \n",
            "0  0.003423  \n",
            "1  0.003902  \n",
            "2  0.006975  \n",
            "3  0.000662  \n",
            "4  0.003814  \n",
            "Jumlah x: 7.5\n",
            "Jumlah y: 22.05\n",
            "Jumlah lnx: 1.3217558399823195\n",
            "Jumlah lny: 5.149581697609982\n",
            "Jumlah ln(x)ln(y): 4.401244630640314\n",
            "Jumlah (lnx)^2: 1.9648966870480429\n",
            "Jumlah (lny)^2: 11.042834509164479\n",
            "\n",
            "\n",
            "Maka, \n",
            "Nilai a: 1.7031397871561753\n",
            "Nilai b: 1.8817501813721564\n",
            "Nilai ln(a): 0.532473481198709\n",
            "Nilai S: 0.018775933950291812\n",
            "\n",
            "\n",
            "Grafik plot data:\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x400 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Soal No 1\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from numpy import *\n",
        "import pylab\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import linregress\n",
        "\n",
        "# Memasukkan data\n",
        "x = np.array([0.5, 1.0, 1.5, 2.0, 2.5])\n",
        "y = np.array([0.49, 1.60, 3.36, 6.44, 10.16])\n",
        "\n",
        "# Menyesuaikan data dengan persamaan regresi linier\n",
        "X = np.log(x)\n",
        "Y = np.log(y)\n",
        "\n",
        "# Menghitung nilai xy, y^2 dan x^2\n",
        "XY = X * Y\n",
        "X_squared = X ** 2\n",
        "Y_squared = Y ** 2\n",
        "\n",
        "# Menghitung nilai a dan b menggunakan regresi linier\n",
        "mean_X = np.mean(X)\n",
        "mean_Y = np.mean(Y)\n",
        "b = np.sum((X - mean_X) * (Y - mean_Y)) / np.sum((X - mean_X) ** 2)\n",
        "A = mean_Y - b * mean_X\n",
        "a = np.exp(A)\n",
        "nilai_s = (Y - A - b * X) ** 2\n",
        "\n",
        "# Mengatur tampilan dataframe\n",
        "data = {'No.': range(1, len(x) + 1), 'x': x, 'y': y, 'ln(x)': X, 'ln(y)': Y, 'ln(x)*ln(y)': XY, '(ln(x))^2': X_squared, '(ln(y))^2': Y_squared,'s ke i' : nilai_s}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Menghitung jumlah nilai s\n",
        "S = np.sum(nilai_s)\n",
        "\n",
        "# Menghitung jumlah x, jumlah y,jumlah ln(x), jumlah ln(y), jumlah ln(x)ln(y), jumlah (lny)^2 dan jumlah (lnx)^2\n",
        "jumx=np.sum(x)\n",
        "jumy=np.sum(y)\n",
        "jumX=np.sum(X)\n",
        "jumY=np.sum(Y)\n",
        "jumXY=np.sum(XY)\n",
        "jumX_squared=np.sum(X_squared)\n",
        "jumY_squared=np.sum(Y_squared)\n",
        "\n",
        "\n",
        "# Menampilkan nilai a dan b\n",
        "print ('Y = bX+A')\n",
        "print ('ln(f(x))= b(ln(x))+ln(a)')\n",
        "print(\"Bentuk Persamaan Regresi Liniernya adalah: ln(f(x)) = \",A,\"+\",b,\"ln(x)\" )\n",
        "print(\"Nilai a:\", a)\n",
        "print(\"Nilai ln(a):\", A)\n",
        "print(\"Nilai b:\", b)\n",
        "print('\\n')\n",
        "\n",
        "# Mendampilkan DataFrame\n",
        "print('Tabel regresi linear dari data:')\n",
        "print(df)\n",
        "print(\"Jumlah x:\", jumx)\n",
        "print(\"Jumlah y:\", jumy)\n",
        "print(\"Jumlah lnx:\", jumX)\n",
        "print(\"Jumlah lny:\", jumY)\n",
        "print(\"Jumlah ln(x)ln(y):\", jumXY)\n",
