## 统计代写|数据可视化代写Data visualization代考|DTSA5304

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

## 统计代写|数据可视化代写Data visualization代考|Related Work

First approaches in brain mapping used rigid models and spatial distributions. In [26], a stereotactic atlas is expressed in an orthogonal grid system, which is rescaled to a patient brain, assuming one-to-one correspondences of specific landmarks. Similar approaches are discussed in $[2,5,11]$ using elastic transformations. The variation in brain shape and geometry is of significant extent between different individuals of one species. Static rigid models are not sufficient to describe appropriately such inter-subject variabilities.

Deformable models were introduced as a means to deal with the high complexity of brain surfaces by providing atlases that can be elastically deformed to match a patient brain. Deformable models use snakes [20], B-spline surfaces [24], or other surface-based deformation algorithms $[8,9]$. Feature matching is performed by minimizing a cost function, which is based on an error measure defined by a sum measuring deformation and similarity. The definition of the cost function is crucial. Some approaches rely on segmentation of the main sulci guided by a user [4, 27, 29], while others automatically generate a structural description of the surface.

Level set methods, as described in [21], are widely used for convex shapes. These methods, based on local energy minimization, achieve shape recognition requiring little known information about the surface. Initialization must be done close to surface boundaries, and interactive seed placement is required. Several approaches have been proposed to perform automatically the seeding process and adapt the external propagation force [1], but small features can still be missed. Using a multiresolution representation of the cortical models, patient and atlas meshes are matched progressively by the method described in [16]. Folds are annotated according to size at a given resolution. The choice of the resolution is crucial. It is not guaranteed that same features are present at the same resolution for different brains.

Many other automatic approaches exist, including techniques using active ribbons [10, 13], graph representations [3, 22], and region growing [18]. A survey is provided in [28]. Even though some of the approaches provide good results, the highly non-convex shape of the cortical surface, in combination with inter-subject variability and feature-size variability, leads to problems and may prevent a correct feature recognition/segmentation and mapping without user intervention.

Our approach is an automated approach that can deal with highly non-convex shapes, since we segment the brain into cortical regions, and with feature-size as well as inter-subject variability, since it is based on discrete curvature behavior. Moreover, isosurface extraction, surface segmentation, and topology graphs are embedded in a graphical system supporting visual understanding.

## 统计代写|数据可视化代写Data visualization代考|Brain Mapping

Our brain mapping approach is based on a pipeline of automated steps. Figure 1 illustrates the sequence of individual processing steps.

The input for our processing pipeline is discrete imaging data in some raw format. Typically, imaging techniques produce stacks of aligned images. If the images are not aligned, appropriate alignment tools must be applied [25]. Volumetric reconstruction results in a volume data set, a trivariate scalar field.

Depending on the used imaging technique, a scanned data set may contain more or less noise. We mainly operate on fMRI data sets, thus having to deal with significant noise levels. We use a three-dimensional discrete Gaussian smoothing filter, which eliminates high-frequency noise without affecting visibly the characteristics of the three-dimensional scalar field. The size of the Gaussian filter must be small. We use a $3 \times 3 \times 3$ mask locally to smooth every value of a rectilinear, regular hexahedral mesh. Figure 2 shows the effect of the smoothing filter applied to a three-dimensional scalar field by extracting isosurfaces from the original and filtered data set.

After this preprocessing step, we extract the geometry of the brain cortex from the volume data. The boundary of the brain cortex is obtained via an isosurface extraction step, as described in Sect. 4. If desired, isosurface extraction can be controlled and supervised in a fashion intuitive to neuroscientists.

Once the geometry of the brain cortices is available for both atlas brain and a user brain, the two surfaces can be registered. Since our brain mapping approach is feature-based, we perform the registration step by a simple and fast rigid body transformation. For an overview and a comparison of rigid body transformation methods, we refer to [6].

## 有限元方法代写

tatistics-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。

## 统计代写|数据可视化代写Data visualization代考|CSE512

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

## 统计代写|数据可视化代写Data visualization代考|Williams’ Convexification Framework

In his seminal paper [41] on techniques for computing visibility orderings for meshes, Williams discusses the problem of handling non-convex meshes (Sect. 9). (Also related is Sect. 8, which contains a discussion of cycles and the use of Delaunay triangulations.) After explaining some challenges of using his visibility sorting algorithm on non-convex meshes, Williams says:
“Therefore, an important area of research is to find ways to convert nonconvex meshes into convex meshes, so that the regular MPVO algorithm can be used.”
Williams proposes two solution approaches to the problem; each relies on “treating the voids and cavities as ‘imaginary’ cells in the mesh.” Basically, he proposes that such non-convex regions could be either triangulated or decomposed into convex pieces, and their parts marked as imaginary cells for the purpose of rendering. Implementing this “simple idea” is actually not easy. In fact, after discussing this general approach, Williams talks about some of the challenges, and finishes the section with the following remark:
“The implementation of the preprocessing methods, described in this section, for converting a non-convex mesh into a convex mesh could take a very significant amount of time; they are by no means trivial. The implementation of a 3D conformed Delaunay triangulation is still a research question at this time.”
In fact, Williams does not provide an implementation of any of the two proposed convexification algorithms. Instead, he developed a variant of MPVO that works on non-convex meshes at the expense of not being guaranteed to generate correct visibility orders.

