以下是使用等待统计信息分析SQLServer性能并排除故障的实用指南。学习如何准确地确定查询运行缓慢的原因。测量每个瓶颈所消耗的时间，以便您可以首先集中精力进行最大的改进。此版本被更新，以涵盖查询存储中等待统计信息的分析、CXCONSUMER等待事件以及SQLServer 2019年的最新情况。无论您是刚刚开始等待统计，还是已经熟悉这些统计信息，这本书提供了关于等待统计信息是如何生成的以及它们对SQL Server实例的性能意味着什么的更深入的理解。PRO SQL Server 2019等待统计不仅限于最常见的等待类型，还包括更复杂和更具性能威胁的等待类型。您将了解每个查询等待统计信息和基于会话的等待统计信息，以及它们各自可以帮助您解决的问题类型。不同的等待类型按其影响区域分类，包括CPU、IO、Lock等。本书提供了明确的示例，帮助您了解具体的等待时间增加或减少的原因和方式，以及它们如何影响SQLServer的性能。读完这本书后，你将不希望没有等待统计数据提供的有价值的信息，这些信息是关于您应该将有限的调优时间用于最大限度地提高性能和对您的业务的价值。

用wait statistics分析诊断 SQL Server 性能。找出查询慢的原因。对每个瓶颈计时以专注于做出最大的改进。这本书已经更新，讲述在Query Store分析wait statistics , CXCONSUMER wait 事件, 以及SQL Server 2019最新进展.

Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce, Andrew Bruce English | ISBN: 1491952962 | 2016 A key component of data science is statistics and machine learning, but only a small proportion of data scientists are actually trained as statisticians. This concise guide illustrates how to apply statistical concepts essential to data science, with advice on how to avoid their misuse. Many courses and books teach basic statistics, but rarely from a data science perspective. And while many data science resources incorporate statistical methods, they typically lack a deep statistical perspective. This quick reference book bridges that gap in an accessible, readable format.

Statistics on Special Manifolds，统计学的书 ，经典

Density Estimation for Statistics and Data Analysis， Silverman著， 1986年版，核密度估计教材

Review From the reviews: "Presuming no previous background in statistics and described by the author as "demanding" yet "understandable because the material is as intuitive as possible" (p. viii), this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." Technometrics, August 2004 "This book should be seriously considered as a text for a theoretical statsitics course for non-majors, and perhaps even for majors...The coverage of emerging and important topics is timely and welcomed...you should have this book on your desk as a reference to nothing less than 'All of Statistics.'" Biometrics, December 2004 "Although All of Statistics is an ambitious title, this book is a concise guide, as the subtitle suggests....I recommend it to anyone who has an interest in learning something new about statistical inference. There is something here for everyone." The American Statistician, May 2005 "As the title of the book suggests, ‘All of Statistics’ covers a wide range of statistical topics. … The number of topics covered in this book is vast … . The greatest strength of this book is as a first point of reference for a wide range of statistical methods. … I would recommend this book as a useful and interesting introduction to a large number of statistical topics for non-statisticians and also as a useful reference book for practicing statisticians." (Matthew J. Langdon, Journal of Applied Statistics, Vol. 32 (1), January, 2005) "This book was written specifically to give students a quick but sound understanding of modern statistics, and its coverage is very wide. … The book is extremely well done … ." (N. R. Draper, Short Book Reviews, Vol. 24 (2), 2004) "This is most definitely a book about mathematical statistics. It is full of theorems and proofs … . Presuming no previous background in statistics … this certainly would be my choice of textbook if I was required to learn mathematical statistics again for a couple of semesters." (Eric R. Ziegel, Technometrics, Vol. 46 (3), August, 2004) "The author points out that this book is for those who wish to learn probability and statistics quickly … . this book will serve as a guideline for instructors as to what should constitute a basic education in modern statistics. It introduces many modern topics … . Adequate references are provided at the end of each chapter which the instructor will be able to use profitably … ." (Arup Bose, Sankhya, Vol. 66 (3), 2004) "The amount of material that is covered in this book is impressive. … the explanations are generally clear and the wide range of techniques that are discussed makes it possible to include a diverse set of examples … . The worked examples are complemented with numerous theoretical and practical exercises … . is a very useful overview of many areas of modern statistics and as such will be very useful to readers who require such a survey. Library copies would also see plenty of use." (Stuart Barber, Journal of the Royal Statistical Society, Series A – Statistics in Society, Vol. 168 (1), 2005) Product Description This book is for people who want to learn probability and statistics quickly. It brings together many of the main ideas in modern statistics in one place. The book is suitable for students and researchers in statistics, computer science, data mining and machine learning. This book covers a much wider range of topics than a typical introductory text on mathematical statistics. It includes modern topics like nonparametric curve estimation, bootstrapping and classification, topics that are usually relegated to follow-up courses. The reader is assumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. The text can be used at the advanced undergraduate and graduate level. Larry Wasserman is Professor of Statistics at Carnegie Mellon University. He is also a member of the Center for Automated Learning and Discovery in the School of Computer Science. His research areas include nonparametric inference, asymptotic theory, causality, and applications to astrophysics, bioinformatics, and genetics. He is the 1999 winner of the Committee of Presidents of Statistical Societies Presidents' Award and the 2002 winner of the Centre de recherches mathematiques de Montreal-Statistical Society of Canada Prize in Statistics. He is Associate Editor of The Journal of the American Statistical Association and The Annals of Statistics. He is a fellow of the American Statistical Association and of the Institute of Mathematical Statistics.

python统计数据分析

Python for Probability,Statistics,and Machine Learning.pdf Python for Probability,Statistics,and Machine Learning.pdf

Introduction to probability and statistics 概率与统计的入门书籍，适合自学

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