21. Inferential Statistics.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"w3d4AM-PM_Inferential Statistics.ipynb","provenance":[{"file_id":"1U8PY-YKh-fnoY2YOiu8hfwzrZUmZj495","timestamp":1654309013813},{"file_id":"https://github.com/ardhiraka/FSDS_Guidelines/blob/master/p0/w3/d3pm.ipynb","timestamp":1638321540659}],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","metadata":{"id":"FP3QbXZESMch"},"source":["# Day 3 AM - Inferential Statistics\n"]},{"cell_type":"code","metadata":{"id":"XQGzj7iMVmni"},"source":["import pandas as pd\n","from scipy import stats\n","import numpy as np\n","import matplotlib.pyplot as plt\n","import seaborn as sns\n","\n","np.random.seed(10)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"NRk3HZRVs7Ct"},"source":["## Resampling\n","\n","Resampling dalam statistik berarti berulang kali sampel nilai dari data yang diamati, dengan tujuan umum menilai variabilitas acak dalam statistik. Untuk memahami apa itu resampling, mari kita lihat harga perumahan di distribusi data (histogram) Amsterdam di bawah ini."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"wJOE-QTgtCnq","executionInfo":{"status":"ok","timestamp":1654309041271,"user_tz":-480,"elapsed":539,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"b01a745c-2af3-431b-cbfa-55a787b9c905"},"source":["#hanya sebagian nilai sampling yang dapat mewakili atau resampling\n","house_price = pd.read_csv('/HousingPrices-Amsterdam-August-2021.csv')\n","house_price\n","#terdapat informasi longitude dan latitude, tapi hanya ingin melihat harganya saja"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Unnamed: 0                                 Address      Zip     Price  \\\n","0           1            Blasiusstraat 8 2, Amsterdam  1091 CR  685000.0   \n","1           2  Kromme Leimuidenstraat 13 H, Amsterdam  1059 EL  475000.0   \n","2           3              Zaaiersweg 11 A, Amsterdam  1097 SM  850000.0   \n","3           4            Tenerifestraat 40, Amsterdam  1060 TH  580000.0   \n","4           5              Winterjanpad 21, Amsterdam  1036 KN  720000.0   \n","\n","   Area  Room       Lon        Lat  \n","0    64     3  4.907736  52.356157  \n","1    60     3  4.850476  52.348586  \n","2   109     4  4.944774  52.343782  \n","3   128     6  4.789928  52.343712  \n","4   138     5  4.902503  52.410538  "],"text/html":["\n","  <div id=\"df-33248978-6e4a-443d-9a1b-1fcac0c2dc65\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  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   <td>850000.0</td>\n","      <td>109</td>\n","      <td>4</td>\n","      <td>4.944774</td>\n","      <td>52.343782</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>Tenerifestraat 40, Amsterdam</td>\n","      <td>1060 TH</td>\n","      <td>580000.0</td>\n","      <td>128</td>\n","      <td>6</td>\n","      <td>4.789928</td>\n","      <td>52.343712</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>Winterjanpad 21, Amsterdam</td>\n","      <td>1036 KN</td>\n","      <td>720000.0</td>\n","      <td>138</td>\n","      <td>5</td>\n","      <td>4.902503</td>\n","      <td>52.410538</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-33248978-6e4a-443d-9a1b-1fcac0c2dc65')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg 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harga rumah\n","#membuat histogram pada tabel house_price koom price, line atau garis probability, label nya penjualan d amsterdam\n","sns.displot(house_price.Price, kde=True, label='Housing Price in Amsterdam')"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<seaborn.axisgrid.FacetGrid at 0x7f914a5656d0>"]},"metadata":{},"execution_count":6},{"output_type":"display_data","data":{"text/plain":["<Figure size 360x360 with 1 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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":["house_price.shape #Ukuran datanya sejumlah 924"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tKysmX2sguB5","executionInfo":{"status":"ok","timestamp":1654309188623,"user_tz":-480,"elapsed":459,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"6a7d2816-64e1-4be6-dae3-c37d6363db23"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(924, 8)"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":294},"id":"mCeCuOk7tKtW","executionInfo":{"status":"ok","timestamp":1654309226616,"user_tz":-480,"elapsed":705,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"1b85819e-99fa-45b7-c78b-5d243bb6ef00"},"source":["#Random sample\n","#Datanya 900 lebih tapi hanya menampilkan random sampel sebanyak 400 dengan pengembalian\n","#Melihat histogram dengan pengambilan sampling menggunakan simpel random sample berukuran 400\n","fig,ax = plt.subplots(ncols=4, figsize = (18,4))\n","for i in range(4): #disampel sebanyak 4 kali\n","  house_price.Price.sample(400).plot(kind='hist',bins=20,ax=ax[i]) #400 nilai resampling yang kita ambil \n","  ax[i].set_title('Sample #{}'.format(i+1))\n","#walaupun ada perbedaan tetapi masih mirip"],"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1296x288 with 4 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"Yzv2OIW0tNb9"},"source":["Kami mengambil 400 sampel secara acak dari data harga rumah dan kami melakukannya sebanyak 4 kali. Visualisasi menunjukkan bahwa distribusi mereka terlihat sama. Itu terjadi hanya karena keacakan."]