The first convexification technique that Williams proposes is based on triangulating the data using a conforming Delaunay triangulation. The idea here is to keep adding more points to the dataset until the original triangulation becomes a Delaunay triangulation. This is discussed in more details in the next section.

The second technique Williams sketches is based on the idea of applying a decomposition algorithm to each of the non-convex polyhedra that constitute the set $\mathrm{CH}(\mathrm{S}) \backslash S$, which is the set difference between the convex hull of the mesh and the mesh itself. In general, $\mathrm{CH}(\mathrm{S}) \backslash S$ is a union of highly non-convex polyhedra of complex topology. Each connected component of $\mathrm{CH}(\mathrm{S}) \backslash S$ is a non-convex polyhedron that can be decomposed into convex polyhedra (e.g., tetrahedra) using, for example, the algorithm of Chazelle and Palios [10], which adds certain new vertices (Steiner points), whose number depends on the number of “reflex” edges of the polyhedron. In general, non-convex polyhedra require the addition of Steiner points in order to decompose them; in fact, it is NP-complete to decide if a polyhedron can be tetrahedralized without the addition of Steiner points.

## 统计代写|数据可视化代写Data visualization代考|Issues

Achieving Peter Williams’s vision of a simple convexification algorithm is much harder than it appears at first. The problem is peculiar since we start with an existing 3D mesh (likely to be a tetrahedralization) that contains not only vertices, edges, and triangles, but also volumetric cells, which need to be respected. Furthermore, the mesh is not guaranteed to respect global geometric criteria (e.g., of being Delaunay). Most techniques need to modify the original mesh in some way. The goal is to “disturb” it as little as possible, preserving most of its original properties.
In particular, several issues need to be considered:
Preserving Acyclicity. Even if the original mesh has no cycles, the convexification process can potentially cause the resulting convex mesh to contain cycles. Certain techniques, such as constructing a conforming Delaunay tetrahedralization, are guaranteed to generate a cycle-free mesh. Ideally, the convexification procedure will not create new cycles in the mesh.

Output Size. For the convexification technique to be useful the number of cells added by the algorithm needs to be kept as small as possible. Ideally, there is a provable bound on the number of cells as well as experimental evidence that for typical input meshes, the size of the output mesh is not much larger than the input mesh (i.e., the set of additional cells is small).

Computational and Memory Complexity. Other important factors are the processing time and the amount of memory used in the algorithm. In order to be practical on the meshes that arise in computational experiments (having on the order of several thousand to a few million cells), convexification algorithms must run in near-linear time, in practice.

Boundary and Interior Preservation. Ideally, the convexification procedure adds cells only “outside” of the original mesh. Furthermore, the newly created cells should exactly match the original boundary of the mesh. In general, this is not feasible without subdividing or modifying the original cells in some way (e.g., to break cycles, or to add extra geometry in order to respect the Delaunay empty-circumsphere condition). Some techniques will only need to modify the cells that are at or near the original boundary while others might need to perform more global modifications that go all the way “inside” the original mesh. One needs to be careful when making such modifications because of issues related to interpolating the original data values in the mesh. Otherwise, the visualization algorithm may generate incorrect pictures leading to wrong comprehension.

Robustness and Degeneracy Handling. It is very important for the convexification algorithms to handle real data. Large scientific datasets often use floating-point precision for specifying vertices, and are likely to have a number of degeneracies. For instance, these datasets are likely to have many vertices (sample points) that are coplanar, or that lie on a common cylinder or sphere, etc., since the underlying physical model may have such features.

