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Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for the Social Sciences introduces statistical methods to students majoring i

本書以非技術性的角度介紹類別反應資料（或離散資料）的分析方法，包括二元反應或二項資料的邏輯迴歸、卜瓦松計數資料及列聯表的對數線性模型等方法。重點放在方法介紹及結果解釋，並不討論公式推導等技術細節。因此，這本書適合統計系大學部及非統計系所之類別資料分析及研究方法等相關課程；也適合所擔任工作中可能涉及資料分析和統計方法的實務工作者參考。書中有許多應用實例，來自醫學及流行病學、人口學、社會學、教育、交通

本書是一本入門書，它所闡釋的，不僅是統計的知識，更是一個方法論。第五章之後所談及的統計議題，都是社會科學研究所用到的工具，特別是第六章的假設檢定，更是不可或缺的。R.C.Merton說過一句話「有的模型都是錯的，但有一些很有用」。我們在這本書第十一章到第十五章迴歸模型並不能剛好精確的描述變數的關聯性，但大體上可以看到一些趨勢。

This manual contains completely worked-out solutions for all the odd-numbered exercises in the text.

Agresti and Finley present statistical methods in a style that emphasizes their concepts and their application to the social sciences rather than the mathematics and computational details behind them.

Agresti and Finley present statistical methods in a style that emphasizes their concepts and their application to the social sciences rather than the mathematics and computational details behind them.

For courses in Statistical Methods for the Social Sciences . Statistical methods applied to social sciences, made accessible to all through an emphasis on concepts Statistical Methods for t

Agresti and Finley present statistical methods in a style that emphasizes their concepts and their application to the social sciences rather than the mathematics and computational details behind them.

Designed as a textbook for a one or two-term introduction to mathematical statistics for students training to become data scientists, Foundations of Statistics for Data Scientists: With R and Python is an in-depth presentation of the topics in statistical science with which any data scientist should be familiar, including probability distributions, descriptive and inferential statistical methods, and linear modelling. The book assumes knowledge of basic calculus, so the presentation can focus on 'why it works' as well as 'how to do it.' Compared to traditional "mathematical statistics" textbooks, however, the book has less emphasis on probability theory and more emphasis on using software to implement statistical methods and to conduct simulations to illustrate key concepts. All statistical analyses in the book use R software, with an appendix showing the same analyses with Python.The book also introduces modern topics that do not normally appear in mathematical statistics texts but ar

Statistical science as organized in formal academic departments is relatively new. With a few exceptions, most Statistics and Biostatistics departments have been created within the past 60 years. Th

NOTE: This edition features the same content as the traditional text in a convenient, three-hole-punched, loose-leaf version. Books a la Carte also offer a great value–this format costs significantly

For courses in introductory statistics. The Art and Science of Learning from Data Statistics: The Art and Science of Learning from Data, Fourth Edition, takes a conceptual approach, helping students u

NOTE: This edition features the same content as the traditional text in a convenient, three-hole-punched, loose-leaf version. Books a la Carte also offer a great value–this format costs significantly