If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python. You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts. Develop your understanding of probability and statistics by writing and testing code Run experiments to test statistical behavior, such as generating samples from several distributions Use simulations to understand concepts that are hard to grasp mathematically Learn topics not usually covered in an introductory course, such as Bayesian estimation Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools Use statistical inference to answer questions about real-world data

《商务统计轻松学》系统地阐述了统计学的基本知识，通俗易懂，深入浅出，理论与实践相结合。所有的统计学基本概念都以简单明了的语言进行定义，并配以实例和说明进行阐释，使读者对概念一目了然，书中编排了丰富的实例和习题，其小“问题详解”不仅是统计理论的简单应用，还为读者指明了相应的解题思路和方法，并突出了如何使用Microsoft Excel工作表与统计计算器来解决统计问题，此外，《商务统计轻松学》还配有大量的网上资源和习题供读者下载练习使用。《商务统计轻松学》是那些畏惧统计学的人和在日常生活中将会用到统计学的人的最佳读本。 读者对象：大众。

This book provides a broad, mature, and systematic introduction to current financial econometric models and their applications to modeling and prediction of financial time series data. It utilizes real-world examples and real financial data throughout the book to apply the models and methods described. The author begins with basic characteristics of financial time series data before covering three main topics: Analysis and application of univariate financial time series The return series of multiple assets Bayesian inference in finance methods Key features of the new edition include additional coverage of modern day topics such as arbitrage, pair trading, realized volatility, and credit risk modeling; a smooth transition from S-Plus to R; and expanded empirical financial data sets. The overall objective of the book is to provide some knowledge of financial time series, introduce some statistical tools useful for analyzing these series and gain experience in financial applications of various econometric methods.

《试验应用统计·设计、创新和发现(原书第2版)》从试验工作者的角度阐述了统计方法在试验设计中的应用，强调科学地利用统计工作从试验数据中获取最大的信息。内容主要包括：基础知识、比较两总体、两水平因析设计、部分因析设计、因析设计及数据变换、变差的多种来源、最小二乘与试验设计的必要性、响应曲面的某些应用等。 《试验应用统计·设计、创新和发现(原书第2版)》内容丰富，从实际问题出发，分析各种方法的利弊，然后采用最佳统计方法解决问题，《试验应用统计·设计、创新和发现(原书第2版)》适合作为理工科各专业本科生，研究生的统计学教材，也可作为相关领域研究人员的参考读物。

This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.

统计计算是数理统计、计算数学和计算机科学的交叉学科。《统计计算》系统地介绍了统计计算的基本方法，并给出各种算法的统计原理和数值计算的步骤，以及部分例子，使读者掌握用统计方法解决具体问题的全过程. 《统计计算》内容包括误差与数据处理、分布函数和分位数的计算、随机数的产生与检验、矩阵计算、无约束最优化方法、多元线性和非线性回归的算法及随机模拟方法等.各章内容丰富，并配有适量的习题和上机实习题. 《统计计算》可作为理工科院校概率统计、数学、应用数学、计算机科学等系大学生的教材，也可作为教师、研究生以及从事统计、信息处理工作的有关工程技术人员的参考书。