assignmentutor-lab™ 为您的留学生涯保驾护航 在代写数据可视化Data visualization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。我们的专家在代写数据可视化Data visualization代写方面经验极为丰富，各种代写数据可视化Data visualization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

## CS代写|数据可视化代写Data visualization代考|Selecting an Appropriate Chart

How do you choose an appropriate chart? If the goal of your chart is to explain, then the answer to this question depends on the message you wish to convey to your audience. If you are exploring data, the best chart type depends on the question you are asking and hope to answer from the data. Also, the type of data you have may influence your chart selection. A few of the more common goals for charts are to show the following:

• Composition-Composition is what makes up the whole of an entity under consideration. An example is the bar chart in Figure 2.2.
• Ranking-Ranking is the relative order of items. Figure $2.2$ is also an example of ranking, because we have sorted the categories by bar length, which is proportional to the amounts allocated.
• Correlation/Relationship-Correlation is how two variables are related to one another. An example of this is the relationship between average low temperature and average annual snowfall for various cities in the United States.
• Distribution-Distribution is how items are dispersed. An example of this is the number of calls received by a call center in a day, measured on an hourly basis.
The type of data you have should also influence your chart selection. For example, a bar or column chart is often an appropriate chart when we are summarizing data about categories. Students’ letter grades in a college course are categories. For summarizing the number of students earning each letter grade, a bar or column chart would be appropriate.
The relationship between two quantitative variables often makes a scatter chart an appropriate choice. Bar charts, scatter charts, and line charts with the horizontal axis being time, are often the best choice for time series data. If your data have a spatial component, a geographic map might be a good choice.

Creating great data visualizations is a skill that is best learned by doing. Therefore, before getting into more detail on the various types of charts and in what circumstances they are most appropriate, we provide detailed instructions on how to create and edit charts in Excel.

## CS代写|数据可视化代写Data visualization代考|Bar Charts

A bar chart shows a summary of categorical data using the length of horizontal bars to display the magnitude of a quantitative variable. That is, a bar chart is a column chart turned on its side. Like column charts, bar charts are useful for comparing categorical variables and are most effective when you do not have too many categories. Figure $2.2$ in the Data Visualization Makeover of the Allocation of Funds in New York City is a good example. As shown in that example, a bar chart can be a good substitute for a pie chart when showing composition. Sorting the data as in Figure $2.2$ makes the rank order of the components by the magnitude of the quantitative variable more obvious. A bar chart is preferred over a column chart if there are lengthy category names because it is easier to display the names horizontally (for improved legibility). However, for time series data, a column chart is better as it is more natural to display the passage of time from left to right horizontally.

A clustered bar chart displays multiple quantitative variables for categories or time pcriods using the length of horizontal bars to denotc the magnitudc of the quantitative variables and separate bars and colors to denote the different variables. Like a stacked column chart, a stacked bar chart is a bar chart that uses color to denote the contribution of each subcategory to the total. As with column charts, clustered and stacked bar charts are available in Excel by clicking on the Insert Column or Bar button in the Charts

# 数据可视化代考

## CS代写|数据可视化代写Data visualization代考|Selecting an Appropriate Chart

• Composition-Composition 构成了所考虑的实体的整体。一个例子是图 2.2 中的条形图。
• Ranking-Ranking是项目的相对顺序。数字2.2也是排名的一个例子，因为我们已经按照条形长度对类别进行了排序，条形长度与分配的数量成正比。
• 相关性/关系-相关性是两个变量相互关联的方式。这方面的一个例子是美国各个城市的平均低温和年平均降雪量之间的关系。
• Distribution-Distribution 是项目的分散方式。这方面的一个例子是呼叫中心在一天内接到的电话数量，按小时计算。
您拥有的数据类型也应该影响您的图表选择。例如，当我们汇总有关类别的数据时，条形图或柱形图通常是合适的图表。大学课程中学生的字母成绩属于类别。为了总结获得每个字母等级的学生人数，条形图或柱形图将是合适的。
两个定量变量之间的关系通常使散点图成为合适的选择。横轴为时间的条形图、散点图和折线图通常是时间序列数据的最佳选择。如果您的数据具有空间组件，则地图可能是一个不错的选择。

## 有限元方法代写

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## 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。

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