},{"cell_type":"markdown","metadata":{"id":"ZivUf859s-AX"},"source":["## Implementation to Marketing Campaign Analysis"]},{"cell_type":"markdown","metadata":{"id":"f6F7beDYtBNu"},"source":["Dalam pelajaran ini, kita akan belajar tentang pengujian hipotesis dan bagaimana menerapkannya pada kasus bisnis sehari-hari seperti ritel online. Kami akan menggunakan data dari dataset ML UCI https://archive.ics.uci.edu/ml/datasets/online+retail."]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"i1bTsOU8VMCi","executionInfo":{"status":"ok","timestamp":1654309283154,"user_tz":-480,"elapsed":3243,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"fa6ab84b-fc5e-456d-8ebc-96d57ea30377"},"source":["data = pd.read_csv('/online_retail_data.csv').drop(columns='Unnamed: 0').sort_values('InvoiceDate',ascending=True).reset_index(drop=True)\n","data"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     InvoiceNo StockCode                        Description  Quantity  \\\n","0       536369     21756           BATH BUILDING BLOCK WORD         3   \n","1       536373     82482  WOODEN PICTURE FRAME WHITE FINISH         6   \n","2       536398     22752       SET 7 BABUSHKA NESTING BOXES         6   \n","3       536401     85150      LADIES & GENTLEMEN METAL SIGN         1   \n","4       536404     20727            LUNCH BAG  BLACK SKULL.        10   \n","...        ...       ...                                ...       ...   \n","9995    581501     22942        CHRISTMAS LIGHTS 10 SANTAS         12   \n","9996    581572     22627                MINT KITCHEN SCALES         4   \n","9997    581578     21507            ELEPHANT BIRTHDAY CARD         12   \n","9998    581579     23353     6 GIFT TAGS VINTAGE CHRISTMAS         12   \n","9999    581580    85049E          SCANDINAVIAN REDS RIBBONS         2   \n","\n","             InvoiceDate  UnitPrice  CustomerID         Country  \n","0    2010-12-01 08:35:00       5.95     13047.0  United Kingdom  \n","1    2010-12-01 09:02:00       2.10     17850.0  United Kingdom  \n","2    2010-12-01 10:52:00       8.50     13448.0  United Kingdom  \n","3    2010-12-01 11:21:00       2.55     15862.0  United Kingdom  \n","4    2010-12-01 11:29:00       1.65     16218.0  United Kingdom  \n","...                  ...        ...         ...             ...  \n","9995 2011-12-09 10:46:00       2.95     12985.0  United Kingdom  \n","9996 2011-12-09 12:08:00       8.50     16705.0  United Kingdom  \n","9997 2011-12-09 12:16:00       0.42     12713.0         Germany  \n","9998 2011-12-09 12:19:00       0.83     17581.0  United Kingdom  \n","9999 2011-12-09 12:20:00       1.25     12748.0  United Kingdom  \n","\n","[10000 rows x 8 columns]"],"text/html":["\n","  <div id=\"df-56c27b55-cfa3-476c-bad9-b1de37e234e9\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>InvoiceNo</th>\n","      <th>StockCode</th>\n","      <th>Description</th>\n","      <th>Quantity</th>\n","      <th>InvoiceDate</th>\n","      <th>UnitPrice</th>\n","      <th>CustomerID</th>\n","      <th>Country</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>536369</td>\n","      <td>21756</td>\n","      <td>BATH BUILDING BLOCK WORD</td>\n","      <td>3</td>\n","      <td>2010-12-01 08:35:00</td>\n","      <td>5.95</td>\n","      <td>13047.0</td>\n","      <td>United Kingdom</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>536373</td>\n","      <td>82482</td>\n","      <td>WOODEN PICTURE FRAME WHITE FINISH</td>\n","      <td>6</td>\n","      <td>2010-12-01 09:02:00</td>\n","      <td>2.10</td>\n","      <td>17850.0</td>\n","      <td>United Kingdom</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>536398</td>\n","      <td>22752</td>\n","      <td>SET 7 BABUSHKA NESTING BOXES</td>\n","      <td>6</td>\n","      <td>2010-12-01 10:52:00</td>\n","      <td>8.50</td>\n","      <td>13448.0</td>\n","      <td>United Kingdom</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>536401</td>\n","      <td>85150</td>\n","      <td>LADIES &amp; GENTLEMEN METAL SIGN</td>\n","      <td>1</td>\n","      <td>2010-12-01 11:21:00</td>\n","      <td>2.55</td>\n","      <td>15862.0</td>\n","      <td>United Kingdom</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>536404</td>\n","      <td>20727</td>\n","      <td>LUNCH BAG  BLACK SKULL.</td>\n","      <td>10</td>\n","      <td>2010-12-01 11:29:00</td>\n","      <td>1.65</td>\n","      <td>16218.0</td>\n","      <td>United Kingdom</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>9995</th>\n","      <td>581501</td>\n","      <td>22942</td>\n","      <td>CHRISTMAS LIGHTS 10 SANTAS</td>\n","      <td>12</td>\n","      <td>2011-12-09 10:46:00</td>\n","      <td>2.95</td>\n","      <td>12985.0</td>\n","      <td>United Kingdom</td>\n","    </tr>\n","    <tr>\n","      <th>9996</th>\n","      <td>581572</td>\n","      <td>22627</td>\n","      <td>MINT KITCHEN SCALES</td>\n","      <td>4</td>\n","      <td>2011-12-09 12:08:00</td>\n","      <td>8.50</td>\n","      <td>16705.0</td>\n","      <td>United Kingdom</td>\n","    </tr>\n","    <tr>\n","      <th>9997</th>\n","      <td>581578</td>\n","      <td>21507</td>\n","      <td>ELEPHANT BIRTHDAY CARD</td>\n","      <td>12</td>\n","      <td>2011-12-09 12:16:00</td>\n","      <td>0.42</td>\n","      <td>12713.0</td>\n","      <td>Germany</td>\n","    </tr>\n","    <tr>\n","      <th>9998</th>\n","      <td>581579</td>\n","      <td>23353</td>\n","      <td>6 GIFT TAGS VINTAGE CHRISTMAS</td>\n","      <td>12</td>\n","      <td>2011-12-09 12:19:00</td>\n","      <td>0.83</td>\n","      <td>17581.0</td>\n","      <td>United Kingdom</td>\n","    </tr>\n","    <tr>\n","      <th>9999</th>\n","      <td>581580</td>\n","      <td>85049E</td>\n","      <td>SCANDINAVIAN REDS RIBBONS</td>\n","      <td>2</td>\n","      <td>2011-12-09 12:20:00</td>\n","      <td>1.25</td>\n","      <td>12748.0</td>\n","      <td>United Kingdom</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>10000 rows × 8 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56c27b55-cfa3-476c-bad9-b1de37e234e9')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-56c27b55-cfa3-476c-bad9-b1de37e234e9 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Misalkan selama lima tahun terakhir, pendapatan harian kita rata-rata \\$500 dan selama setahun terakhir, kita mencapai rata-rata \\$580 sehari. Apakah itu berarti bahwa kita meningkat secara signifikan?"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"RuQM2P3FOV5y","executionInfo":{"status":"ok","timestamp":1654309592393,"user_tz":-480,"elapsed":439,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"28db407a-341c-4548-da8f-44a3d3cf5f1f"},"source":["data['income'] = data['Quantity']*data['UnitPrice'] #variabel data income berisikan data quantity control pada barang unitprice\n","data['date'] = data['InvoiceDate'].dt.date #variabel data berisikan tahun dilakukannya produksi\n","daily_income = data[['date','income']].groupby('date').sum() #melihat daily_income dari dari data jumlah quantity control pada waktu \n","print('Average Income a Day for the last a year: ${}'.format(np.round(daily_income['income'].mean()))) #melihat rata-rata"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Average Income a Day for the last a year: $580.0\n"]}]},{"cell_type":"markdown","metadata":{"id":"d2X_YkmmTH-a"},"source":["Untuk memeriksa apakah penjualan kami meningkat secara signifikan atau tidak, kami akan melakukan sampel tunggal satu sisi dan menetapkan tingkat signifikansi 0,05. Kami menggunakan metode ini karena kami hanya menguji variabel dan membandingkan sampel (data satu tahun terakhir) dan populasi (kami menganggap itu adalah data lima tahun terakhir).\n","\n","Our hypothesis on this case:\n","\n","**H0: μ <= \\$500**\n","\n","**H1: μ > \\$500**"]},{"cell_type":"code","metadata":{"id":"s5vOqF2sfxLI","colab":{"base_uri":"https://localhost:8080/","height":455},"executionInfo":{"status":"ok","timestamp":1654309696839,"user_tz":-480,"elapsed":495,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"4575baba-a94c-4d07-f718-66fd12c34f0e"},"source":["#variabel daily_income \n","daily_income"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["             income\n","date               \n","2010-12-01  1000.20\n","2010-12-02   846.13\n","2010-12-03   924.86\n","2010-12-05   592.83\n","2010-12-06  1853.98\n","...             ...\n","2011-12-05  1574.18\n","2011-12-06  1006.74\n","2011-12-07   467.56\n","2011-12-08  2969.21\n","2011-12-09   265.97\n","\n","[305 rows x 1 columns]"],"text/html":["\n","  <div id=\"df-83cb2989-cb5c-4a75-b59a-54067f1bf1e1\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>income</th>\n","    </tr>\n","    <tr>\n","      <th>date</th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>2010-12-01</th>\n","      <td>1000.20</td>\n","    </tr>\n","    <tr>\n","      <th>2010-12-02</th>\n","      <td>846.13</td>\n","    </tr>\n","    <tr>\n","      <th>2010-12-03</th>\n","      <td>924.86</td>\n","    </tr>\n","    <tr>\n","      <th>2010-12-05</th>\n","      <td>592.83</td>\n","    </tr>\n","    <tr>\n","      <th>2010-12-06</th>\n","      <td>1853.98</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>2011-12-05</th>\n","      <td>1574.18</td>\n","    </tr>\n","    <tr>\n","      <th>2011-12-06</th>\n","      <td>1006.74</td>\n","    </tr>\n","    <tr>\n","      <th>2011-12-07</th>\n","      <td>467.56</td>\n","    </tr>\n","    <tr>\n","      <th>2011-12-08</th>\n","      <td>2969.21</td>\n","    </tr>\n","    <tr>\n","      <th>2011-12-09</th>\n","      <td>265.97</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>305 rows × 1 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-83cb2989-cb5c-4a75-b59a-54067f1bf1e1')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-83cb2989-cb5c-4a75-b59a-54067f1bf1e1 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n"]},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7f9148c03bd0>"]},"metadata":{},"execution_count":19},{"output_type":"display_data","data":{"text/plain":["<Figure size 1152x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"oQ3jU1B8VOTv"},"source":["**Karena nilai p kami kurang dari `0,05`, jadi kami menolak hipotesis nol** dan kami dapat menyimpulkan bahwa, penjualan kami selama setahun terakhir meningkat daripada lima tahun terakhir.