pythagorean.ipynb

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      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import statsmodels.formula.api as smf\n",
        "import math\n",
        "import numpy as np"
      ]
    },
    {
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          "data": {
            "text/plain": [
              "   season home  pts_home  wl_home away  pts_away  wl_away\n",
              "0    1947  GSW        66        1  BOS        54        0\n",
              "1    1947  NYK        64        1  GSW        60        0\n",
              "2    1947  GSW        62        1  NYK        51        0\n",
              "3    1947  BOS        65        0  NYK        90        1\n",
              "4    1947  NYK        62        1  BOS        44        0"
            ],
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              "      <th></th>\n",
              "      <th>season</th>\n",
              "      <th>home</th>\n",
              "      <th>pts_home</th>\n",
              "      <th>wl_home</th>\n",
              "      <th>away</th>\n",
              "      <th>pts_away</th>\n",
              "      <th>wl_away</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1947</td>\n",
              "      <td>GSW</td>\n",
              "      <td>66</td>\n",
              "      <td>1</td>\n",
              "      <td>BOS</td>\n",
              "      <td>54</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1947</td>\n",
              "      <td>NYK</td>\n",
              "      <td>64</td>\n",
              "      <td>1</td>\n",
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              "      <th>2</th>\n",
              "      <td>1947</td>\n",
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              "      <td>1947</td>\n",
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              "      <td>1947</td>\n",
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              "      <td>1</td>\n",
              "      <td>BOS</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dados",
              "summary": "{\n  \"name\": \"dados\",\n  \"rows\": 58501,\n  \"fields\": [\n    {\n      \"column\": \"season\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17,\n        \"min\": 1947,\n        \"max\": 2023,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          1973,\n          1947,\n          2002\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"home\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"TOR\",\n          \"POR\",\n          \"CHA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pts_home\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14,\n        \"min\": 18,\n        \"max\": 184,\n        \"num_unique_values\": 120,\n        \"samples\": [\n          102,\n          100,\n          77\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wl_home\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"away\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"MEM\",\n          \"CLE\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pts_away\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 19,\n        \"max\": 186,\n        \"num_unique_values\": 122,\n        \"samples\": [\n          67,\n          82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wl_away\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "url = 'https://raw.githubusercontent.com/mths-andrade/pyth_exp/6b781052e0e1ac922cec4f5f58487421663dc4ab/games.csv'\n",
        "\n",
        "dados = pd.read_csv(url)\n",
        "dados.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "kvpYjWwTxK1I"
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      "outputs": [],
      "source": [
        "def pyth_exp(made,allow):\n",
        "  return made**2/(made**2+allow**2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OVDdjxyDnB3_",
        "outputId": "fd696750-16cb-4c99-8c97-51888e8ccfed"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Ganhou 2052 jogos de 4516, portanto tem um aproveitamento de 0.4544.\n"
          ]
        }
      ],
      "source": [
        "home_w=dados.query(\"home=='WAS' and wl_home==1\")\n",
        "away_w=dados.query(\"away=='WAS' and wl_away==1\")\n",
        "home=dados.query(\"home=='WAS'\")\n",
        "away=dados.query(\"away=='WAS'\")\n",
        "games=home.shape[0]+away.shape[0]\n",
        "games_w=home_w.shape[0]+away_w.shape[0]\n",
        "win_perc=games_w/games\n",
        "win_perc = round(win_perc, 4)\n",
        "print(\"Ganhou %.0f jogos de %.0f, portanto tem um aproveitamento de %.4f.\"%(games_w,games,win_perc))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K0htLh2_ybod",
        "outputId": "f494fdd4-4fdc-4a72-ffb6-cb39642d22e4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Os pontos marcados e permitidos são, respectivamente,471509 e 477840.\n",
            "A expectativa pitagórica é de 0.4933.\n"
          ]
        }
      ],
      "source": [
        "pth_m=dados.query(\"home=='WAS'\")[\"pts_home\"].sum()\n",
        "pth_a=dados.query(\"home=='WAS'\")[\"pts_away\"].sum()\n",
        "pta_m=dados.query(\"away=='WAS'\")[\"pts_away\"].sum()\n",
        "pta_a=dados.query(\"away=='WAS'\")[\"pts_home\"].sum()\n",
        "pt_made=pth_m+pta_m\n",
        "pt_allow = pth_a + pta_a\n",
        "print(\"Os pontos marcados e permitidos são, respectivamente,%.0f e %.0f.\"%(pt_made,pt_allow))\n",
        "print(\"A expectativa pitagórica é de %.4f.\"%(pyth_exp(pt_made,pt_allow)))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
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        },
        "id": "iQ6idWF2D2MY",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      wins  games  win_perc  pts_made  pts_allow  pyth_exp\n",
              "team                                                      \n",
              "ATL   2583   5189    0.4978    531868     533111    0.4988\n",
              "BKN   1551   3622    0.4282    369031     376958    0.4894\n",
              "BOS   3105   5256    0.5908    554328     539600    0.5135\n",
              "CHA   1132   2549    0.4441    256910     261911    0.4904\n",
              "CHI   2147   4189    0.5125    429550     428070    0.5017"
            ],
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              "      <th></th>\n",
              "      <th>wins</th>\n",
              "      <th>games</th>\n",
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              "      <th>pts_allow</th>\n",
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              "      <th>ATL</th>\n",
              "      <td>2583</td>\n",
              "      <td>5189</td>\n",
              "      <td>0.4978</td>\n",
              "      <td>531868</td>\n",
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              "      <th>BOS</th>\n",
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              "      <td>554328</td>\n",
              "      <td>539600</td>\n",
              "      <td>0.5135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHA</th>\n",
              "      <td>1132</td>\n",
              "      <td>2549</td>\n",
              "      <td>0.4441</td>\n",
              "      <td>256910</td>\n",
              "      <td>261911</td>\n",
              "      <td>0.4904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHI</th>\n",
              "      <td>2147</td>\n",
              "      <td>4189</td>\n",
              "      <td>0.5125</td>\n",
              "      <td>429550</td>\n",
              "      <td>428070</td>\n",
              "      <td>0.5017</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea44d08b-6a85-455e-96a6-f3c576fd500a')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-ea44d08b-6a85-455e-96a6-f3c576fd500a 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-ea44d08b-6a85-455e-96a6-f3c576fd500a');\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",
              "\n",
              "\n",
