电子工程代写|面向对象的系统设计代写Object-Oriented Systems Design代考|CSE522

statistics-lab™ 为您的留学生涯保驾护航 在代写面向对象的系统设计Object-Oriented Systems Design方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写面向对象的系统设计Object-Oriented Systems Design方面经验极为丰富，各种代写面向对象的系统设计Object-Oriented Systems Design相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

电子工程代写|面向对象的系统设计代写Object-Oriented Systems Design代考|Meaningful Thresholds

Statistics-based thresholds are useful for most metrics, but for some others they are implicitly given by observations. In that sense they are also based on statistics, but their values have become part of our culture. Therefore we do not need to statistically measure them, but we can infer them from common knowledge.

Example. If we think about the maximum nesting level of statements in a method it is clear that 0 denotes a method without any conditional statements and 1,2 or 3 would mean that there is some nesting but it is quite shallow; but if the maximum nesting level gets higher than that we know that the method has a deep nesting level and following the control flow is harder.

We identified two cases of thresholds based on meanings that are generally accepted and easy to understand: (1) commonly-used fraction thresholds and (2) thresholds with generally-accepted meaning.
Common Fraction Thresholds
Quiz. Which of the following (fractional) numbers can you mentally associate with a semantic: 0.07; 0.39; 0.75; 0.33; 0.72?

We guess you picked $0.75$ because it means three quarters; and you also picked $0.33$ because it is one third. We guess that while looking at $0.72$ you thought: “it is close to three quarters”. Normalized metrics thus have thresholds which seem natural to us. We summarized them in Table $2.3 .$

电子工程代写|面向对象的系统设计代写Object-Oriented Systems Design代考|Visualizing Metrics and Design

Characterizing, evaluating and improving the design of large-scale system is a highly complex enterprise, and while metrics are a highly needed means for this purpose, they must be used in conjunction with further techniques to handle this level of complexity. In our opinion the most adequate means to complement metrics is visualization, as it has long been adopted as a means to break down the complexity of information.

The goal of visualization in general is to visualize any kind of data. Applications in visualization are so frequent and common, that most people do not notice them: examples include meteorology (weather maps), geography (street maps), geology, medicine (computer-aided displays to show the inner of the human body), transportation (train tables and metro maps), etc..

It is easy to assess that the cylinder on the right has the largest diameter, the one in the middle has the greatest height, while the one on the left has the smallest diameter. Why is that? Human perception allows us to perform such non-trivial analysis as an in-grained mechanism, despite the fact that we had no numbers to hand. However, when provided with a table containing metric information (height, diameter, weight) for the cylinders we have no problem assigning those numbers. Do we? There is a problem with the weight metric which confuses us. Why? It does not respect the so-called representation condition.

In measurement theory, the procedure of rendering metrics on visual characteristics of representations is called measurement mapping, and must fulfill the representation condition, which asserts that “a measurement mapping $M$ must map entities into numbers and empirical relations into numerical relations in such a way that the empirical relations preserve and are preserved by the numerical relations” [FP96]. In other words, if a number $a$ is greater than a number $b$, the graphical representations of $a$ and $b$ must preserve this fact.
The reader must be aware that visualization does not provide a means to visualize every metric. The provided weight metrics above actually confuse us because we would think that the smallest cylinder would also be the lightest. In that sense, at least regarding the weight, the above visualization does not completely respect the representation condition.

广义线性模型代考

statistics-lab作为专业的留学生服务机构，多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务，包括但不限于Essay代写，Assignment代写，Dissertation代写，Report代写，小组作业代写，Proposal代写，Paper代写，Presentation代写，计算机作业代写，论文修改和润色，网课代做，exam代考等等。写作范围涵盖高中，本科，研究生等海外留学全阶段，辐射金融，经济学，会计学，审计学，管理学等全球99%专业科目。写作团队既有专业英语母语作者，也有海外名校硕博留学生，每位写作老师都拥有过硬的语言能力，专业的学科背景和学术写作经验。我们承诺100%原创，100%专业，100%准时，100%满意。

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。