Day_037_HW.ipynb

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    "## [作業重點]\n",
    "了解線性回歸的模型發展歷程，並了解優勢與劣勢，以及其使用情境"
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   "source": [
    "## 作業"
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    "請閱讀以下相關文獻，並回答以下問題\n",
    "\n",
    "[Linear Regression 詳細介紹](https://brohrer.mcknote.com/zh-Hant/how_machine_learning_works/how_linear_regression_works.html)\n",
    "\n",
    "[Logistics Regression 詳細介紹](https://medium.com/@yehjames/%E8%B3%87%E6%96%99%E5%88%86%E6%9E%90-%E6%A9%9F%E5%99%A8%E5%AD%B8%E7%BF%92-%E7%AC%AC3-3%E8%AC%9B-%E7%B7%9A%E6%80%A7%E5%88%86%E9%A1%9E-%E9%82%8F%E8%BC%AF%E6%96%AF%E5%9B%9E%E6%AD%B8-logistic-regression-%E4%BB%8B%E7%B4%B9-a1a5f47017e5)\n"
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    "1. 線性回歸模型能夠準確預測非線性關係的資料集嗎?\n",
    "2. 回歸模型是否對資料分布有基本假設?\n"
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    "> 1.只要因變量(y)為自變量的線性組合(wx+b)，就可以稱作線性迴歸」，這代表就算線條不是直的，也可能是線性迴歸  \n",
    "2.需要假設每次的取樣事件是獨立且分佈一至的"
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