              "<div id=\"df-ee580ec8-f3c0-417a-81e4-14252a2a7f8d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ee580ec8-f3c0-417a-81e4-14252a2a7f8d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ee580ec8-f3c0-417a-81e4-14252a2a7f8d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pyth",
              "summary": "{\n  \"name\": \"pyth\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"team\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"TOR\",\n          \"MIA\",\n          \"PHX\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 614,\n        \"min\": 755,\n        \"max\": 3105,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          1037,\n          1409,\n          2231\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"games\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1068,\n        \"min\": 1604,\n        \"max\": 5256,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          5225,\n          2147,\n          5247\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"win_perc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05056858049553432,\n        \"min\": 0.403,\n        \"max\": 0.6023,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.4832,\n          0.5178,\n          0.5422\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pts_made\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 114779,\n        \"min\": 162833,\n        \"max\": 556559,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          216600,\n          269527,\n          444019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pts_allow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 113494,\n        \"min\": 164107,\n        \"max\": 559188,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          216982,\n          268667,\n          438076\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pyth_exp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0073776892622673396,\n        \"min\": 0.4864,\n        \"max\": 0.5148,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          0.5077,\n          0.506,\n          0.4864\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "url='https://raw.githubusercontent.com/mths-andrade/pyth_exp/main/pythagorean.csv'\n",
        "pyth=pd.read_csv(url)\n",
        "pyth.set_index('team',inplace=True)\n",
        "pyth.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_theme(style=\"darkgrid\")\n",
        "sns.set_context(\"notebook\", font_scale=1, rc={\"lines.linewidth\": 1.5})\n",
        "sns.set_palette(\"Spectral\")"
      ],
      "metadata": {
        "id": "tR_H9Qdsav7Q"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "3nMGSz6YLhJf",
        "outputId": "64a293a9-10bf-4af5-9042-a7bdd33ca974"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "x = pyth.pyth_exp\n",
        "y = pyth.win_perc\n",
        "\n",
        "media_win_perc = pyth['win_perc'].mean()\n",
        "media_pyth_exp = pyth['pyth_exp'].mean()\n",
        "\n",
        "cores = []\n",
        "for i, row in pyth.iterrows():\n",
        "  if row['win_perc'] >= media_win_perc and row['pyth_exp'] >= media_pyth_exp:\n",
        "    cores.append('green')\n",
        "  elif row['win_perc'] < media_win_perc and row['pyth_exp'] < media_pyth_exp:\n",
        "    cores.append('red')\n",
        "  else:\n",
        "    cores.append('gray')\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "plt.scatter(data=pyth, x=\"pyth_exp\", y=\"win_perc\",c=cores)\n",
        "plt.title(\"Gráfico de dispersão das estatísticas\")\n",
        "plt.xlabel(\"Expectativa pitagórica\")\n",
        "plt.ylabel(\"Porcentagem real de vitórias\")\n",
        "\n",
        "for i, row in pyth.iterrows():\n",
        "    plt.text(row['pyth_exp'], row['win_perc'], i, fontsize=8)\n",
        "\n",
        "ax.hlines(y = y.mean(), xmin = x.min(), xmax = x.max(), colors='black', linestyles='dashed')\n",
        "ax.vlines(x = x.mean(), ymin = y.min(), ymax = y.max(), colors='darkblue', linestyles='dashed')\n",
        "\n",
        "ax.text(0.4857, 0.5, 'Média da porcentagem de vitórias', fontsize=9, color = 'black', va = \"bottom\")\n",
        "ax.text(0.496, 0.604, 'Média da expectativa pitagórica', fontsize=9, color = 'darkblue', ha = \"left\")\n",
        "\n",
        "ax.text(0.490, 0.402, 'Quadrante abaixo das médias', fontsize=10, color = 'red', ha = \"left\")\n",
        "ax.text(0.505, 0.5, 'Quadrante acima das médias', fontsize=10, color = 'green', ha = \"left\")\n",
        "\n",
        "ax.annotate(\"O único time que não está nos quadrantes em \\n destaque é o Atlanta Hawks, \"\n",
        "            \"que está acima \\n da média de porcentagem de vitórias, mas \\n abaixo da média de expectativa pitagórica. \\n\\n\"\n",
        "            \"Ainda assim, é o time mais próximo das duas \\n médias \"\n",
        "            \"junto com o Denver Nuggets, que está \\n no quadrante negativo de destaque.\",\n",
        "            xy=(0.487, 0.54), xycoords='data',\n",
        "            bbox=dict(boxstyle=\"round\", fc='white', ec='gray'),\n",
        "            xytext=(0, 0), textcoords='offset points', fontsize=8)\n",
        "\n",
        "ax.annotate(\"O time mais regular é o San Antonio Spurs, \\n com o maior valor em ambas as estatísticas.\",\n",
        "            xy=(0.502, 0.46), xycoords='data',\n",
        "            bbox=dict(boxstyle=\"round\", fc='white', ec='green'),\n",
        "            xytext=(0, 0), textcoords='offset points', fontsize=8)\n",
        "\n",
        "ax.annotate(\"Os time mais irregulares são o Los Angeles Clippers \\n e o Minnesota Timberwolves, respectivamente \\n com \"\n",
        "            \"os menores valor da expectativa pitagórica \\n e porcentagem de vitórias.\",\n",
        "            xy=(0.502, 0.425), xycoords='data',\n",
        "            bbox=dict(boxstyle=\"round\", fc='white', ec='crimson'),\n",
        "            xytext=(0, 0), textcoords='offset points', fontsize=8)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        },
        "id": "LKh61oiWDofE",
        "outputId": "cc5461f4-58d1-4e36-86f2-e67e22d7e04c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.05, 'Expectativa pitagórica X porcentagem real de vitórias de cada time')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ax=sns.lmplot(x='pyth_exp',y='win_perc',data=pyth)\n",
        "ax.fig.suptitle(\"Expectativa pitagórica X porcentagem real de vitórias de cada time\",y=1.05)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr=pyth.corr()\n",
        "corr"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "IRSGeXWI9-WF",
        "outputId": "800bf4d3-c713-41ca-b206-6254075146cb"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               wins     games  win_perc  pts_made  pts_allow  pyth_exp\n",
              "wins       1.000000  0.867366  0.606902  0.955113   0.938493  0.583713\n",
              "games      0.867366  1.000000  0.356588  0.900015   0.897278  0.325501\n",
              "win_perc   0.606902  0.356588  1.000000  0.358627   0.310600  0.991663\n",
              "pts_made   0.955113  0.900015  0.358627  1.000000   0.998609  0.334829\n",
              "pts_allow  0.938493  0.897278  0.310600  0.998609   1.000000  0.286120\n",
              "pyth_exp   0.583713  0.325501  0.991663  0.334829   0.286120  1.000000"
            ],
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              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-f4999fbe-e15d-4b09-9932-78905123f051 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-f4999fbe-e15d-4b09-9932-78905123f051');\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",
              "\n",
              "\n",
              "<div id=\"df-ab05412a-7a46-4fb8-941f-bcff9b88a070\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ab05412a-7a46-4fb8-941f-bcff9b88a070')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ab05412a-7a46-4fb8-941f-bcff9b88a070 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8707b57b-55e2-4cd8-b801-4dee104fd90f\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('corr')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8707b57b-55e2-4cd8-b801-4dee104fd90f button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('corr');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "corr",