        "print(\"Jumlah (lnx)^2:\", jumX_squared)\n",
        "print(\"Jumlah (lny)^2:\", jumY_squared)\n",
        "print('\\n')\n",
        "\n",
        "# Menampilkan nilai a, b dan S\n",
        "print('Maka, ')\n",
        "print(\"Nilai a:\", a)\n",
        "print(\"Nilai b:\", b)\n",
        "print(\"Nilai ln(a):\", A)\n",
        "print(\"Nilai S:\", S)\n",
        "print('\\n')\n",
        "\n",
        "print('Grafik plot data:')\n",
        "\n",
        "# Melakukan Plot Sebelum Linearisasi\n",
        "plt.figure(figsize=(12, 4))\n",
        "plt.subplot(131)\n",
        "plt.scatter(x, y, label=\"Data Sebelum Linearisasi\")\n",
        "plt.xlabel(\"x\")\n",
        "plt.ylabel(\"y\")\n",
        "plt.legend()\n",
        "\n",
        "# Melakukan Plot Sesudah Linearisasi\n",
        "plt.subplot(132)\n",
        "plt.scatter(X, Y, label=\"Data Sesudah Linearisasi\")\n",
        "plt.xlabel(\"ln(X)\")\n",
        "plt.ylabel(\"ln(Y)\")\n",
        "plt.legend()\n",
        "\n",
        "# Melakukan Plot Hasil Regresi Linear\n",
        "plt.subplot(133)\n",
        "plt.scatter(X, Y, label=\"Data Linearisasi\")\n",
        "x_fit = np.linspace(min(X), max(X), 100)\n",
        "y_fit = A + b * x_fit\n",
        "plt.plot(x_fit, y_fit, color=\"red\", label=\"Regresi Linier\")\n",
        "plt.xlabel(\"ln(x)\")\n",
        "plt.ylabel(\"ln(y)\")\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lAzGFKjabP7e",
        "outputId": "43053d5f-9eaf-4870-e594-d25010805008"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Persamaan Regresi Linier: ln(y(t)) = -0.0016 - (0.0086)t\n",
            "Nilai a: 0.9984157812833949\n",
            "Nilai ln(a): -0.0015854749179831755\n",
            "Nilai b: 0.008639549701453635\n",
            "\n",
            "\n",
            "Tabel regresi linear dari data:\n",
            "    No.    t      y     ln(y)   t*ln(y)    t^2  (ln(y))^2        s ke i\n",
            "0     1  0.0  1.000  0.000000  0.000000   0.00   0.000000  2.513731e-06\n",
            "1     2  0.5  0.994 -0.006018 -0.003009   0.25   0.000036  1.272893e-08\n",
            "2     3  1.0  0.990 -0.010050 -0.010050   1.00   0.000101  3.051616e-08\n",
            "3     4  1.5  0.985 -0.015114 -0.022670   2.25   0.000228  3.235771e-07\n",
            "4     5  2.0  0.979 -0.021224 -0.042447   4.00   0.000450  5.565174e-06\n",
            "5     6  2.5  0.977 -0.023269 -0.058172   6.25   0.000541  7.102742e-09\n",
            "6     7  3.0  0.972 -0.028399 -0.085198   9.00   0.000807  8.016525e-07\n",
            "7     8  3.5  0.969 -0.031491 -0.110217  12.25   0.000992  1.110434e-07\n",
            "8     9  4.0  0.967 -0.033557 -0.134227  16.00   0.001126  6.692001e-06\n",
            "9    10  4.5  0.960 -0.040822 -0.183699  20.25   0.001666  1.285552e-07\n",
            "10   11  5.0  0.956 -0.044997 -0.224987  25.00   0.002025  4.585701e-08\n",
            "11   12  5.5  0.952 -0.049190 -0.270546  30.25   0.002420  7.611850e-09\n",
            "Jumlah t: 33.0\n",
            "Jumlah y: 11.700999999999999\n",
            "Jumlah lny: -0.30413083916376804\n",
            "Jumlah tln(y): -1.1452237095273294\n",
            "Jumlah t^2: 126.5\n",
            "Jumlah (lny)^2: 0.010392648525644642\n",