## 统计代写|数据可视化代写Data visualization代考|Williams’ Convexification Framework

“因此，一个重要的研究领域是找到将非凸网格转换为凸网格的方法，以便可以使用常规的MPVO算法。”

“本节中描述的将非凸网格转换为凸网格的预处理方法的实现可能需要非常多的时间;它们绝不是微不足道的。目前，实现三维符合Delaunay三角剖分仍然是一个研究问题。”

Williams提出的第一种凸化技术是基于使用符合的Delaunay三角剖分法对数据进行三角剖分。这里的想法是不断向数据集中添加更多的点，直到原始三角剖分变成Delaunay三角剖分。下一节将对此进行更详细的讨论。

Williams概述的第二种技术是基于将分解算法应用于构成集合$\ mathm {CH}(\ mathm {S}) \反斜杠S$的每个非凸多面体的思想，这是网格的凸壳和网格本身之间的集合差。一般来说，$\ mathm {CH}(\ mathm {S}) \反斜杠S$是复拓扑的高度非凸多面体的并。$\ mathm {CH}(\ mathm {S}) \反斜线S$的每个连通成分都是一个非凸多面体，可以使用例如Chazelle和Palios[10]的算法分解为凸多面体(例如，四面体)，该算法添加了某些新顶点(斯坦纳点)，其数量取决于多面体的“反射”边的数量。一般来说，非凸多面体需要添加斯坦纳点来分解它们;事实上，在不加施泰纳点的情况下判定多面体是否可以四面体是np完全的。

## 有限元方法代写

tatistics-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。

## 统计代写|数据可视化代写Data visualization代考|INFS6023

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

Given a set of function values $f_0, f_1 \ldots f_n$ at positions $x_0, x_1 \ldots x_n$, we create a quadratic function that passes through the end points and approximates the remaining data values.

The quadratic function $C(t)$ we use to approximate the function values along an edge is defined as
$$C(t)=\sum_{i=0}^2 c_i B_i^2(t)$$
The quadratic Bernstein polynomial $B_i^2(t)$ is defined as
$$B_i^2(t)=\frac{2 !}{(2-i) ! i !}(1-u)^{2-i} u^i$$

First we parameterize the data by assigning parameter values $t_0, t_1 \ldots t_n$ in the interval $[0,1]$ to the positions $x_0, x_1 \ldots x_n$. Parameter values are defined with a chordlength parameterization as
$$t_i=\frac{x_i-x_0}{x_n-x_0}$$
Next, we solve a least-squares approximation problem to determine the coefficients $c_i$ of $C(t)$. The resulting overdetermined system of linear equations is
$$\left[\begin{array}{ccc} \left(1-t_0\right)^2 & 2\left(1-t_0\right) t_0 & t_0^2 \ \left(1-t_1\right)^2 & 2\left(1-t_1\right) t_1 & t_1^2 \ \vdots & \vdots & \vdots \ \left(1-t_n\right)^2 & 2\left(1-t_n\right) t_n & t_n^2 \end{array}\right]\left[\begin{array}{c} c_0 \ c_1 \ c_2 \end{array}\right]=\left[\begin{array}{c} f_0 \ f_1 \ \vdots \ f_n \end{array}\right] .$$
Constraining $C(t)$, so that it interpolates the endpoint values, i.e. $C(0)=f_0$ and $C(1)=f_n$, leads to the system
$$\begin{gathered} {\left[\begin{array}{c} 2\left(1-t_1\right) t_1 \ 2\left(1-t_2\right) t_2 \ \vdots \ 2\left(1-t_{n-1}\right) t_{n-1} \end{array}\right]\left[c_1\right]=} \ {\left[\begin{array}{c} f_1-f_0\left(1-t_1\right)^2-f_n t_1^2 \ f_2-f_0\left(1-t_2\right)^2-f_n t_2{ }^2 \ \vdots \ f_{n-1}-f_0\left(1-t_{n-1}\right)^2-f_n t_{n-1}{ }^2 \end{array}\right]} \end{gathered}$$
for the one degree of freedom $c_1$.

## 统计代写|数据可视化代写Data visualization代考|Approximating a Dataset

A quadratic approximation of a dataset is created by approximating the data values along each edge in the tetrahedral mesh with a quadratic function as described in Sect. 4.1. Each linear tetrahedron becomes a quadratic tetrahedron. The resulting approximation is $C^1$-continuous within a tetrahedron and $C^0$-continuous on shared faces and edges. The approximation error $e_a$ for a tetrahedron $T$ is the maximum difference between the quadratic approximation over $T$ and all original data values associated with points inside and on $T$ ‘ $s$ boundary.

In tetrahedral meshes created by longest-edge bisection, each edge $E$ in the mesh, except for the edges at the finest level of the mesh, is the split edge of a diamond $D$, see [5], and is associated with a split vertex $S V$. The computed coefficient $c_1$ for the edge $E$ is stored with the split vertex $S V$. The edges used for computing the quadratic representation can be enumerated by recursively traversing the tetrahedral mesh and examining the refinement edges. This process is illustrated for the $2 \mathrm{D}$ case in Fig. 2 . Since quadratic tetrahedra have three coefficients along each edge, the leaf level of a mesh with quadratic tetrahedra is one level higher in the mesh than the leaf level for linear tetrahedra, see Fig. 3.