              "summary": "{\n  \"name\": \"corr\",\n  \"rows\": 6,\n  \"fields\": [\n    {\n      \"column\": \"wins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.18331154934126978,\n        \"min\": 0.5837125573452697,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          1.0,\n          0.867366052798558,\n          0.5837125573452697\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"games\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3005089361683127,\n        \"min\": 0.3255009044686971,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.867366052798558,\n          1.0,\n          0.3255009044686971\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"win_perc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3208148906380628,\n        \"min\": 0.31060033115526814,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.6069024272481065,\n          0.35658835603242595,\n          0.9916630217433073\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pts_made\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.320640195243829,\n        \"min\": 0.33482923610898746,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9551127378546764,\n          0.9000149960785343,\n          0.33482923610898746\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pts_allow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3432177991466709,\n        \"min\": 0.28612007133027195,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.9384929602494173,\n          0.8972784280944499,\n          0.28612007133027195\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pyth_exp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3337162165639752,\n        \"min\": 0.28612007133027195,\n        \"max\": 1.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          0.5837125573452697,\n          0.3255009044686971,\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(corr,cmap='crest',linecolor='black',annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "vO7mXjiB-BJm",
        "outputId": "86967b30-a487-4aab-f590-5c8c784741df"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "QcPSWLEw2jgs"
      },
      "outputs": [],
      "source": [
        "pyth_lm=smf.ols(formula='win_perc ~ pyth_exp', data=pyth).fit()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EtIcgqjKWKuj"
      },
      "source": [
        "#Primeiro modo de calcular R²"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QLEGnDsDWKHV",
        "outputId": "754c6b1d-13fe-437c-e223-a06ea05bbbe9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                            OLS Regression Results                            \n",
            "==============================================================================\n",
            "Dep. Variable:               win_perc   R-squared:                       0.983\n",
            "Model:                            OLS   Adj. R-squared:                  0.983\n",
            "Method:                 Least Squares   F-statistic:                     1658.\n",
            "Date:                Fri, 09 Aug 2024   Prob (F-statistic):           1.82e-26\n",
            "Time:                        17:44:45   Log-Likelihood:                 108.94\n",
            "No. Observations:                  30   AIC:                            -213.9\n",
            "Df Residuals:                      28   BIC:                            -211.1\n",
            "Df Model:                           1                                         \n",
            "Covariance Type:            nonrobust                                         \n",
            "==============================================================================\n",
            "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
            "------------------------------------------------------------------------------\n",
            "Intercept     -2.8987      0.083    -34.773      0.000      -3.069      -2.728\n",
            "pyth_exp       6.7971      0.167     40.722      0.000       6.455       7.139\n",
            "==============================================================================\n",
            "Omnibus:                        1.216   Durbin-Watson:                   1.642\n",
            "Prob(Omnibus):                  0.545   Jarque-Bera (JB):                0.695\n",
            "Skew:                          -0.373   Prob(JB):                        0.706\n",
            "Kurtosis:                       3.023   Cond. No.                         172.\n",
            "==============================================================================\n",
            "\n",
            "Notes:\n",
            "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
          ]
        }
      ],
      "source": [
        "print(pyth_lm.summary())"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x=pyth.pyth_exp\n",
        "y=pyth.win_perc\n",
        "SOMA_X = pyth.pyth_exp.sum()\n",
        "SOMA_Y = pyth.win_perc.sum()\n",
        "SOMA_X2 = pyth.pyth_exp.apply(lambda x: x**2).sum()\n",
        "SOMA_Y2 = pyth.win_perc.apply(lambda y: y**2).sum()\n",
        "SOMA_XY = pyth.apply(lambda data: data.pyth_exp * data.win_perc, axis = 1).sum()"
      ],
      "metadata": {
        "id": "AxVy4mRqIRDF"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "n=30\n",
        "numerador = n * SOMA_XY - SOMA_X * SOMA_Y\n",
        "denominador = n * SOMA_X2 - (SOMA_X)**2\n",
        "beta_2 = numerador / denominador\n",
        "print(\"O coeficiente angular da reta de regressão é\",beta_2.round(2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UUa00MOVIY0t",
        "outputId": "b2630026-acce-4dc7-ebc2-b52bc2deaf22"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O coeficiente angular da reta de regressão é 6.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "beta_1 = pyth.win_perc.mean() - beta_2 * pyth.pyth_exp.mean()\n",
        "print(\"O coeficiente linear da reta de regressão é\",beta_1.round(2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j25ou5wgIo6b",
        "outputId": "48daa691-0fda-41bb-cea4-d14f30ea2a95"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O coeficiente linear da reta de regressão é -2.9\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"A reta de regressão é y=%.2f+%.2fx\"%(beta_1,beta_2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBfdNzIiJ-u1",
        "outputId": "22ff5123-3eb5-4a98-eebf-b9f938438d5d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A reta de regressão é y=-2.90+6.80x\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def previsão(x):\n",
        "  print(\"Dada a expectativa pitagórica %.3f, a previsão da porcentagem real de vitórias é %.3f.\"%(x,beta_1 + beta_2 * x))\n",
        "previsão(0.515)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCxvz7YyL8uK",
        "outputId": "a402b798-43f9-4ac6-8b40-d6c77bda7071"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dada a expectativa pitagórica 0.515, a previsão da porcentagem real de vitórias é 0.602.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Outra maneira de obter os coeficientes"
      ],
      "metadata": {
        "id": "dld6vx_Yui1C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pyth_lm.params"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "5tzUQEGHti6X",
        "outputId": "ea7b4119-66af-4de6-b1cd-ed74cea9f204"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Intercept   -2.898724\n",
              "pyth_exp     6.797114\n",
              "dtype: float64"
            ],
            "text/html": [
              "<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>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Intercept</th>\n",
              "      <td>-2.898724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pyth_exp</th>\n",
              "      <td>6.797114</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "$\\beta_1$ é o intercepto, enquanto $\\beta_2$ é o pyth_exp."
