### 统计代写|数据可视化作业代写data visualization代考| Preattentive Attributes

statistics-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

## 统计代写|数据可视化作业代写data visualization代考|Color

In terms of data visualization, color includes the attributes of hue, saturation, and luminance. Figure $3.6$ displays the difference between these aspects of color. Hue refers to what we typically think of as the basis of different colors, for example, red versus blue versus orange. In technical terms, the hue is defined by the position the light occupies on the visible light spectrum. Saturation refers to the intensity or purity of the color, which is defined as the amount of gray in the color. Luminance refers to the amount of black versus white within the color.

Hue, saturation, and luminance can each be used to draw the user’s attention to specific parts of a data visualization and to differentiate among values in a visualization. Using differences in hue in a data visualization creates bold, stark contracts while changing the saturation or luminance creates softer, less stark contrasts.

Color can be an extremely effective attribute to use to differentiate particular aspects of data in a visualization. However, one must be careful not to overuse color as it can become distracting in a visualization. It should also be noted that many people suffer from colorblindness, which affects their ability to differentiate between some colors.

## 统计代写|数据可视化作业代写data visualization代考|Form

Form includes the preattentive attributes of orientation, size, shape, length, and width. Each of these attributes can be used to call attention to a particular aspect of a data visualization. Figure $3.7$ shows an example for each of these form related preattentive attributes.

Orientation refers to the relative positioning of an object within a data visualization. It is a common preattentive attribute present in line graphs. Consider the chart in Figure $3.8$ that visualizes sales of a specific form of syringe that is used for administering insulin to diabetic patients in Europe and the United States. The difference in the orientation of these lines makes it easy for the audience to perceive that sales in Europe are increasing at a much faster rate than the United States for the years 2019 and 2020 .

Because the slope of the line for sales in Europe is much steeper than the slope of the line for sales in the United States, the orientation of these lines is different. Therefore, we quickly perceive that sales in Europe have increased much faster than in the United States since $2019 .$

Size refers to the relative amount of 2D space that an object occupies in a visualization. One must be careful with the use of size in data visualizations because humans are not particularly good at judging relative differences in the 2D sizes of objects. Consider Figure $3.9$, which shows a pair of squares and a pair of circles. Try to determine how much larger the bigger square and bigger circle are than their smaller counterparts.

Both the bigger square and bigger circle are nine times larger than their smaller counterparts in terms of area. Most people are not good at estimating this relative size difference, so we must be careful when using the attribute of size to convey information about relative amounts.

The difficulty most people have in estimating relative differences in $2 \mathrm{D}$ size is a major reason why the use of pie charts in a data visualization is generally not recommended.
There are often alternatives to a pie chart that do not rely as heavily on the attribute of size to convey relative differences in amounts.
Shape refers to the type of object used in a data visualization. Contrary to size and orientation, the preattentive attribute of shape does not usually convey a sense of quantitative amount. In a line graph, the orientation of a line (going up, staying flat, or going down) generally provides a sense of a quantitative change in amount. For size, most people assume that a larger object conveys a larger quantitative amount. In general, most shapes do not specifically correspond to certain quantitative amounts. Nevertheless, shape can be effectively used to draw attention in a visualization or as a way to group common items and distinguish between items from different groups.

Figure $3.10$ uses the attributes of color and shape to show how items are grouped. For example, suppose these 20 items represented 20 employees of a company. In Figure $3.10$ a we use the preattentive attribute of color to divide the items into three different groups, or categories: orange, blue, and black. For example, color could represent the type of educational degree the corresponding employee has earned. Orange could represent a business degree, blue could represent an engineering degree, and black could represent any other degree. In Figure $3.10 \mathrm{~b}$, we use the preattentive attribute of shape to divide the items into three groups: circle, square, and triangle. For example, shape could represent the highest educational degree level that the corresponding employee has earned. A circle could represent a bachelor’s degree, a triangle could represent a master’s degree, and black could represent a doctorate degree. In either case, the mind can quickly process these visualizations and divide the items into their distinct groups. Figure $3.10 \mathrm{c}$ uses both attributes, color and shape, to group items into nine groups-each combination of color (degree type) and shape (degree level). It requires a much higher cognitive load here to determine which items are in the same group. This illustrates why we have to be careful not to overuse combinations of preattentive attributes, or else we lose the ability for our mind to quickly recognize these.

## 统计代写|数据可视化作业代写data visualization代考|Length and Width

When we refer to the prealtentive attributes ol length and widh for datal visualization, we are generally referring to their use with lines, bars, or columns. Therefore, length refers to the horizontal, vertical, or diagonal distance of a line or bar/column while width refers to the thickness of the line or bar/column (see Figure 3.7). Length is useful for illustrating quantitative values because a longer line corresponds to a larger value. Length is used extensively in bar and column charts to visualize data. Because it is much easier to compare relative lengths than relative sizes, bar and column charts are often preferred to pie charts for visualizing data. Consider data on the number of accounts managed by eight account managers. Figure $3.11$ displays these same data as a pie chart (using size of pie pieces to indicate number of accounts and color to indicate the manager) and a bar chart (using length of bars to indicate number of accounts and labels on the vertical axis to indicate the manager).

It is much easier to see that the manager with the most accounts managed is Elijah and that Kate manages the second-most accounts from the bar chart in Figure $3.11 \mathrm{~b}$ than from the pie chart in Figure 3.11a. We could make the bar chart even easier to interpret by sorting the bars by their length with the longest bar on top and the shortest bar on the bottom by using the Excel Sort function as described in the following steps. The data appear in Figure 3.12.

## 广义线性模型代考

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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。