In summary, we construct a quadratic approximation of a volume data set as follows:

1. For each edge of the mesh hierarchy, approximate the data values along the edge with a quadratic function that passes through the endpoints.
2. For each tetrahedron in the hierarchy, construct a quadratic tetrahedron from the six quadratic functions along its edges.
3. Compute the approximation error $e_a$ for each tetrahedron.

## 数据可视化代考

$$C(t)=\sum_{i=0}^2 c_i B_i^2(t)$$

$$B_i^2(t)=\frac{2 !}{(2-i) ! i !}(1-u)^{2-i} u^i$$

$$t_i=\frac{x_i-x_0}{x_n-x_0}$$

$$\left[\begin{array}{ccc} \left(1-t_0\right)^2 & 2\left(1-t_0\right) t_0 & t_0^2 \ \left(1-t_1\right)^2 & 2\left(1-t_1\right) t_1 & t_1^2 \ \vdots & \vdots & \vdots \ \left(1-t_n\right)^2 & 2\left(1-t_n\right) t_n & t_n^2 \end{array}\right]\left[\begin{array}{c} c_0 \ c_1 \ c_2 \end{array}\right]=\left[\begin{array}{c} f_0 \ f_1 \ \vdots \ f_n \end{array}\right] .$$

$$\begin{gathered} {\left[\begin{array}{c} 2\left(1-t_1\right) t_1 \ 2\left(1-t_2\right) t_2 \ \vdots \ 2\left(1-t_{n-1}\right) t_{n-1} \end{array}\right]\left[c_1\right]=} \ {\left[\begin{array}{c} f_1-f_0\left(1-t_1\right)^2-f_n t_1^2 \ f_2-f_0\left(1-t_2\right)^2-f_n t_2{ }^2 \ \vdots \ f_{n-1}-f_0\left(1-t_{n-1}\right)^2-f_n t_{n-1}{ }^2 \end{array}\right]} \end{gathered}$$

## 有限元方法代写

tatistics-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。

## 统计代写|数据可视化代写Data visualization代考|STAT1100

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

## 统计代写|数据可视化代写Data visualization代考|The Laws of Motion

There is, in nature, perhaps nothing older than motion, concerning which the books written by philosophers are neither few nor small; nevertheless I have discovered by experiment some properties of it which are worth knowing and which have not hitherto been either observed or demonstrated.

Galileo’s seventeenth-century observations foreshadow the origins of the cinema, computer-animated films, and-most relevant to this narrativedynamic data graphics. The popularity of modern dynamic data displays can be traced to scientific questions about the nature of human and animal motion. As technology developed, the study of motion and its visualization branched out from a pleasurable pastime to the huge industries of Hollywood and Netflix while also having important scientific applications in aerodynamics (the wind tunnel), medical imaging (blood flow in the heart and brain), and ecology (migratory patterns of animal species) among others

Aristotle’s De Motu Animalium (The Movement of Animals) was the first book setting out the principles of animal locomotion. Nichole Oresme’s 1360 “pipes” diagram (see Figure 2.2) was intended to show some possible mathematical relations between time and distance traveled. Around 1517, Leonardo da Vinci drew detailed anatomical studies of moving cats, horses, and dragons; Galileo later conducted experiments on motion and gravity between 1633 and 1642. However, the modern interest in these questions arose in the late 1800 s when new technologies for recording could provide new insights.
To a physicist, motion is nothing more interesting than a change in position over time. It can be reduced to simple, but elegant, equations giving velocity (the first derivative) and acceleration (the second derivative). A velocity, $v$, of a horse galloping at $45 \mathrm{mph}$ can be reduced to the equation $v=d x / d t=45$. The acceleration, $g$, due to gravity on Earth can very nearly be reduced to a constant, ${ }^2 \mathrm{~g}=9.8 \mathrm{~m} / \mathrm{s}^2$, or $32 \mathrm{ft} / \mathrm{s}^2$.

But to a ballet dancer, the art is in getting all the body parts to do those things in sync with a musical score to tell a wordless story of emotion ${ }^3$ entirely through change in position over time. In data visualization, as in physics and ballet, motion is a manifestation of the relation between time and space, and so the recording and display of motion added time as a fourth dimension to the abstract world of data. We focus here on a few developments that led to the visual depiction, understanding, and explanation of time-changing phenomena.

## 统计代写|数据可视化代写Data visualization代考|The Horse in Motion

The modern scientific interest in visualizing motion can be traced to some simple yet perplexing questions of the late 1800 s concerning the locomotion of the horse.