      ],
      "metadata": {
        "id": "cIVZlIP6uH57"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Intervalos de confiança"
      ],
      "metadata": {
        "id": "tHdTJQpOuq5C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pyth_lm.conf_int(alpha=0.05)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "02NY1ewnti1l",
        "outputId": "15b15af8-0857-469b-a6b4-adad0467e3c8"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  0         1\n",
              "Intercept -3.069482 -2.727966\n",
              "pyth_exp   6.455205  7.139022"
            ],
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              "\n",
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              "      <th>pyth_exp</th>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-13ff0988-34a7-4088-8bf5-bd6f3bb9df25');\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",
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              "  </div>\n",
              "\n",
              "\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
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              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "  }\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-1b49fac7-f36f-41a7-a290-ba87c30b0053 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pyth_lm\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.73497068739921,\n        \"min\": -3.069481908892614,\n        \"max\": 6.455204979412596,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          6.455204979412596,\n          -3.069481908892614\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6.977014067349985,\n        \"min\": -2.727965883116398,\n        \"max\": 7.139022035797822,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7.139022035797822,\n          -2.727965883116398\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A um nível de 95%, o intervalo de confiança de $\\beta_1$ é [-3.07, -2.73], enquanto o de $\\beta_2$ é [6.46, 7.14]."
      ],
      "metadata": {
        "id": "hj5z034Su4T6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Previsões no *dataframe*"
      ],
      "metadata": {
        "id": "GzMV0mJJvl__"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pyth.insert(pyth.columns.get_loc(\"pts_made\"), 'prev_win_perc', (pyth_lm.predict()).round(4))\n",
        "pyth"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "-ulZ0w_WvkCo",
        "outputId": "253b4402-53ce-43b5-bd81-fc784cc437ad"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      wins  games  win_perc  prev_win_perc  pts_made  pts_allow  pyth_exp\n",
              "team                                                                     \n",
              "ATL   2583   5189    0.4978         0.4917    531868     533111    0.4988\n",
              "BKN   1551   3622    0.4282         0.4278    369031     376958    0.4894\n",
              "BOS   3105   5256    0.5908         0.5916    554328     539600    0.5135\n",
              "CHA   1132   2549    0.4441         0.4346    256910     261911    0.4904\n",
              "CHI   2147   4189    0.5125         0.5114    429550     428070    0.5017\n",
              "CLE   1853   3943    0.4699         0.4686    399130     402790    0.4954\n",
              "DAL   1706   3379    0.5049         0.5026    350205     349939    0.5004\n",
              "DEN   1790   3623    0.4941         0.4842    389525     391353    0.4977\n",
              "DET   2484   5225    0.4754         0.4767    532200     535839    0.4966\n",
              "GSW   2476   5247    0.4719         0.4754    555153     559188    0.4964\n",
              "HOU   2154   4196    0.5133         0.5134    441320     439578    0.5020\n",
              "IND   1798   3623    0.4963         0.5060    374059     373393    0.5009\n",
              "LAC   1638   3950    0.4147         0.4074    407568     418822    0.4864\n",
              "LAL   3100   5233    0.5924         0.5800    556559     543548    0.5118\n",
              "MEM    915   2147    0.4262         0.4257    212327     216995    0.4891\n",
              "MIA   1409   2721    0.5178         0.5107    269527     268667    0.5016\n",
              "MIL   2168   4115    0.5269         0.5406    431182     426042    0.5060\n",
              "MIN   1060   2630    0.4030         0.4176    265117     271632    0.4879\n",
              "NOP    755   1604    0.4707         0.4733    162833     164107    0.4961\n",
              "NYK   2524   2525    0.4806         0.4849    535672     538020    0.4978\n",
              "OKC   2232   4196    0.5319         0.5297    442762     438854    0.5044\n",
              "ORL   1248   2639    0.4729         0.4733    266933     269012    0.4961\n",
              "PHI   2658   5190    0.5121         0.5053    539556     538683    0.5008\n",
              "PHX   2231   4115    0.5422         0.5454    444019     438076    0.5067\n",
              "POR   2123   3952    0.5372         0.5399    414827     409947    0.5059\n",
              "SAC   2361   5230    0.4514         0.4489    546276     554560    0.4925\n",
              "SAS   2181   3621    0.6023         0.6004    380542     369416    0.5148\n",
              "TOR   1037   2146    0.4832         0.4937    216600     216982    0.4991\n",
              "UTA   2030   3704    0.5481         0.5522    383725     377880    0.5077\n",
              "WAS   2052   4516    0.4544         0.4543    471509     477840    0.4933"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-4d2d7fc1-5274-4491-a22d-46001d1aa134\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>wins</th>\n",
              "      <th>games</th>\n",
              "      <th>win_perc</th>\n",
              "      <th>prev_win_perc</th>\n",
              "      <th>pts_made</th>\n",
              "      <th>pts_allow</th>\n",
              "      <th>pyth_exp</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>team</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ATL</th>\n",
              "      <td>2583</td>\n",
              "      <td>5189</td>\n",
              "      <td>0.4978</td>\n",
              "      <td>0.4917</td>\n",
              "      <td>531868</td>\n",
              "      <td>533111</td>\n",
              "      <td>0.4988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BKN</th>\n",
              "      <td>1551</td>\n",
              "      <td>3622</td>\n",
              "      <td>0.4282</td>\n",
              "      <td>0.4278</td>\n",
              "      <td>369031</td>\n",
              "      <td>376958</td>\n",
              "      <td>0.4894</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOS</th>\n",
              "      <td>3105</td>\n",
              "      <td>5256</td>\n",
              "      <td>0.5908</td>\n",
              "      <td>0.5916</td>\n",
              "      <td>554328</td>\n",
              "      <td>539600</td>\n",
              "      <td>0.5135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHA</th>\n",
              "      <td>1132</td>\n",
              "      <td>2549</td>\n",
              "      <td>0.4441</td>\n",
              "      <td>0.4346</td>\n",
              "      <td>256910</td>\n",
              "      <td>261911</td>\n",
              "      <td>0.4904</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHI</th>\n",
              "      <td>2147</td>\n",
              "      <td>4189</td>\n",
              "      <td>0.5125</td>\n",
              "      <td>0.5114</td>\n",
              "      <td>429550</td>\n",
              "      <td>428070</td>\n",
              "      <td>0.5017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CLE</th>\n",