\n","\n","*Catatan: Variabel `t-statistics` mengacu pada seberapa jauh hipotesis alternatif dari hipotesis nol.*"]},{"cell_type":"markdown","metadata":{"id":"oTTOrZxQlZQ0"},"source":["### One Sample Two Tailed"]},{"cell_type":"markdown","metadata":{"id":"RGOXpJ-3lc4s"},"source":["Our hypothesis on this case:\n","\n","**H0: μ =\\$500**\n","\n","**H1: μ != \\$500**"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ww0K2MIJllR8","executionInfo":{"status":"ok","timestamp":1654311686664,"user_tz":-480,"elapsed":484,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"aa2349eb-a8d8-4ee9-9ec1-fee6f5eb5247"},"source":["t_stat,p_val = stats.ttest_1samp(daily_income.income, 500)\n","print('P-value:',p_val) #Tetap akan menolak h0 karena 0,005 < 0.05\n","print('t-statistics:',t_stat)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["P-value: 0.005749098120238477\n","t-statistics: 2.781482852334568\n"]}]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":392},"id":"T7PgMzahl3C0","executionInfo":{"status":"ok","timestamp":1654311783240,"user_tz":-480,"elapsed":1240,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"a75a5fab-af79-4988-fcd8-e532cbf280e3"},"source":["daily_income_pop = np.random.normal(daily_income.income.mean(), daily_income.income.std(), 10000)\n","\n","ci = stats.norm.interval(0.95, daily_income.income.mean(), daily_income.income.std()) #tingkat kepercayaannya\n","\n","plt.figure(figsize=(16,5))\n","sns.distplot(daily_income_pop, label='Daily Income (Populasi)', color='blue')\n","plt.axvline(daily_income.income.mean(), color='red', linewidth=2, label='Daily Income (Rata-rata)')\n","\n","plt.axvline(ci[1], color='green', linestyle='dashed', linewidth=2, label='confidence threshold of 95%/Tingkat Kepercayaan 0,05')\n","plt.axvline(ci[0], color='green', linestyle='dashed', linewidth=2, label='confidence threshold of 95%/Tingkat Kepercayaan 0,05')\n","\n","plt.axvline(daily_income_pop.mean() + t_stat*daily_income_pop.std(), color='black', linestyle='dashed', linewidth=2, label = 'Alternative Hypothesis')\n","plt.axvline(daily_income_pop.mean() - t_stat*daily_income_pop.std(), color='black', linestyle='dashed', linewidth=2)\n","plt.legend()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n"]},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7f91489c5750>"]},"metadata":{},"execution_count":22},{"output_type":"display_data","data":{"text/plain":["<Figure size 1152x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"TDk6rMRZbFQf"},"source":["### Dua Sampel Uji Hipotesis Dua Sisi Independen\n","\n","Sekarang, kami ingin memeriksa, apakah rata-rata harian penjualan dua negara berbeda secara signifikan atau tidak menggunakan dua sampel uji dua arah yang independen. Kami akan memilih sampel Perancis dan Jerman.\n","\n","seharusnya berbeda karena masing-masing negara memiliki pasarnya sendiri. Pertanyaannya apakah pasar prancis dan jerman itu signifikan?"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XdEVSvqubSfk","executionInfo":{"status":"ok","timestamp":1654312863251,"user_tz":-480,"elapsed":466,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"671172f0-6dfa-40d8-ef9a-93eeaea17272"},"source":["daily_france = data[data['Country']=='France'][['date','income']].groupby('date').sum()\n","daily_germany = data[data['Country']=='Germany'][['date','income']].groupby('date').sum()\n","\n","print('Average sales of France a day: ${}'.format(np.round(daily_france.income.mean())))\n","print('Average sales of Germany a day: ${}'.format(np.round(daily_germany.income.mean())))"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Average sales of France a day: $32.0\n","Average sales of Germany a day: $37.0\n"]}]},{"cell_type":"markdown","source":["secara rata-rata memiliki selisih 5 dolar. pertanyaanya apakah 5 dolar sebuah kebetulan? atau signifikan?"],"metadata":{"id":"7OjsjOFDu3JD"}},{"cell_type":"markdown","metadata":{"id":"1Fijn7LgquIr"},"source":["Our hypothesis on this case:\n","\n","**H0: μ_france = μ_germany** (apakah rata-rata prancis sama dengan jerman)\n","\n","**H1: μ_france != μ_germany** (apakah rata-rata prancis tidak sama dengan jerman)"]},{"cell_type":"code","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9prVBBcZoy4o","executionInfo":{"status":"ok","timestamp":1654313103832,"user_tz":-480,"elapsed":467,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"a8d4c4fe-2060-4b39-a3c1-aa45587a4b7c"},"source":["#Kalau independent pakai stats.ttest_ind\n","t_stat, p_val = stats.ttest_ind(daily_france,daily_germany)\n","print('P-value:',p_val[0]) #the p-value isn't divided by 2 since the output is two-sided p-value\n","print('t-statistics:',t_stat[0])"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["P-value: 0.42798052232930384\n","t-statistics: -0.7943449043302956\n"]}]},{"cell_type":"markdown","source":["hasilnya 0.4 > 0,05 maka gagal menolak ho. secara visual nilai p-vaue berada di dalam garis hitam dan tidak berada confidence thershols of 95%."],"metadata":{"id":"L5jZRbufvxF5"}},{"cell_type":"code","metadata":{"id":"dU4Uhgqv6OV3","colab":{"base_uri":"https://localhost:8080/","height":427},"executionInfo":{"status":"ok","timestamp":1654313455580,"user_tz":-480,"elapsed":1687,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"4f48ec4c-6c25-4aea-e05b-5147cdd86a91"},"source":["france_pop = np.random.normal(daily_france.income.mean(),daily_france.income.std(),10000)\n","germany_pop = np.random.normal(daily_germany.income.mean(),daily_germany.income.std(),10000)\n","\n","ci = stats.norm.interval(0.95, daily_france.income.mean(), daily_france.income.std())\n","plt.figure(figsize=(16,5))\n","sns.distplot(france_pop, label='Penjualan Rata-Rata Prancis Sehari *Pop',color='blue')\n","sns.distplot(germany_pop, label='Penjualan Rata-Rata Jerman Sehari *Pop',color='red')\n","\n","plt.axvline(daily_france.income.mean(), color='blue', linewidth=2, label='Prancis rata-rata')\n","plt.axvline(daily_germany.income.mean(), color='red',  linewidth=2, label='Jerman rata-rata')\n","\n","plt.axvline(ci[1], color='green', linestyle='dashed', linewidth=2, label='confidence threshold of 95%/Tingkat Kepercayaan 0,05')\n","plt.axvline(ci[0], color='green', linestyle='dashed', linewidth=2)\n","\n","plt.axvline(france_pop.mean()+t_stat[0]*france_pop.std(), color='black', linestyle='dashed', linewidth=2, label = 'Alternative Hypothesis')\n","plt.axvline(france_pop.mean()-t_stat[0]*france_pop.std(), color='black', linestyle='dashed', linewidth=2)\n","\n","plt.legend()"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n","/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n","  warnings.warn(msg, FutureWarning)\n"]},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.legend.Legend at 0x7f91485a87d0>"]},"metadata":{},"execution_count":26},{"output_type":"display_data","data":{"text/plain":["<Figure size 1152x360 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","metadata":{"id":"K5ljS7cK6FYx"},"source":["Berdasarkan hasil di atas, kita dapat menyimpulkan bahwa **kita gagal menolak hipotesis nol** yang antara Perancis dan Jerman tidak berbeda secara signifikan dalam hal penjualan rata-rata per hari."]},{"cell_type":"markdown","metadata":{"id":"wzcuVPo0tmf_"},"source":["## ANOVA\n","\n","ANOVA mirip dengan uji-t. Digunakan untuk menguji apakah lebih dari dua variabel berbeda nyata atau tidak. Jadi, kita akan menguji apakah rata-rata penjualan harian IEV,N8U, dan U5F berbeda nyata atau tidak."]},{"cell_type":"code","metadata":{"id":"ZI2edvUmtnGn","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1654315124269,"user_tz":-480,"elapsed":1505,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"73519a72-8077-4917-ce8f-ec70b7890e22"},"source":["scanner_data = pd.read_csv('scanner_data.csv')\n","scanner_data"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        Unnamed: 0        Date  Customer_ID  Transaction_ID SKU_Category  \\\n","0                1  02/01/2016         2547               1          X52   \n","1                2  02/01/2016          822               2          2ML   \n","2                3  02/01/2016         3686               3          0H2   \n","3                4  02/01/2016         3719               4          0H2   \n","4                5  02/01/2016         9200               5          0H2   \n","...            ...         ...          ...             ...          ...   \n","131701      131702  04/07/2016        20203           32900          IEV   \n","131702      131703  04/07/2016        20203           32900          N8U   \n","131703      131704  04/07/2016        20203           32900          U5F   \n","131704      131705  04/07/2016        20203           32900          0H2   \n","131705      131706  04/07/2016        20203           32900          Q4N   \n","\n","          SKU  Quantity  Sales_Amount  \n","0       0EM7L       1.0          3.13  \n","1       68BRQ       1.0          5.46  \n","2       CZUZX       1.0          6.35  \n","3       549KK       1.0          5.59  \n","4       K8EHH       1.0          6.88  \n","...       ...       ...           ...  \n","131701  FO112       3.0          6.46  \n","131702  I36F2       1.0          4.50  \n","131703  4X8P4       1.0          5.19  \n","131704  ZVTO4       1.0          4.57  \n","131705  QM9BP       1.0         13.68  \n","\n","[131706 rows x 8 columns]"],"text/html":["\n","  <div id=\"df-419ca7df-0d47-415d-9d90-515f2f641979\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>Date</th>\n","      <th>Customer_ID</th>\n","      <th>Transaction_ID</th>\n","      <th>SKU_Category</th>\n","      <th>SKU</th>\n","      <th>Quantity</th>\n","      <th>Sales_Amount</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>1</td>\n","      <td>02/01/2016</td>\n","      <td>2547</td>\n","      <td>1</td>\n","      <td>X52</td>\n","      <td>0EM7L</td>\n","      <td>1.0</td>\n","      <td>3.13</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>2</td>\n","      <td>02/01/2016</td>\n","      <td>822</td>\n","      <td>2</td>\n","      <td>2ML</td>\n","      <td>68BRQ</td>\n","      <td>1.0</td>\n","      <td>5.46</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>3</td>\n","      <td>02/01/2016</td>\n","      <td>3686</td>\n","      <td>3</td>\n","      <td>0H2</td>\n","      <td>CZUZX</td>\n","      <td>1.0</td>\n","      <td>6.35</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>4</td>\n","      <td>02/01/2016</td>\n","      <td>3719</td>\n","      <td>4</td>\n","      <td>0H2</td>\n","      <td>549KK</td>\n","      <td>1.0</td>\n","      <td>5.59</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>5</td>\n","      <td>02/01/2016</td>\n","      <td>9200</td>\n","      <td>5</td>\n","      <td>0H2</td>\n","      <td>K8EHH</td>\n","      <td>1.0</td>\n","      <td>6.88</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>131701</th>\n","      <td>131702</td>\n","      <td>04/07/2016</td>\n","      <td>20203</td>\n","      <td>32900</td>\n","      <td>IEV</td>\n","      <td>FO112</td>\n","      <td>3.0</td>\n","      <td>6.46</td>\n","    </tr>\n","    <tr>\n","      <th>131702</th>\n","      <td>131703</td>\n","      <td>04/07/2016</td>\n","      <td>20203</td>\n","      <td>32900</td>\n","      <td>N8U</td>\n","      <td>I36F2</td>\n","      <td>1.0</td>\n","      <td>4.50</td>\n","    </tr>\n","    <tr>\n","      <th>131703</th>\n","      <td>131704</td>\n","      <td>04/07/2016</td>\n","      <td>20203</td>\n","      <td>32900</td>\n","      <td>U5F</td>\n","      <td>4X8P4</td>\n","      <td>1.0</td>\n","      <td>5.19</td>\n","    </tr>\n","    <tr>\n","      <th>131704</th>\n","      <td>131705</td>\n","      <td>04/07/2016</td>\n","      <td>20203</td>\n","      <td>32900</td>\n","      <td>0H2</td>\n","      <td>ZVTO4</td>\n","      <td>1.0</td>\n","      <td>4.57</td>\n","    </tr>\n","    <tr>\n","      <th>131705</th>\n","      <td>131706</td>\n","      <td>04/07/2016</td>\n","      <td>20203</td>\n","      <td>32900</td>\n","      <td>Q4N</td>\n","      <td>QM9BP</td>\n","      <td>1.0</td>\n","      <td>13.68</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>131706 rows × 8 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-419ca7df-0d47-415d-9d90-515f2f641979')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg 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  169.00       1341.96\n","31/08/2016    24788181      4674456        13660542    477.67       3955.78\n","31/10/2016    38037247      6012329        19834981    678.33       4691.38\n","31/12/2016    24216722      2843852        12215471    216.00       1713.20\n","\n","[363 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-925ab9b4-1602-4d2e-a890-8f1202ebab07\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Unnamed: 0</th>\n","      <th>Customer_ID</th>\n","      <th>Transaction_ID</th>\n","      <th>Quantity</th>\n","      <th>Sales_Amount</th>\n","    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Benarkah kita mengupgrade game kita dari versi gate 30 ke gate 40? (Data dari https://www.kaggle.com/yufengsui/mobile-games-ab-testing) Kami akan menggunakan data retention_1 yang menangkap informasi bahwa pemain kembali dan bermain 1 hari setelah menginstal."]},{"cell_type":"code","metadata":{"id":"wAr21OQ18eRT","colab":{"base_uri":"https://localhost:8080/","height":424},"executionInfo":{"status":"ok","timestamp":1654313778596,"user_tz":-480,"elapsed":454,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"32634f9e-7195-471b-8515-6d246e47fe95"},"source":["#Melihat perbedaan game yang gate 30 dengan gate 40\n","cookie_cats = pd.read_csv('/cookie_cats.csv')\n","cookie_cats"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["        userid  version  sum_gamerounds  retention_1  retention_7\n","0          116  gate_30               3        False        False\n","1          337  gate_30              38         True        False\n","2          377  gate_40             165         True        False\n","3          483  gate_40               1        False        False\n","4          488  gate_40             179         True         True\n","...        ...      ...             ...          ...          ...\n","90184  9999441  gate_40              97         True        False\n","90185  9999479  gate_40              30        False        False\n","90186  9999710  gate_30              28         True        False\n","90187  9999768  gate_40              51         True        False\n","90188  9999861  gate_40              16        False        False\n","\n","[90189 rows x 5 columns]"],"text/html":["\n","  <div id=\"df-cc3b01ec-2a59-4e8c-a742-24cbd526df2b\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>userid</th>\n","      <th>version</th>\n","      <th>sum_gamerounds</th>\n","      <th>retention_1</th>\n","      <th>retention_7</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>116</td>\n","      <td>gate_30</td>\n","      <td>3</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>337</td>\n","      <td>gate_30</td>\n","      <td>38</td>\n","      <td>True</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>377</td>\n","      <td>gate_40</td>\n","      <td>165</td>\n","      <td>True</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>483</td>\n","      <td>gate_40</td>\n","      <td>1</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>488</td>\n","      <td>gate_40</td>\n","      <td>179</td>\n","      <td>True</td>\n","      <td>True</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>90184</th>\n","      <td>9999441</td>\n","      <td>gate_40</td>\n","      <td>97</td>\n","      <td>True</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>90185</th>\n","      <td>9999479</td>\n","      <td>gate_40</td>\n","      <td>30</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>90186</th>\n","      <td>9999710</td>\n","      <td>gate_30</td>\n","      <td>28</td>\n","      <td>True</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>90187</th>\n","      <td>9999768</td>\n","      <td>gate_40</td>\n","      <td>51</td>\n","      <td>True</td>\n","      <td>False</td>\n","    </tr>\n","    <tr>\n","      <th>90188</th>\n","      <td>9999861</td>\n","      <td>gate_40</td>\n","      <td>16</td>\n","      <td>False</td>\n","      <td>False</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>90189 rows × 5 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cc3b01ec-2a59-4e8c-a742-24cbd526df2b')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":28}]},{"cell_type":"code","metadata":{"id":"9eGqWn9JXlNs","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654313798869,"user_tz":-480,"elapsed":459,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"fbe23641-62ca-4b16-f844-7a06a8b63329"},"source":["gate_30 = cookie_cats[cookie_cats['version']=='gate_30']['retention_1'].replace({True:1,False:0})#retention yang true di ganti jadi satu dan false jadi 0\n","gate_40 = cookie_cats[cookie_cats['version']=='gate_40']['retention_1'].replace({True:1,False:0})\n","\n","#rata-rata masing-masing mean\n","print('Retention rate of gate 30 version:',gate_30.sum()/gate_30.count())\n","print('Retention rate of gate 40 version:',gate_40.sum()/gate_40.count())"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Retention rate of gate 30 version: 0.4481879194630872\n","Retention rate of gate 40 version: 0.44228274967574577\n"]}]},{"cell_type":"code","metadata":{"id":"R9VBzIbWsEjx","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654314082584,"user_tz":-480,"elapsed":472,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"ca2ca65b-c30f-417c-b899-d4d56bb95e3e"},"source":["gate_30"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0        0\n","1        1\n","6        0\n","11       0\n","13       1\n","        ..\n","90179    1\n","90180    0\n","90182    1\n","90183    0\n","90186    1\n","Name: retention_1, Length: 44700, dtype: int64"]},"metadata":{},"execution_count":30}]},{"cell_type":"code","metadata":{"id":"vC6YVVXRsF_J","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654314086373,"user_tz":-480,"elapsed":440,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"a048e934-f33a-47be-c6e0-72a60d5cd131"},"source":["gate_40"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["2        1\n","3        0\n","4        1\n","5        1\n","7        0\n","        ..\n","90181    1\n","90184    1\n","90185    0\n","90187    1\n","90188    0\n","Name: retention_1, Length: 45489, dtype: int64"]},"metadata":{},"execution_count":31}]},{"cell_type":"markdown","metadata":{"id":"QARuvr2IYpQ1"},"source":["Tingkat retensi antara versi tersebut sedikit berbeda. Apakah perbedaannya benar-benar sama atau karena kebetulan?\n","\n","Untuk memastikan hasil kami, kami akan memeriksa menggunakan uji berpasangan dua sisi.\n","\n","Hipotesis kami tentang kasus ini:\n","\n","**H0: μ_gate30 = μ_gate40**\n","\n","**H1: μ_gate30 != μ_gate40**"]},{"cell_type":"code","source":["print('Retention rate of gate 30 version:',gate_30.sum())\n","print('Retention rate of gate 40 version:',gate_40.sum())\n","#retention rate atau jumlah barisnya jika di jumlahkan bernilai 40000, makanya pakai 40000 sampel yang paling optimal"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ax5JOdcu0I92","executionInfo":{"status":"ok","timestamp":1654314355199,"user_tz":-480,"elapsed":438,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"e0e993c1-989c-4fb3-dd0d-afeaa1341f23"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Retention rate of gate 30 version: 20034\n","Retention rate of gate 40 version: 20119\n"]}]},{"cell_type":"code","metadata":{"id":"n0kHhWsbYoWr","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654314191860,"user_tz":-480,"elapsed":492,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"f66dcdb7-dcb9-422b-8072-fac186c03916"},"source":["t_stat,p_val = stats.ttest_rel(gate_30.sample(40000),gate_40.sample(40000)) #kami mengambil 40000 sampel dari setiap data untuk membuat ukurannya sama\n","print('P-value:',p_val)"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["P-value: 0.07982534732793503\n"]}]},{"cell_type":"markdown","metadata":{"id":"zOg7PKVPZ391"},"source":["**Karena nilai p lebih dari 0,05, kami menyimpulkan bahwa kami gagal menolak Hipotesis Null** yang berarti antara gerbang 30 dan versi 40 adalah sama. Kami dengan aman meningkatkan game kami ke versi gerbang 40. Tidak ada perubahan signifikan antara gate 3 ke gate 4."]},{"cell_type":"markdown","metadata":{"id":"N415uzlNt7lx"},"source":["## Chi-Square Test\n","\n","Uji chi-kuadrat digunakan untuk menguji independensi antara dua data kategorikal. Karena statistik menangani data numerik, kita perlu menghitung frekuensi setiap variabel dan disajikan dengan tabel kontingensi."]