• How exactly do horses’ feet move differently in a walk, a trot, a canter, and a gallop?
• What is the exact sequence of the four legs in cach gait?
• How many feet are off the ground at any given time in each gait?
• Is there any moment, in each gait, when a horse is at least instantaneously suspended in air, with all four feet off the ground?
In the 1860s to 1870 s, the last question was called “unsupported transit,” and various writers weighed in to argue each side of the controversy. 4

But there were no “data”; no information was available that was sufficiently precise to answer the question convincingly. The motion of a galloping horse was too rapid for either sight or sound to decipher, and even records of the positions of hooves on a specially prepared track could not be used to discern their exact pattern in time and space. In some ways, the armchair discussion on this topic resembled that of what to do about crime in France in the time of Guerry (Chapter 3), or the transmission of cholera in the time of Farr and Snow (Chapter 4).

The debate on horse locomotion was sufficiently intriguing to Leland Stanford (railway baron, governor of California, and a horse breeder) that he hired Eadweard Muybridge [1830-1904], a well-known photographer with a bent for technology, to try to answer the question by photographic means.

## 有限元方法代写

tatistics-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。

## 统计代写|数据可视化代写Data visualization代考|COSC3000

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

## 统计代写|数据可视化代写Data visualization代考|Three-Dimensional Plots

Contour maps and contour plots were certainly useful, but they were still images on a two-dimensional surface, using shading or level curves to show a third dimension. There is a huge difference between trying to navigate a driving route or a hike from a $2 \mathrm{D}$ map that shows elevation with isolines versus a 3D relief map that shows elevation in context, using perspective, realistic lighting (“raytracing”), color (“terrain colors”), texture mapping, and other techniques to generate beautiful and more useful 3D topographic maps. ${ }^{10}$

The technique of rendering 3D views in depth and perspective on a flat surface was known to artists for centuries, but early landscapes lacked realism. The first exemplar to get perspective approximately right was the painting View of the Arno Valley by Leonardo da Vinci in 1473, his first known drawing; but that is just an artist’s view. For data graphics, the precise technical details of drawing a 3D surface of a response variable $z$ over a plane defined by $(x, y)$ coordinates did not develop until the late 1800s. By 1869, in the course of work on thermodynamics, the German physicist Gustav Zeuner [1828-1907] worked out the mathematics of what has come to be called the axonometric projection: a way of drawing a 3D coordinate system so that the coordinate axes looked to be at right angles, and parallel slices or curves had the proper appearance. Zeuner took Descartes to 3D.

An example is shown in Figure 8.6. The coordinate axes, $X, Y, Z$ are shown with the origin in the back. Two parallel curves are drawn, and the goal of this diagram is to explain how the rectangular region can be seen in terms of its projected shadows (shaded) as rectangles on the bottom and left planes.
The first known use of a 3D data graphic using these ideas was designed by Luigi Perozzo [1856-1916], an Italian mathematician, statistician, and,ultimately, a hero of demography, largely for this contribution to the study of the distribution of age over time.

A graphic innovation on this topic appeared in the U.S. Census atlas of 1870, where Francis Walker pioneered the idea of an “age-sex pyramid” showing the age distribution of the population by sex. It was called a pyramid because it compared the populations of men and women in back-to-back histograms by age, in a way that resembled a pyramid. In a number of plates, these data were broken down by state and other factors, in such a way that insurance agencies could begin to set age-, sex-, and region-specific rates for an annuity or life insurance policy. To demographers, this method gave a way to characterize fertility, life expectancy, and other questions regarding population variation. But these were still 2D graphs.

## 统计代写|数据可视化代写Data visualization代考|Visualizing Time and Space

The three decades from 1950 to 1980 were a period of active growth in the development and use of increasingly realistic data visualization. One thread concerned statistical and computational: dimension-reduction methods for representing high-D data in a low-D space that could be plotted, mostly in 2 D. ${ }^1$ Another thread in this period reflected new graphical methods, boosted by increasing computing power, which allowed graphic displays to become increasingly dynamic and interactive. Such displays were capable of showing changes over time with animation, thus changing the nature of a graph from a static image to one that a viewer can directly manipulate, zoom, or query. In these ways, the escape from Flatland continued as a wide range of important problems were illuminated by new approaches to understanding data in higher dimensions.

Once again, these developments illustrate the interplay between advances in technology (computer display and software engineering) and scientific questions for which visualization methods held promise. Today, we see the impact of this in the work of data journalists who now routinely present the details behind important stories (the Brexit vote in the United Kingdom, climate change, COVID-19, etc.) in high-impact online, interactive graphic applications. This chapter traces the origins of these ideas and some of the scientific questions that prompted this evolution of visualizing motion, time and space.