              "      <td>1853</td>\n",
              "      <td>3943</td>\n",
              "      <td>0.4699</td>\n",
              "      <td>0.4686</td>\n",
              "      <td>399130</td>\n",
              "      <td>402790</td>\n",
              "      <td>0.4954</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DAL</th>\n",
              "      <td>1706</td>\n",
              "      <td>3379</td>\n",
              "      <td>0.5049</td>\n",
              "      <td>0.5026</td>\n",
              "      <td>350205</td>\n",
              "      <td>349939</td>\n",
              "      <td>0.5004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DEN</th>\n",
              "      <td>1790</td>\n",
              "      <td>3623</td>\n",
              "      <td>0.4941</td>\n",
              "      <td>0.4842</td>\n",
              "      <td>389525</td>\n",
              "      <td>391353</td>\n",
              "      <td>0.4977</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DET</th>\n",
              "      <td>2484</td>\n",
              "      <td>5225</td>\n",
              "      <td>0.4754</td>\n",
              "      <td>0.4767</td>\n",
              "      <td>532200</td>\n",
              "      <td>535839</td>\n",
              "      <td>0.4966</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GSW</th>\n",
              "      <td>2476</td>\n",
              "      <td>5247</td>\n",
              "      <td>0.4719</td>\n",
              "      <td>0.4754</td>\n",
              "      <td>555153</td>\n",
              "      <td>559188</td>\n",
              "      <td>0.4964</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HOU</th>\n",
              "      <td>2154</td>\n",
              "      <td>4196</td>\n",
              "      <td>0.5133</td>\n",
              "      <td>0.5134</td>\n",
              "      <td>441320</td>\n",
              "      <td>439578</td>\n",
              "      <td>0.5020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>IND</th>\n",
              "      <td>1798</td>\n",
              "      <td>3623</td>\n",
              "      <td>0.4963</td>\n",
              "      <td>0.5060</td>\n",
              "      <td>374059</td>\n",
              "      <td>373393</td>\n",
              "      <td>0.5009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAC</th>\n",
              "      <td>1638</td>\n",
              "      <td>3950</td>\n",
              "      <td>0.4147</td>\n",
              "      <td>0.4074</td>\n",
              "      <td>407568</td>\n",
              "      <td>418822</td>\n",
              "      <td>0.4864</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAL</th>\n",
              "      <td>3100</td>\n",
              "      <td>5233</td>\n",
              "      <td>0.5924</td>\n",
              "      <td>0.5800</td>\n",
              "      <td>556559</td>\n",
              "      <td>543548</td>\n",
              "      <td>0.5118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MEM</th>\n",
              "      <td>915</td>\n",
              "      <td>2147</td>\n",
              "      <td>0.4262</td>\n",
              "      <td>0.4257</td>\n",
              "      <td>212327</td>\n",
              "      <td>216995</td>\n",
              "      <td>0.4891</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIA</th>\n",
              "      <td>1409</td>\n",
              "      <td>2721</td>\n",
              "      <td>0.5178</td>\n",
              "      <td>0.5107</td>\n",
              "      <td>269527</td>\n",
              "      <td>268667</td>\n",
              "      <td>0.5016</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIL</th>\n",
              "      <td>2168</td>\n",
              "      <td>4115</td>\n",
              "      <td>0.5269</td>\n",
              "      <td>0.5406</td>\n",
              "      <td>431182</td>\n",
              "      <td>426042</td>\n",
              "      <td>0.5060</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIN</th>\n",
              "      <td>1060</td>\n",
              "      <td>2630</td>\n",
              "      <td>0.4030</td>\n",
              "      <td>0.4176</td>\n",
              "      <td>265117</td>\n",
              "      <td>271632</td>\n",
              "      <td>0.4879</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NOP</th>\n",
              "      <td>755</td>\n",
              "      <td>1604</td>\n",
              "      <td>0.4707</td>\n",
              "      <td>0.4733</td>\n",
              "      <td>162833</td>\n",
              "      <td>164107</td>\n",
              "      <td>0.4961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NYK</th>\n",
              "      <td>2524</td>\n",
              "      <td>2525</td>\n",
              "      <td>0.4806</td>\n",
              "      <td>0.4849</td>\n",
              "      <td>535672</td>\n",
              "      <td>538020</td>\n",
              "      <td>0.4978</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OKC</th>\n",
              "      <td>2232</td>\n",
              "      <td>4196</td>\n",
              "      <td>0.5319</td>\n",
              "      <td>0.5297</td>\n",
              "      <td>442762</td>\n",
              "      <td>438854</td>\n",
              "      <td>0.5044</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ORL</th>\n",
              "      <td>1248</td>\n",
              "      <td>2639</td>\n",
              "      <td>0.4729</td>\n",
              "      <td>0.4733</td>\n",
              "      <td>266933</td>\n",
              "      <td>269012</td>\n",
              "      <td>0.4961</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHI</th>\n",
              "      <td>2658</td>\n",
              "      <td>5190</td>\n",
              "      <td>0.5121</td>\n",
              "      <td>0.5053</td>\n",
              "      <td>539556</td>\n",
              "      <td>538683</td>\n",
              "      <td>0.5008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PHX</th>\n",
              "      <td>2231</td>\n",
              "      <td>4115</td>\n",
              "      <td>0.5422</td>\n",
              "      <td>0.5454</td>\n",
              "      <td>444019</td>\n",
              "      <td>438076</td>\n",
              "      <td>0.5067</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>POR</th>\n",
              "      <td>2123</td>\n",
              "      <td>3952</td>\n",
              "      <td>0.5372</td>\n",
              "      <td>0.5399</td>\n",
              "      <td>414827</td>\n",
              "      <td>409947</td>\n",
              "      <td>0.5059</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAC</th>\n",
              "      <td>2361</td>\n",
              "      <td>5230</td>\n",
              "      <td>0.4514</td>\n",
              "      <td>0.4489</td>\n",
              "      <td>546276</td>\n",
              "      <td>554560</td>\n",
              "      <td>0.4925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SAS</th>\n",
              "      <td>2181</td>\n",
              "      <td>3621</td>\n",
              "      <td>0.6023</td>\n",
              "      <td>0.6004</td>\n",
              "      <td>380542</td>\n",
              "      <td>369416</td>\n",
              "      <td>0.5148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TOR</th>\n",
              "      <td>1037</td>\n",
              "      <td>2146</td>\n",
              "      <td>0.4832</td>\n",
              "      <td>0.4937</td>\n",
              "      <td>216600</td>\n",
              "      <td>216982</td>\n",
