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Do any values appear to stick out? Often such values tell us something interesting or exciting about the data. You should always point out any stragglers or outliers that stand off away from the body of the distribution. For example, if you’re studying the personal wealth of Americans and Bill Gates is in your sample, he would certainly be an outlier. Because his wealth would be so obviously atypical, you’d want to point it out as a special feature,

Outliers can affect almost every method we discuss in this book, so we’ll always be on the lookout for them. An outlier can be the most informative part of your data,or it might just be an error. Either way, you shouldn’t throw it away without comment. Treat it specially and discuss it when you report your conclusions about your data. (Or find the error and fix it if you can.)

How you characterize a distribution is often a judgment call. Do the two humps in the histogram really reveal two subgroups, or will the shape look different if you change the bin width slightly? Are those observations at the high end of the histogram truly unusual, or are they just the largest ones at the end of a long tail? These are matters of judgment on which different people can legitimately disagree. There’s no automatic calculation or rule of thumb that can make the decision for you. Understanding your data and how they arose can help. What should guide your decisions is an honest desire to understand what is happening in the data. That’s what you’ll need to make sound business decisions.

Viewing a histogram at several different bin widths can help you to see how persistent some of the features are. Some technologies offer ways to change the bin width interactively to get multiple views of the histogram. If the number of observations in each bin is so small that moving a couple of values to the next bin changes your assessment of how many modes there are, be careful. Be sure to think about the data, where they came from, and what kinds of questions you hope to answer from them.

Matlab代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。