## 统计代写|数据可视化代写Data visualization代考|Three-Dimensional Plots

Luigi Perozzo [1856-1916] 是意大利数学家、统计学家，并且最终成为人口统计学的英雄，他使用这些想法首次使用 3D 数据图形，主要是因为他对年龄分布研究的贡献随着时间的推移。

1870 年的美国人口普查地图集中出现了关于该主题的图形创新，其中 Francis Walker 率先提出了“年龄-性别金字塔”的想法，该金字塔显示了按性别划分的人口年龄分布。它之所以被称为金字塔，是因为它以一种类似于金字塔的方式比较了按年龄排列的背靠背直方图中的男性和女性人口。在许多板块中，这些数据按州和其他因素细分，这样保险机构就可以开始为年金或人寿保险单设定年龄、性别和地区特定的费率。对于人口统计学家来说，这种方法提供了一种表征生育率、预期寿命和其他有关人口变化的问题的方法。但这些仍然是二维图形。

## 有限元方法代写

tatistics-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。

## 统计代写|数据可视化代写Data visualization代考|BINF7003

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

## 统计代写|数据可视化代写Data visualization代考|The Modern Dark Ages

We defined a golden age is a period of high accomplishment surrounded on both sides by relatively lower levels: a mountain or a plateau. This is true for the Golden Age of Graphics. You can see this in the dip in graphical innovations into the 1950s shown in Figure 7.1. If the last half of the nineteenth century can be called the Golden Age of Statistical Graphics, the first half of the twentieth century can equally be called the “Modern Dark Ages” of data visualization. ${ }^{28}$ What happened?

As mentioned earlier, the costs associated with government-sponsored statistical albums eventually outweighed the enthusiasm of those who paid the bills. But more importantly, a new zeitgeist began to appear, which would turn the attention and enthusiasm of both theoretical and applied statisticians away from graphic displays, back to numbers and tables, with a rise of quantification that would supplant visualization. Modern statistical methods had arrived.

It is somewhat ironic that this change of view reflects a form of intellectual parricide. The statistical theory that had started with games of chance and the calculus of astronomical observations developed into the first ideas of statistical models, starting with correlation and regression, due to Galton,Pearson, and others, and this development was aided greatly by the birth of visualization methods and dependent on visual thinking.

Yet, by 1908, W. S. Gosset (publishing under the pseudonym Student) developed the $t$-test, allowing researchers to determine whether two groups of numbers (yields of wheat grown with or without a fertilizer) differed “significantly” in their average value. All that was needed was a single number (a probability or $p$-value) to decide, or so it seemed.

Between 1918 and 1925, R. A. Fisher elaborated the ideas of analysis of variance and experimental design, among his many inventions, turning numerical statistical methods into an entire enterprise capable of delivering exact conclusions from experiments testing multiple causes (fertilizer type and concentration, pesticide application, watering levels) all together. Numbers, parameter estimates-particularly those with standard errors-came to be viewed as precise. Pictures of data became considered-well, just pictures: pretty or evocative perhaps, but incapable of stating a “fact” to three or more decimals places At least it began to seem this way to many statisticians and practitioners. $^{29}$

## 统计代写|数据可视化代写Data visualization代考|Contour Maps

Maps start with a two-dimensional surface defined by latitude and longitude. After geographic features such as rivers, cities, and towns had been inscribed, it was natural for cartographers to want to show features of elevation, and landforms such as mountains and plateaus, in what came to be called topographic maps. This idea was a natural initial impetus for 3D thinking and visual depiction.

The first large-scale topographic map of an entire country was the Carte géométrique de la France, by the French astronomer and surveyor CésarFrançis Cassini de Thury [1714-1784], ${ }^2$ completed in 1789 . But well before these precise determinations of altitude were made, map makers began to try to show topographical features using contour lines of equal elevation on their maps. These were useful for finding the way through a mountain range as well as for military defense.

Beyond wayfinding and route navigation, thematic maps use the features of geography to show something more: how some quantity of interest varies from place to place. Figure $3.3$ by Balbi and Guerry is a nice example of the use of shaded (choropleth) maps of France to display the geographic distribution of crimes and compare this with the distribution of literacy. But this and similar maps treat geographic regions as discrete, and simply shade the entire area in relation to a variable of interest.

The language and symbolism of maps expanded to display more abstract quantitative phenomena that varied systematically over geographical space. This was technically a small step from topographic maps that showed elevation of terrain using either color shading or iso-curves (lines of equal magnitude), but the impact was profound in scientific investigation. It was essentially what Galton had done in mapping the contours of equal barometric pressure across Europe (see Plate 12).

This idea, of drawing level curves or contours on a map to show a data variable, began much earlier. Perhaps the first complete example ${ }^3$ is the 1701 map by Edmund Halley, showing lines of equal magnetic declination (isogons) for the world, shown here in Figure 8.2. It was titled, in a style that tried to tell the whole story on the frontispiece, The Description and Uses of a New, and Correct Sea-Chart of the Whole World, Shewing Variations of the Compass.