              "      <td>0.4991</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>UTA</th>\n",
              "      <td>2030</td>\n",
              "      <td>3704</td>\n",
              "      <td>0.5481</td>\n",
              "      <td>0.5522</td>\n",
              "      <td>383725</td>\n",
              "      <td>377880</td>\n",
              "      <td>0.5077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WAS</th>\n",
              "      <td>2052</td>\n",
              "      <td>4516</td>\n",
              "      <td>0.4544</td>\n",
              "      <td>0.4543</td>\n",
              "      <td>471509</td>\n",
              "      <td>477840</td>\n",
              "      <td>0.4933</td>\n",
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              "  </tbody>\n",
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              "  .colab-df-quickchart:hover {\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-color: transparent;\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-47f2d1bb-7c41-42f2-8f50-678b60ab3bac button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_023c175a-f516-40ec-a9ec-e243dbbc3c83\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('pyth')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_023c175a-f516-40ec-a9ec-e243dbbc3c83 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('pyth');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pyth",
              "summary": "{\n  \"name\": \"pyth\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"team\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"TOR\",\n          \"MIA\",\n          \"PHX\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 614,\n        \"min\": 755,\n        \"max\": 3105,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          1037,\n          1409,\n          2231\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"games\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1068,\n        \"min\": 1604,\n        \"max\": 5256,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          5225,\n          2147,\n          5247\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"win_perc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05056858049553432,\n        \"min\": 0.403,\n        \"max\": 0.6023,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.4832,\n          0.5178,\n          0.5422\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prev_win_perc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05014042132426404,\n        \"min\": 0.4074,\n        \"max\": 0.6004,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          0.5522,\n          0.5406,\n          0.4074\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pts_made\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 114779,\n        \"min\": 162833,\n        \"max\": 556559,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          216600,\n          269527,\n          444019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pts_allow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 113494,\n        \"min\": 164107,\n        \"max\": 559188,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          216982,\n          268667,\n          438076\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pyth_exp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0073776892622673396,\n        \"min\": 0.4864,\n        \"max\": 0.5148,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          0.5077,\n          0.506,\n          0.4864\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q4kGQDu_WBIu"
      },
      "source": [
        "#Segundo modo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "fI7bnF-3Bof6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "811e4995-fa39-4f6e-c8a2-386b904b898d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A soma dos quadrados dos erros é 0.001\n",
            "A soma dos quadrados dos pontos da regressão é 0.073\n",
            "A soma dos quadrados totais é 0.074\n"
          ]
        }
      ],
      "source": [
        "SQE=pyth_lm.ssr\n",
        "SQR=pyth_lm.ess\n",
        "SQT=SQE+SQR\n",
        "print(\"A soma dos quadrados dos erros é\", SQE.round(3))\n",
        "print(\"A soma dos quadrados dos pontos da regressão é\",SQR.round(3))\n",
        "print(\"A soma dos quadrados totais é\",SQT.round(3))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "8-zwg76WYD9J",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ff74426d-1b1e-4d27-ab22-497159d233ef"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O erro quadrático médio é 4.3977042679913256e-05\n"
          ]
        }
      ],
      "source": [
        "EQM=pyth_lm.mse_resid\n",
        "print(\"O erro quadrático médio é\",EQM)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "4b2KCDX-BodA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "85066e3a-e9b8-42d5-de4d-2e9b27091ab4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O coeficiente de regressão do modelo é 0.983\n"
          ]
        }
      ],
      "source": [
        "R2=SQR/SQT\n",
        "print(\"O coeficiente de regressão do modelo é\",R2.round(3))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JGtJLy0XWEe8"
      },
      "source": [
        "#Terceiro modo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "j30TxKgpRQuz",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5c553cfe-5660-4f43-f111-5817562ead60"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.983"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "pyth_lm.rsquared.round(3)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Teste de hipótese para a nulidade do coeficiente angular\n",
        "\n",
        "## $H_0: \\beta_2 = 0$\n",
        "## $H_1: \\beta_2 \\neq 0$\n",
        "# $$E(b_2) = \\beta_2$$"
      ],
      "metadata": {
        "id": "owCdYPuXEKRZ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### $$s_{b_2} = \\frac{s}{\\sqrt{\\sum{(X_i - \\bar{X})^2}}}$$"
      ],
      "metadata": {
        "id": "7MjASX_1Fcqm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "s = np.sqrt(pyth_lm.mse_resid)\n",
        "s"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DYyTEN6HDDwD",
        "outputId": "3f078246-73ce-464d-9ccf-986819a4ce82"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.006631518881818347"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "SOMA_DESVIO2 = pyth.pyth_exp.apply(lambda x: (x - pyth.pyth_exp.mean())**2).sum()\n",
        "SOMA_DESVIO2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cLQUuRn2DN37",
        "outputId": "9c1cc061-1aae-421a-8176-2dff2c36dde0"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0015784786666666693"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s_beta_2 = s / np.sqrt(SOMA_DESVIO2)\n",
        "s_beta_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IZZLg-GEDXAa",
        "outputId": "cb8c3d85-cf7b-4d35-c85d-7265441f1a13"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.16691434100984504"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Áreas de aceitação e rejeição de $H_0$"
      ],
      "metadata": {
        "id": "4V6dL32MFyNv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import t as t_student"
      ],
      "metadata": {
        "id": "1TFUXBlpDaqq"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graus_de_liberdade = pyth_lm.df_resid\n",
        "graus_de_liberdade"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BBA4SDsBDcw4",
        "outputId": "3a332ebe-36e3-4108-afb9-c7843f2f08c7"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "28.0"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "significancia=0.05\n",
        "confiança=1-significancia\n",
