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Introductory self-learning text for interval estimation and hypothesis testing, which are the two-central topics in frequentist statistics

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NobuhiroMoteki/Introduction-to-Statistics

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Introduction-to-Statistics

📌 Description

Self-learning text (in Japanese) of frequentist statistics (Interval estimation and Hypothesis testing) with computational demo with Python.

📚Contents

1. 確率変数 pp. 1-13 
  1.1. 離散分布 
  1.2. 連続分布 
  1.3. 数値積分 
  1.4. 統計量  
  1.5. 乱数 
  1.6. 中心極限定理 

2. 区間推定と仮説検定 pp. 14-23 
  2.1. 方法論 
  2.2. スチューデントの T 
  2.3. 正規母集団の母平均の推定 
    2.3.1. 区間推定と仮説検定の共通事項 
    2.3.2. 区間推定 
    2.3.3. 仮説検定 
    2.3.4. 区間推定と仮説検定の関係 
    2.3.5. 点推定の標準誤差と区間推定の関係 
    2.3.6. 2つの正規母集団の母平均の差の推定 

3. 母集団が未知のときの統計解析 pp. 24-33 
  3.1. Bootstrap 原理 
  3.2. BS 区間推定法 
  3.3. 2 つの未知母集団の比較(Permutation 検定) 
  
付録 A-G, 参考文献 pp. 34-46

🔧 Usage

You need to install Python >= 3.9 and numpy,scipy,matplotlib, ipykernel modules for running the demo codes (JupyterNotebook files).

📝 License

This project is licensed under the MIT License. See the LICENSE file for details.

📢 Author

Name: Nobuhiro Moteki GitHub: @NobuhiroMoteki Email: nobuhiro.moteki@gmail.com

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Introductory self-learning text for interval estimation and hypothesis testing, which are the two-central topics in frequentist statistics

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