## 统计代写|数据可视化代写Data visualization代考|The Modern Dark Ages

1918 年至 1925 年间，RA Fisher 详细阐述了方差分析和实验设计的思想，在他的众多发明中，将数值统计方法变成了一个完整的企业，能够从测试多种原因（肥料类型和浓度、农药施用、浇水水平）一起。数字、参数估计——尤其是那些有标准误差的——被认为是精确的。数据图片被认为是——嗯，只是图片：也许漂亮或令人回味，但无法将“事实”陈述到小数点后三位或更多位至少许多统计学家和从业者开始这样认为。29

## 有限元方法代写

tatistics-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。

## CS代写|数据可视化代写Data visualization代考|EDS240

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|Human Resource Management

Human resource management (HRM) is the part of an organization that focuses on an organization’s recruitment, training, and retention of employees. With the increased use of analytics in business, HRM has become much more data-driven. Indeed, HRM is sometimes now referred to as “people analytics.” HRM professionals use data and analytical models to form high-performing teams, monitor productivity and employee performance, and ensure diversity of the workforce. Data visualization is an important component of HRM, as HRM professionals use data dashboards to monitor relevant data supporting their goal of having a high-performing workforce.
A key interest of HRM professionals is employee churn, or turnover in an organization’s workforce. When employees leave and others are hired, there is often a loss of productivity as positions go unfilled. Also, new employees typically have a training period and then must gain experience, which means employees will not be fully productive at the beginning of their tenure with the company. Figure $1.8$, a stacked column chart, is an example of a visual display of employee turnover. It shows gains and losses of employees by month. A stacked column chart is a column chart that shows part-to-whole comparisons, either over time or across categories. Different colors or shades of color are used to denote the different parts of the whole within a column. In Figure 1.8, gains in employees (new hires) are represented by positive numbers in darker blue and losses (people leaving the company) are presented as negative numbers and lighter blue bars. We see that January and July-October are the months during which the greatest numbers of employees left the company, and the months with the highest numbers of new hires are April through June.Visualizations like Figure $1.8$ can be helpful in better understanding and managing workforce fluctuations.

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

Marketing is one of the most popular application areas of analytics. Analytics lis used for optimal pricing, markdown pricing for seasonal goods, and optimal allocation of marketing budget. Sentiment analysis using text data such as tweets, social networks to determine influence, and website analytics for understanding website traffic and sales, are just a few examples of how data visualization can be used to support more effective marketing.
Let us consider a software company’s website effectiveness. Figure $1.9$ shows a funnel chart of the conversion of website visitors to subscribers and then to renewal customers. A funnel chart is a chart that shows the progression of a numerical variable for various categories from larger to smaller values. In Figure 1.9, at the top of the funnel, we track $100 \%$ of the first-time visitors to the website over some period of time, for example, a six-month period. The funnel chart shows that of those original visitors, $74 \%$ return to the website one or more times after their initial visit. Sixty-one percent of the first-time visitors downloaded a 30-day trial version of the software, $47 \%$ eventually contacted support services, $28 \%$ purchased a one-year subscription to the software, and $17 \%$ eventually renewed their subscription. This type of funnel chart can be used to compare the conversion effectiveness of different website configurations, the use of bots, or changes in support services.

## 有限元方法代写

tatistics-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。

## CS代写|数据可视化代写Data visualization代考|INF552

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|Big Data

There is no universally accepted definition of big data. However, probably the most general definition of big data is any set of data that is too large or too complex to be handled by standard data-processing techniques using a typical desktop computer. People refer to the four $\mathrm{Vs}$ of big data:

• volume-the amount of data generated
• velocity-the speed at which the data are generated
• variety-the diversity in types and structures of data generated
• veracity-the reliability of the data generated
Volume and velocity can pose a challenge for processing analytics, including data visualization. Special data management software such as Hadoop and higher capacity hardware (increased server or cloud computing) may be required. The variety of the data is handled by converting video, voice, and text data to numerical data, to which we can then apply standard data visualization techniques.
In summary, the type of data you have will influence the type of graph you should use to convey your message. The zoo attendance data in Figure $1.1$ are time series data. We used a column chart in Figure $1.1$ because the numbers are the total attendance for each month, and we wanted to compare the attendance by month. The height of the columns allows us to easily compare attendance by month. Contrast Figure $1.1$ with Figure 1.4, which is also time series data. Here we have the value of the Dow Jones Index. These data are a snapshot of the current value of the DJI on the first trading day of each month. They provide what is essentially a time path of the value, and so we use a line graph to emphasize the continuity of time.