        "probabilidade = (0.5 + (0.95 / 2))\n",
        "probabilidade"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S2mVp5EFDe8z",
        "outputId": "e3514f17-7f9c-4ae0-95e8-3c4ba32c6941"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.975"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "![Região de Aceitação](https://caelum-online-public.s3.amazonaws.com/1273-estatistica-parte4/01/img031.png)"
      ],
      "metadata": {
        "id": "Mt1gTfEGF_8h"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t_alpha_2 = t_student.ppf(probabilidade, graus_de_liberdade)\n",
        "t_alpha_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YtEESLp-Difi",
        "outputId": "5ff0c6ab-529d-4e7c-e698-5e022cc3f0fc"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.048407141795244"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### $$t = \\frac{b_2 - \\beta_2}{s_{b_2}}$$"
      ],
      "metadata": {
        "id": "jFVtp6cgE_MX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t = (beta_2 - 0) / s_beta_2\n",
        "t"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyuR35g3DuGJ",
        "outputId": "e703dec3-0e23-4a36-fefd-41b75b325a7c"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "40.72216603124802"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Critério do valor crítico\n",
        "\n",
        "> ### Teste Bicaudal\n",
        "> ### <font color='lightgreen'>Rejeitar $H_0$ se $t \\leq -t_{\\alpha / 2}$ ou se $t \\geq t_{\\alpha / 2}$</font>"
      ],
      "metadata": {
        "id": "JeWK7XyaGW9s"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "t <= -t_alpha_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oy28u5TUDy48",
        "outputId": "7b58fac6-6a39-4330-c53f-95b8b33deb74"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t >= t_alpha_2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4eeJsjQ3DzTB",
        "outputId": "e35c229d-ba9a-4f81-bbac-0956448e5841"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Critério do p-valor\n",
        "\n",
        "> ### Teste Bicaudal\n",
        "> ### <font color='lightyellow'>Rejeitar $H_0$ se $p-valor\\leq\\alpha$</font>"
      ],
      "metadata": {
        "id": "vALr83M9GhHL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "p_valor = 2 * (t_student.sf(t, graus_de_liberdade))\n",
        "p_valor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "N5UA9MGFD3yP",
        "outputId": "cbe5040f-3803-4ee1-8958-94ae845470a3"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.823977221052551e-26"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p_valor <= significancia"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CR4ucdobGrRa",
        "outputId": "e074d6b6-5f61-4213-f7de-f1250fd72ded"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color='lightblue'>Rejeito $H_0$, logo existe uma relação significativa entre as duas variáveis.</font>"
      ],
      "metadata": {
        "id": "87E1o5AtGu3z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Teste F\n",
        "\n",
        "## $H_0: \\beta_2 = 0$\n",
        "## $H_0: \\beta_2 \\neq 0$"
      ],
      "metadata": {
        "id": "cIz2noRwHu-p"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### $$F = \\frac{\\frac{SQR}{k}}{\\frac{SQE}{n-k-1}}$$"
      ],
      "metadata": {
        "id": "g_Og6aMoH66X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pyth_lm.fvalue"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ruRh9XjcHxJY",
        "outputId": "66c6ddbd-88a3-466c-9b48-2a1f82da44f9"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1658.2948062794328"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Teste do p-valor"
      ],
      "metadata": {
        "id": "D8OaFzD3Lngl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pyth_lm.f_pvalue"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yiiSgz1dIDFh",
        "outputId": "1bf21388-cb9c-44ae-8a00-1abb5fad23ae"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.8239772210085797e-26"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p_valor <= 0.05"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OChPD8njINPf",
        "outputId": "d800bb8d-2f64-479e-bea2-299a680b58d2"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color='lightblue'>Rejeito $H_0$, logo $\\beta_2 \\neq 0$.</font>"
      ],
      "metadata": {
        "id": "nterYtDvLn8r"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Resíduos"
      ],
      "metadata": {
        "id": "HBSuGwvNI6tG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pyth['residue'] = (pyth_lm.resid).round(4)\n",
        "pyth.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "H259hr1lI5qx",
        "outputId": "3f5e490b-4fb3-4e81-e14e-58d63ecb17ed"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      wins  games  win_perc  prev_win_perc  pts_made  pts_allow  pyth_exp  \\\n",
              "team                                                                        \n",
              "ATL   2583   5189    0.4978         0.4917    531868     533111    0.4988   \n",
              "BKN   1551   3622    0.4282         0.4278    369031     376958    0.4894   \n",
              "BOS   3105   5256    0.5908         0.5916    554328     539600    0.5135   \n",
              "CHA   1132   2549    0.4441         0.4346    256910     261911    0.4904   \n",
              "CHI   2147   4189    0.5125         0.5114    429550     428070    0.5017   \n",
              "\n",
              "      residue  \n",
              "team           \n",
              "ATL    0.0061  \n",
              "BKN    0.0004  \n",
              "BOS   -0.0008  \n",
              "CHA    0.0095  \n",
              "CHI    0.0011  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>wins</th>\n",
              "      <th>games</th>\n",
              "      <th>win_perc</th>\n",
              "      <th>prev_win_perc</th>\n",
              "      <th>pts_made</th>\n",
              "      <th>pts_allow</th>\n",
              "      <th>pyth_exp</th>\n",
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              "      <th>team</th>\n",
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ATL</th>\n",
              "      <td>2583</td>\n",
              "      <td>5189</td>\n",
              "      <td>0.4978</td>\n",
              "      <td>0.4917</td>\n",
              "      <td>531868</td>\n",
              "      <td>533111</td>\n",
              "      <td>0.4988</td>\n",
              "      <td>0.0061</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BKN</th>\n",
              "      <td>1551</td>\n",
              "      <td>3622</td>\n",
              "      <td>0.4282</td>\n",
              "      <td>0.4278</td>\n",
              "      <td>369031</td>\n",
              "      <td>376958</td>\n",
              "      <td>0.4894</td>\n",
              "      <td>0.0004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BOS</th>\n",
              "      <td>3105</td>\n",
              "      <td>5256</td>\n",
              "      <td>0.5908</td>\n",
              "      <td>0.5916</td>\n",
              "      <td>554328</td>\n",
              "      <td>539600</td>\n",
              "      <td>0.5135</td>\n",
              "      <td>-0.0008</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHA</th>\n",
              "      <td>1132</td>\n",
              "      <td>2549</td>\n",
              "      <td>0.4441</td>\n",
              "      <td>0.4346</td>\n",
              "      <td>256910</td>\n",
              "      <td>261911</td>\n",
              "      <td>0.4904</td>\n",
              "      <td>0.0095</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CHI</th>\n",
