Building Statistical Models in Python: Develop useful models for regression, classification, time series, and survival analysis

Make data-driven, informed decisions and enhance your statistical expertise in Python by turning raw data into meaningful insights

Purchase of the print or Kindle book includes a free PDF eBook

Key Features:

• Gain expertise in identifying and modeling patterns that generate success
• Explore the concepts with Python using important libraries such as stats models
• Learn how to build models on real-world data sets and find solutions to practical challenges

Book Description:

The ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation.

This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you'll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you'll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more.

By the end of this book, you'll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.

What You Will Learn:

• Explore the use of statistics to make decisions under uncertainty
• Answer questions about data using hypothesis tests
• Understand the difference between regression and classification models
• Build models with stats models in Python
• Analyze time series data and provide forecasts
• Discover Survival Analysis and the problems it can solve

Who this book is for:

If you are looking to get started with building statistical models for your data sets, this book is for you! Building Statistical Models in Python bridges the gap between statistical theory and practical application of Python. Since you'll take a comprehensive journey through theory and application, no previous knowledge of statistics is required, but some experience with Python will be useful.

以精準和專業的口吻翻譯如下:

「以Python將原始數據轉化為有意義的洞察力，做出數據驅動的明智決策，並提升您的統計專業知識。購買印刷版或Kindle電子書，即可免費獲得PDF電子書。

主要特點:
- 獲得識別和建模成功模式的專業知識
- 使用Python和重要的庫（如stats models）探索概念
- 學習如何在實際挑戰中建立模型並找到解決方案

書籍描述:
精通統計建模是數據科學家的基本技能，對於依賴數據洞察力的企業來說也是必不可少的。《使用Python建立統計模型》是一本全面的指南，將使您能夠在數據評估、理解和推斷生成中利用數學和統計原則。

本書不僅為您提供了應對統計建模複雜性的技能，還通過實例提供了實際指導，以便立即實施。通過強調應用和代碼示例，您將在獲得實踐經驗的同時理解概念。借助Python及其重要庫的幫助，您將探索關鍵的統計模型，包括假設檢驗、回歸、時間序列分析、分類等。

通過閱讀本書，您將在統計建模方面獲得流利，同時充分利用Python豐富的數據分析生態系統。

學到什麼:
- 探索使用統計學在不確定性下做出決策
- 使用假設檢驗回答有關數據的問題
- 理解回歸和分類模型的區別
- 在Python中使用stats models建立模型
- 分析時間序列數據並提供預測
- 發現生存分析及其解決的問題

適合對於為數據集建立統計模型感興趣的讀者，本書將填補統計理論和Python實際應用之間的差距。雖然不需要具備統計學的先備知識，但具有一些Python經驗將會有所幫助。」