},{"cell_type":"markdown","source":["H0 : gender dan kesukaan belanja tidak ada hubungan\n","\n","H1 : gender dan kesukaan belanja saling berhubungan"],"metadata":{"id":"R8okY3US6ztu"}},{"cell_type":"code","metadata":{"id":"N2GfoVktt7-H","colab":{"base_uri":"https://localhost:8080/","height":332},"executionInfo":{"status":"ok","timestamp":1654315606904,"user_tz":-480,"elapsed":424,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"32707a7d-3e40-424d-9400-49df2e7370d3"},"source":["chi_df=pd.read_csv('chi-test.csv')\n","chi_df"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["   Gender Like Shopping?\n","0    Male             No\n","1  Female            Yes\n","2    Male            Yes\n","3  Female            Yes\n","4  Female            Yes\n","5    Male            Yes\n","6    Male             No\n","7  Female             No\n","8  Female             No"],"text/html":["\n","  <div id=\"df-50296ac1-af35-4557-8632-2ec964345b4e\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>Gender</th>\n","      <th>Like Shopping?</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Male</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>Female</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Male</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>Female</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>Female</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>Male</td>\n","      <td>Yes</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>Male</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>Female</td>\n","      <td>No</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>Female</td>\n","      <td>No</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-50296ac1-af35-4557-8632-2ec964345b4e')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-50296ac1-af35-4557-8632-2ec964345b4e button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-50296ac1-af35-4557-8632-2ec964345b4e');\n","          const dataTable =\n","            await google.colab.kernel.invokeFunction('convertToInteractive',\n","                                                     [key], {});\n","          if (!dataTable) return;\n","\n","          const docLinkHtml = 'Like what you see? Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":39}]},{"cell_type":"code","metadata":{"id":"x1wyClNHt95l","colab":{"base_uri":"https://localhost:8080/","height":143},"executionInfo":{"status":"ok","timestamp":1654316124723,"user_tz":-480,"elapsed":502,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"23f67aed-bfb4-4cd0-9a95-3f86a9347b82"},"source":["#untuk meihat apakah gender ity mempengaruhi tingkat kesukaan dalam berbelanja\n","contingency_table=pd.crosstab(chi_df[\"Gender\"],chi_df[\"Like Shopping?\"]) #memakai fungsi crosstab pada pandas\n","contingency_table"],"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["Like Shopping?  No  Yes\n","Gender                 \n","Female           2    3\n","Male             2    2"],"text/html":["\n","  <div id=\"df-e3be1465-6286-4fcf-8a34-63611808aae9\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th>Like Shopping?</th>\n","      <th>No</th>\n","      <th>Yes</th>\n","    </tr>\n","    <tr>\n","      <th>Gender</th>\n","      <th></th>\n","      <th></th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>Female</th>\n","      <td>2</td>\n","      <td>3</td>\n","    </tr>\n","    <tr>\n","      <th>Male</th>\n","      <td>2</td>\n","      <td>2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e3be1465-6286-4fcf-8a34-63611808aae9')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      flex-wrap:wrap;\n","      gap: 12px;\n","    }\n","\n","    .colab-df-convert {\n","      background-color: #E8F0FE;\n","      border: none;\n","      border-radius: 50%;\n","      cursor: pointer;\n","      display: none;\n","      fill: #1967D2;\n","      height: 32px;\n","      padding: 0 0 0 0;\n","      width: 32px;\n","    }\n","\n","    .colab-df-convert:hover {\n","      background-color: #E2EBFA;\n","      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n","      fill: #174EA6;\n","    }\n","\n","    [theme=dark] .colab-df-convert {\n","      background-color: #3B4455;\n","      fill: #D2E3FC;\n","    }\n","\n","    [theme=dark] .colab-df-convert:hover {\n","      background-color: #434B5C;\n","      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n","      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n","      fill: #FFFFFF;\n","    }\n","  </style>\n","\n","      <script>\n","        const buttonEl =\n","          document.querySelector('#df-e3be1465-6286-4fcf-8a34-63611808aae9 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":41}]},{"cell_type":"code","metadata":{"id":"o8hRDOjOt_QM","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1654316180686,"user_tz":-480,"elapsed":466,"user":{"displayName":"Hillidatul Ilmi","userId":"07932027707791477475"}},"outputId":"71d5de62-b643-4549-f786-b974f65297e3"},"source":["stat, p, dof, expected = stats.chi2_contingency(contingency_table)\n","print('p=%.3f' % (p))\n","if p > 0.05:\n","    print('Probably independent')\n","else:\n","    print('Probably dependent')"],"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["p=0.708\n","Probably independent\n"]}]},{"cell_type":"markdown","source":["H0 nya benar"],"metadata":{"id":"jJtYk8kP7vud"}}]}