              "      <td>2147</td>\n",
              "      <td>4189</td>\n",
              "      <td>0.5125</td>\n",
              "      <td>0.5114</td>\n",
              "      <td>429550</td>\n",
              "      <td>428070</td>\n",
              "      <td>0.5017</td>\n",
              "      <td>0.0011</td>\n",
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              "\n",
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              "        document.querySelector('#df-4d670f67-be91-42c4-95ea-7954212506cf 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-4d670f67-be91-42c4-95ea-7954212506cf');\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",
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              "</svg>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
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              "    border: none;\n",
              "    border-radius: 50%;\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pyth",
              "summary": "{\n  \"name\": \"pyth\",\n  \"rows\": 30,\n  \"fields\": [\n    {\n      \"column\": \"team\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 30,\n        \"samples\": [\n          \"TOR\",\n          \"MIA\",\n          \"PHX\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"wins\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 614,\n        \"min\": 755,\n        \"max\": 3105,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          1037,\n          1409,\n          2231\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"games\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1068,\n        \"min\": 1604,\n        \"max\": 5256,\n        \"num_unique_values\": 27,\n        \"samples\": [\n          5225,\n          2147,\n          5247\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"win_perc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05056858049553432,\n        \"min\": 0.403,\n        \"max\": 0.6023,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          0.4832,\n          0.5178,\n          0.5422\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prev_win_perc\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.05014042132426404,\n        \"min\": 0.4074,\n        \"max\": 0.6004,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          0.5522,\n          0.5406,\n          0.4074\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pts_made\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 114779,\n        \"min\": 162833,\n        \"max\": 556559,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          216600,\n          269527,\n          444019\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pts_allow\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 113494,\n        \"min\": 164107,\n        \"max\": 559188,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          216982,\n          268667,\n          438076\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pyth_exp\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0073776892622673396,\n        \"min\": 0.4864,\n        \"max\": 0.5148,\n        \"num_unique_values\": 29,\n        \"samples\": [\n          0.5077,\n          0.506,\n          0.4864\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"residue\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.006517905488270215,\n        \"min\": -0.0146,\n        \"max\": 0.0124,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          -0.0105,\n          0.0071,\n          -0.0032\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pyth.residue.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e2q9Gn0FJKjq",
        "outputId": "a44759cb-41f8-4aa6-b315-bc9795daff6c"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-3.333333333333264e-06"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Normalidade dos resíduos: Omnibus\n",
        "> ### $H_0:$ Os dados se distribuem como uma normal"
      ],
      "metadata": {
        "id": "KXRerjsTJW_O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import normaltest\n",
        "statistic,p_valor=normaltest(pyth.residue)"
      ],
      "metadata": {
        "id": "MYcE6ezOJW0o"
      },
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "p_valor<=0.05"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rB2cHFBFJWxP",
        "outputId": "14c4467a-4f1e-42f0-ef2b-e57ea859cde6"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color='red'>Rejeito $H_0$, logo os dados não se distribuem como uma normal.</font>"
      ],
      "metadata": {
        "id": "pOqmaWyjKrOF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### DIstribuição dos resíduos"
      ],
      "metadata": {
        "id": "cVnIeRYeLWaj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import probplot\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "LliNK7dXJWsO"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "(_,(_,_,_))=probplot(pyth.residue,plot=plt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 480
        },
        "id": "g0h6bSICJysn",
        "outputId": "eb339ecf-380c-4cb3-b1e6-fb6e3f45d0bc"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Os dados/pontos não estão alinhados à reta, logo não se distribuem como uma normal."
      ],
      "metadata": {
        "id": "JLBu-eZIzOGL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Normalidade dos resíduos: Jarque-Bera"
      ],
      "metadata": {
        "id": "OZAlBL8mKMUG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Simetria"
      ],
      "metadata": {
        "id": "L82sC-OWIsTu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import skew\n",
        "S=skew(pyth.residue)\n",
        "S"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vAoXbpE3Irtn",
        "outputId": "e9fcc2a2-8bdb-4843-bc9c-f1548c2c8312"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-0.3716096362952579"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Curtose"
      ],
      "metadata": {
        "id": "guqeMsXnKDsq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import kurtosis\n",
        "C=3+kurtosis(pyth.residue)\n",
        "C"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CuKbu9EuIXPQ",
        "outputId": "45f5eb71-61cc-43ec-8db6-8557427fed80"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.0244100681777164"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### $$JB = \\frac{n}{6}(S^2 + \\frac{1}{4}(C - 3)^2)$$"
      ],
      "metadata": {
        "id": "12T9stZvLJpT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "JB=(n/6)*(S**2+(1/4)*(C-3)**2)\n",
        "JB"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RG3SY2VLIXMP",
        "outputId": "8dc4d7c1-031b-4c99-db88-36b092e9524b"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6912134232230203"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Teste do p-valor"
      ],
      "metadata": {
        "id": "CVdVIC8jLMqE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.stats import chi2\n",
        "p_valor=chi2.sf(JB,2)\n",
        "p_valor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JBD3SSXQIXJP",
        "outputId": "c4cb818e-fa8c-4f6e-f26b-b4a2fe1ae18d"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7077907982770808"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "p_valor<=0.05"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HdBcKz2BIXGQ",
        "outputId": "f50f2536-e2d5-4fe1-f71c-023c725fd9c2"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### <font color='red'>Rejeito $H_0$, logo os dados não se distribuem como uma normal.</font>"
      ],
      "metadata": {
        "id": "DpJaf3uGKmZ3"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}