Challenge_Distribution_Osuolale_Iyanuloluwa.ipynb

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      "name": "Challenge_Distribution Osuolale Iyanuloluwa.ipynb",
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        "<a href=\"https://colab.research.google.com/github/dmarinere/10academysolution/blob/master/Challenge_Distribution_Osuolale_Iyanuloluwa.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        "## Challenge_Distribution\n",
        "\n",
        "In this challenge you consider the real estate data from the city of Ames, Iowa. The details of every real estate transaction in Ames is recorded by the City Assessor's office (https://www.openintro.org/stat/data/ames.csv).\n",
        "\n",
        "**Based on this data, perform the following tasks**:\n",
        "\n",
        "- Take a random sample of size 50 from <code>price</code>. Using this sample, what is your best point estimate of the population mean?\n",
        "\n",
        "\n",
        "- Since you have access to the population, simulate the sampling distribution for the average home price in Ames by taking 5000 samples from the population of size 50 and computing 5000 sample means. Store these means in a vector called <code>sample_means50</code>. Plot the data, then describe the shape of this sampling distribution. Based on this sampling distribution, what would you guess the mean home price of the population to be? Finally, calculate and report the population mean.\n",
        "\n",
        "\n",
        "- Change your sample size from 50 to 150, then compute the sampling distribution using the same method as above, and store these means in a new vector called <code>sample_means150</code>. Describe the shape of this sampling distribution, and compare it to the sampling distribution for a sample size of 50. Based on this sampling distribution, what would you guess to be the mean sale price of homes in Ames?\n",
        "\n",
        "\n",
        "- Of the sampling distributions from 2 and 3, which has a smaller spread? If we’re concerned with making estimates that are more often close to the true value, would we prefer a distribution with a large or small spread?\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9z_2UTkKJ1Cs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4z5e_Rb3Jirm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "df = pd.read_csv(\"https://www.openintro.org/stat/data/ames.csv\")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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        "colab": {
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          "height": 253
        },
        "outputId": "e31cd563-ee43-4af8-bb4d-4de15fba06ba"
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      "source": [
        "#I wanted to see what the data looks like\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>IR1</td>\n",
              "      <td>Lvl</td>\n",
              "      <td>AllPub</td>\n",
              "      <td>Inside</td>\n",
              "      <td>Gtl</td>\n",
              "      <td>Gilbert</td>\n",
              "      <td>Norm</td>\n",
              "      <td>Norm</td>\n",
              "      <td>1Fam</td>\n",
              "      <td>2Story</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>1997</td>\n",
              "      <td>1998</td>\n",
              "      <td>Gable</td>\n",
              "      <td>CompShg</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>VinylSd</td>\n",
              "      <td>None</td>\n",
              "      <td>0.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>PConc</td>\n",
              "      <td>Gd</td>\n",
              "      <td>TA</td>\n",
              "      <td>No</td>\n",
              "      <td>GLQ</td>\n",
              "      <td>791.0</td>\n",
              "      <td>Unf</td>\n",
              "      <td>0.0</td>\n",
              "      <td>137.0</td>\n",
              "      <td>928.0</td>\n",
              "      <td>...</td>\n",
              "      <td>Y</td>\n",
              "      <td>SBrkr</td>\n",
              "      <td>928</td>\n",
              "      <td>701</td>\n",
              "      <td>0</td>\n",
              "      <td>1629</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>6</td>\n",
              "      <td>Typ</td>\n",
              "      <td>1</td>\n",
              "      <td>TA</td>\n",
              "      <td>Attchd</td>\n",
              "      <td>1997.0</td>\n",
              "      <td>Fin</td>\n",
              "      <td>2.0</td>\n",
              "      <td>482.0</td>\n",
              "      <td>TA</td>\n",
              "      <td>TA</td>\n",
              "      <td>Y</td>\n",
              "      <td>212</td>\n",
              "      <td>34</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>MnPrv</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>2010</td>\n",
              "      <td>WD</td>\n",
              "      <td>Normal</td>\n",
              "      <td>189900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 82 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Order        PID  MS.SubClass  ... Sale.Type  Sale.Condition  SalePrice\n",
              "0      1  526301100           20  ...       WD           Normal     215000\n",
              "1      2  526350040           20  ...       WD           Normal     105000\n",
              "2      3  526351010           20  ...       WD           Normal     172000\n",
              "3      4  526353030           20  ...       WD           Normal     244000\n",
              "4      5  527105010           60  ...       WD           Normal     189900\n",
              "\n",
              "[5 rows x 82 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VeWDzEx8Ji78",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "7f006bb5-63f5-4362-ef3b-2bdf672f9d45"
      },
      "source": [
        "#creating price dataframe\n",
        "price = df['SalePrice']\n",
        "#taking a sample of size 50 from price\n",
        "rand_sample = price.sample(n=50)\n",
        "#sample mean which is also known as the point estimate\n",
        "print(f\"Mean price =  {rand_sample.mean()}\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean price = 205911.42\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tXuqqFqpq9Hs",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#taking 5000 samples  of size 50 and computing its mean using list comprehension\n",
        "sample_means50 = [price.sample(n=50).mean() for i in range(0, 5000)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hA7BAODJrIGW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "outputId": "c0dfdcfc-0216-4d2c-8e77-2f37e7f3fb9f"
      },
      "source": [
        "#plotting the data\n",
        "plt.rcParams['figure.figsize'] = (12,8)\n",
        "plt.hist(sample_means50, edgecolor = 'black', linewidth = 1.2, color='blue')\n",
        "plt.title(\"SAMPLE_MEAN 2\", size = 20) \n",
        "plt.xlabel(\"Sample means\", size=15)\n",
        "plt.ylabel(\"Frequency\", size=15)\n",
        "plt.show();"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWbhs8Lds9JE",
        "colab_type": "text"
      },
      "source": [
        "**I would say that this sample mean look very much like a normal distribution and I would assume that the mean SalePrice of this population is around 180000**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aC8Wt0wKsw6u",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "392454b7-76e5-4f9b-edbe-a82c0b7aa33a"
      },
      "source": [
        "#calculating and reporting the population mean of the salesPrice\n",
        "print(\"Mean:\",np.mean(price))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mean: 180796.0600682594\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vp26PwfqsN33",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 522
        },
        "outputId": "fe42fa81-94ef-4dd8-a7b5-8e9cf568071a"
      },
      "source": [
        "sample_means150 = [price.sample(n=150).mean() for i in range(0, 5000)]\n",
        "\n",
        "\n",
        "#plotting the data\n",
        "plt.rcParams['figure.figsize'] = (12,8)\n",
        "plt.hist(sample_means150, edgecolor = 'black', linewidth = 1.2, color='green')\n",
        "plt.title(\"SAMPLE_MEANS 3\", size = 20)\n",
        "plt.xlabel(\"Sample means\", size=15)\n",
        "plt.ylabel(\"Frequency\", size=15)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7wlalaXtvxnc",
        "colab_type": "text"
      },
      "source": [
        "It looks like the mean has shifted a little bit to the left and my assumption for the current sample price  would be 180700 for the new mean, the shape of the new mean sample size has lower spread since it has a larger size for each sample"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k95gOvAky3D8",
        "colab_type": "text"
      },
      "source": [
        "We would prefer a sample with a lower spread so that our estimations would be closer to the truth and a lower variable range\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Zfd5v59c0b_W",
        "colab_type": "text"
      },
      "source": [
        "Sample 3 (Sample Mean 150) had a lower spread and i would choose sample 3, because we wouldn't be to far from the truth when making our estimation"
      ]
    }
  ]
}