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Exercise book of statistical inference

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Exercise book of statistical inference/ by Francesca Gasperoni, Francesca Ieva, Anna Maria Paganoni.
作者:	Gasperoni, Francesca.
其他作者:	Ieva, Francesca.
出版者:	Cham :Springer Nature Switzerland : : 2025.,
面頁冊數:	xi, 293 p. :ill. (some col.), digital ;24 cm.
內容註:	Part I: Statistical Inference -- 1 Fundamentals of Probability and Statistics -- 2 Sufficient, Minimal, and Complete Statistics -- 3 Point Estimators -- 4 Uniform Minimum Variance Unbiased Estimators (UMVUEs) -- 5 Likelihood Ratio Test -- 6 Uniformly Most Powerful Test -- 7 Confidence Intervals -- 8 Asymptotic Statistics -- Part II: Regression Models and Analysis of Variance -- 9 Linear Regression -- 10 Generalized Linear Models -- 11 ANOVA: Analysis of Variance -- 12 Summary Exercises -- Appendix A: Probability Distributions.
Contained By:	Springer Nature eBook
標題:	Mathematical statistics - Problems, exercises, etc. -
電子資源:	https://doi.org/10.1007/978-3-031-86670-8
ISBN:	9783031866708

Exercise book of statistical inference
Gasperoni, Francesca.

Exercise book of statistical inference[electronic resource] /by Francesca Gasperoni, Francesca Ieva, Anna Maria Paganoni. - Cham :Springer Nature Switzerland :2025. - xi, 293 p. :ill. (some col.), digital ;24 cm. - UNITEXT. La matematica per il 3+2,v. 1732038-5757 ;. - UNITEXT.La matematica per il 3+2 ;v. 173..

Part I: Statistical Inference -- 1 Fundamentals of Probability and Statistics -- 2 Sufficient, Minimal, and Complete Statistics -- 3 Point Estimators -- 4 Uniform Minimum Variance Unbiased Estimators (UMVUEs) -- 5 Likelihood Ratio Test -- 6 Uniformly Most Powerful Test -- 7 Confidence Intervals -- 8 Asymptotic Statistics -- Part II: Regression Models and Analysis of Variance -- 9 Linear Regression -- 10 Generalized Linear Models -- 11 ANOVA: Analysis of Variance -- 12 Summary Exercises -- Appendix A: Probability Distributions.

This book was created with the goal of helping students transition from the theoretical and methodological concepts of statistical inference to their implementation on a computer. The first part of the book is primarily focused on exercises to be solved with pen and paper, so that students can apply knowledge derived from lemmas and theorems; while the second part consists of labs, which involve both the manual implementation of algorithms and the learning of built-in tools for efficient analysis of datasets derived from real-world problems. To optimize the understanding of the topics developed and to guide the reader through their studies, the book is organized into chapters, each of which includes an introductory section that reviews the theoretical foundations of statistical inference, followed by a second part with exercises, each accompanied by a comprehensive solution on paper and, when appropriate, using software. This book is aimed at undergraduate students in Statistics, Mathematics, Engineering, and for graduate-level courses in Data Science.

ISBN: 9783031866708

Standard No.: 10.1007/978-3-031-86670-8doiSubjects--Topical Terms:

604071
Mathematical statistics
--Problems, exercises, etc.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Exercise book of statistical inference
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505 0 $a Part I: Statistical Inference -- 1 Fundamentals of Probability and Statistics -- 2 Sufficient, Minimal, and Complete Statistics -- 3 Point Estimators -- 4 Uniform Minimum Variance Unbiased Estimators (UMVUEs) -- 5 Likelihood Ratio Test -- 6 Uniformly Most Powerful Test -- 7 Confidence Intervals -- 8 Asymptotic Statistics -- Part II: Regression Models and Analysis of Variance -- 9 Linear Regression -- 10 Generalized Linear Models -- 11 ANOVA: Analysis of Variance -- 12 Summary Exercises -- Appendix A: Probability Distributions.
520 $a This book was created with the goal of helping students transition from the theoretical and methodological concepts of statistical inference to their implementation on a computer. The first part of the book is primarily focused on exercises to be solved with pen and paper, so that students can apply knowledge derived from lemmas and theorems; while the second part consists of labs, which involve both the manual implementation of algorithms and the learning of built-in tools for efficient analysis of datasets derived from real-world problems. To optimize the understanding of the topics developed and to guide the reader through their studies, the book is organized into chapters, each of which includes an introductory section that reviews the theoretical foundations of statistical inference, followed by a second part with exercises, each accompanied by a comprehensive solution on paper and, when appropriate, using software. This book is aimed at undergraduate students in Statistics, Mathematics, Engineering, and for graduate-level courses in Data Science.
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