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[null,"1.","Record Nr.",null,"UNINA9911007457403321"]
[null,null,"Autore",null,"Gasperoni Francesca"]
[null,null,"Titolo",null,"Exercise Book of Statistical Inference / / by Francesca Gasperoni, Francesca Ieva, Anna Maria Paganoni"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"3-031-86670-3"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (346 pages)"]
[null,null,"Collana",null,"La Matematica per il 3+2, , 2038-5757 ; ; 173"]
[null,null,"Disciplina",null,"519.5"]
[null,null,"Soggetti",null,"Statistics"]
[{"$nulls":4},"Probabilities"]
[{"$nulls":4},"Mathematics"]
[{"$nulls":4},"Statistical Theory and Methods"]
[{"$nulls":4},"Probability Theory"]
[{"$nulls":4},"Estadística"]
[{"$nulls":4},"Probabilitats"]
[{"$nulls":4},"Problemes i exercicis"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"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."]
[null,null,null,"Sommario/riassunto","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 "]
[{"$nulls":4},"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."]