            "\n",
            "\n",
            "Maka, \n",
            "Nilai a: 0.9984157812833949\n",
            "Nilai ln(a): -0.0015854749179831755\n",
            "Nilai b: 0.008639549701453635\n",
            "Nilai S: 1.623955062084946e-05\n",
            "\n",
            "\n",
            "Grafik plot data:\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "#Soal No 2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Memasukkan data\n",
        "t = np.array([0.0, 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5])\n",
        "y = np.array([1.000, 0.994, 0.990, 0.985, 0.979, 0.977, 0.972, 0.969, 0.967, 0.960, 0.956, 0.952])\n",
        "\n",
        "# Menyesuaikan data dengan persamaan regresi linier\n",
        "Y = np.log(y)\n",
        "\n",
        "# Menghitung nilai ty, y^2, dan t^2\n",
        "tY = t * Y\n",
        "t_squared = t ** 2\n",
        "Y_squared = Y ** 2\n",
        "\n",
        "# Menghitung nilai a dan b menggunakan regresi linier\n",
        "mean_t = np.mean(t)\n",
        "mean_Y = np.mean(Y)\n",
        "b = np.sum((t - mean_t) * (Y - mean_Y)) / np.sum((t - mean_t) ** 2)\n",
        "A = mean_Y - b * mean_t\n",
        "a = np.exp(A)\n",
        "nilai_s = (Y - A - b * t) ** 2\n",
        "\n",
        "# Mengatur tampilan dataframe\n",
        "data = {'No.': range(1, len(t) + 1), 't': t, 'y': y, 'ln(y)': Y, 't*ln(y)': tY, 't^2': t_squared, '(ln(y))^2': Y_squared, 's ke i': nilai_s}\n",
        "df = pd.DataFrame(data)\n",
        "\n",
        "# Menghitung jumlah nilai s\n",
        "S = np.sum(nilai_s)\n",
        "\n",
        "# Menghitung jumlah t, jumlah y, jumlah ln(y), jumlah tln(y), jumlah (ln(y))^2, dan jumlah t^2\n",
        "jumt = np.sum(t)\n",
        "jumy = np.sum(y)\n",
        "jumY = np.sum(Y)\n",
        "jumtY = np.sum(tY)\n",
        "jumt_squared = np.sum(t_squared)\n",
        "jumY_squared = np.sum(Y_squared)\n",
        "\n",
        "# Menampilkan nilai a dan b\n",
        "print('Persamaan Regresi Linier: ln(y(t)) = {:.4f} - ({:.4f})t'.format(A, -b))\n",
        "print(\"Nilai a:\", a)\n",
        "print(\"Nilai ln(a):\", A)\n",
        "print(\"Nilai b:\", -b)\n",
        "print('\\n')\n",
        "\n",
        "# Menampilkan DataFrame\n",
        "print('Tabel regresi linear dari data:')\n",
        "print(df)\n",
        "print(\"Jumlah t:\", jumt)\n",
        "print(\"Jumlah y:\", jumy)\n",
        "print(\"Jumlah lny:\", jumY)\n",
        "print(\"Jumlah tln(y):\", jumtY)\n",
        "print(\"Jumlah t^2:\", jumt_squared)\n",
        "print(\"Jumlah (lny)^2:\", jumY_squared)\n",
        "print('\\n')\n",
        "\n",
        "# Menampilkan nilai a, b, dan S\n",
        "print('Maka, ')\n",
        "print(\"Nilai a:\", a)\n",
        "print(\"Nilai ln(a):\", A)\n",
        "print(\"Nilai b:\", -b)\n",
        "print(\"Nilai S:\", S)\n",
        "print('\\n')\n",
        "\n",
        "# Plot data dan hasil regresi\n",
        "print('Grafik plot data:')\n",
        "plt.scatter(t, Y, label=\"Data\")\n",
        "plt.plot(t, A - b * t, color=\"red\", label=\"Regresi Linier\")\n",
        "plt.xlabel(\"t\")\n",
        "plt.ylabel(\"ln(y)\")\n",
        "plt.legend()\n",
        "plt.show()\n"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}