Providing a much-needed bridge between elementary statistics courses and advanced research methods courses, Understanding Advanced Statistical Methods helps students grasp the fundamental assumptions and machinery behind sophisticated statistical topics, such as logistic regression, maximum likelihood, bootstrapping, nonparametrics, and Bayesian methods. The book teaches students how to properly model, think critically, and design their own studies to avoid common errors. It leads them to think differently not only about math and statistics but also about general research and the scientific method. With a focus on statistical models as producers of data, the book enables students to more easily understand the machinery of advanced statistics. It also downplays the "population" interpretation of statistical models and presents Bayesian methods before frequentist ones. Requiring no prior calculus experience, the text employs a "just-in-time" approach that introduces mathematical topics, including calculus, where needed. Formulas throughout the text are used to explain why calculus and probability are essential in statistical modeling. The authors also intuitively explain the theory and logic behind real data analysis, incorporating a range of application examples from the social, economic, biological, medical, physical, and engineering sciences. Enabling your students to answer the why behind statistical methods, this text teaches them how to successfully draw conclusions when the premises are flawed. It empowers them to use advanced statistical methods with confidence and develop their own statistical recipes. Ancillary materials are available on the book's website.
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flag已拔。想打六星的教材。作者很认真地说明了主流统计检验背后的motivation，而且一步一步推公式给读者看，从来不会跳步，还会详细解释中间每一步都发生了什么。章节安排也很独特，贝叶斯统计放在了frequentist统计前面，并不断穿插二者的对比。读完有种豁然开朗的感觉。
评分笔记整理中。非常厉害的解释。
评分flag已拔。想打六星的教材。作者很认真地说明了主流统计检验背后的motivation，而且一步一步推公式给读者看，从来不会跳步，还会详细解释中间每一步都发生了什么。章节安排也很独特，贝叶斯统计放在了frequentist统计前面，并不断穿插二者的对比。读完有种豁然开朗的感觉。
评分我见过的最好的统计学导论著作
评分后面关于假设检验的部分写的很赞。
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