## CS代写|数据可视化代写Data visualization代考|Data Visualization in Practice

Data visualization is used to explore and explain data and to guide decision making in all areas of business and science. Even the most analytically advanced companies such as Google, Uber, and Amazon rely heavily on data visualization. Consumer goods giant Procter \& Gamble (P\&G), the maker of household brands such as Tide, Pampers, Crest, and Swiffer, has invested heavily in analytics, including data visualization. P\&G has built what it calls the Business Sphere ${ }^{\mathrm{TM}}$ in more than 50 of its sites around the world. The Business Sphere is a conference room with technology for displaying data visualizations on its walls. The Business Sphere displays data and information P\&G executives and managers can use to make better-informed decisions. Let us briefly discuss some ways in which the functional areas of business, engineering, science, and sports use data visualization.

Accounting is a data-driven profession. Accountants prepare financial statements and examine financial statements for accuracy and conformance to legal regulations and best practices, including reporting required for tax purposes. Data visualization is a part of every accountant’s tool kit. Data visualization is used to detect outliers that could be an indication of a data error or fraud. As an example of data visualization in accounting, let us consider Benford’s Law.
Benfords Law, also known as the First-Digit Law, gives the expected probability that the first digit of a reported number takes on the values one through nine, based on many real-life numerical data sets such as company expense accounts. A column chart displaying Benford’s Law is shown in Figure 1.5. We have rounded the probabilities to four digits. We see, for example, that the probability of the first digit being a 1 is $0.3010$. The probability of the first digit being a 2 is $0.1761$, and so forth.

Benford’s Law can be used to detect fraud. If the first digits of numbers in a data set do not conform to Bedford’s Law, then further investigation of fraud may be warranted. Consider the accounts payable (money owed the company) for Tucker Software. Figure $1.6$ is a clustered column chart (also known as a side-by-side column chart). A clustered column chart is a column chart that shows multiple variables of interest on the same chart, with the different variables usually denoted by different colors or shades of a color. In Figure 1.6, the two variables are Benford’s Law probability and the first digit data for a random sample of 500 of Tucker’s accounts payable entries. The frequency of occurrence in the data is used to estimate the probability of the first digit for all of Tucker’s accounts payable entries. It appears that there are an inordinate number of first digits of 5 and 9 and a lower than expected number of first digits of 1 . These might warrant further investigation by Tucker’s auditors.

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

• volume——产生的数据量
• 速度——生成数据的速度
• 多样性——生成的数据类型和结构的多样性
• 准确性——生成的数据的可靠性
总之，您拥有的数据类型将影响您应该用来传达信息的图表类型。动物园出勤数据如图1.1是时间序列数据。我们在图中使用了柱形图1.1因为这些数字是每个月的总出勤率，我们想按月比较出勤率。列的高度使我们可以轻松地按月比较出勤率。对比图1.1图 1.4 也是时间序列数据。这里我们有道琼斯指数的价值。这些数据是每个月第一个交易日 DJI 当前价值的快照。它们提供了本质上是价值的时间路径，因此我们使用折线图来强调时间的连续性。

## 有限元方法代写

tatistics-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。

## CS代写|数据可视化代写Data visualization代考|BINF7003

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|Data Visualization for Exploration

Data visualization is a powerful tool for exploring data to more easily identify patterns, recognize anomalies or irregularities in the data, and better understand the relationships between variables. Our ability to spot these types of characteristics of data is much stronger and quicker when we look at a visual display of the data rather than a simple listing.
As an example of data visualization for exploration, let us consider the zoo attendance data shown in Table $1.1$ and Figure 1.1. These data on monthly attendance to a zoo can be found in the file Zoo. Comparing Table $1.1$ and Figure 1.1, observe that the pattern in the data is more detectable in the column chart of Figure $1.1$ than in a table of numbers. A column chart shows numerical data by the height of the column for a variety of categories or time periods. In the case of Figure 1.1, the time periods are the different months of the year.

Our intuition and experience tells us that we would expect zoo attendance to be highest in the summer months when many school-aged children are out of school for summer break. Figure $1.1$ confirms this, as the attendance at the zoo is highest in the summer months of June, July, and August. Furthermore, we see that attendance increases gradually each month from February through May as the average temperature increases, and attendance gradually decreases each month from September through November as the average temperature decreases. But why does the zoo attendance in December and January not follow these patterns? It turns out that the zoo has an event known as the “Festival of Lights” that runs from the end of November through early January. Children are out of school during the last half of December and early January for the holiday season, and this leads to increased attendance in the evenings at the zoo despite the colder winter temperatures.
Visual data exploration is an important part of descriptive analytics. Data visualization can also be used directly to monitor key performance metrics, that is, measure how an organization is performing relative to its goals. A data dashboard is a data visualization tool that gives multiple outputs and may update in real time. Just as the dashboard in your car measures the speed, engine temperature, and other important performance data as you drive, corporate data dashboards measure performance metrics such as sales, inventory levels, and service levels relative to the goals set by the company. These data dashboards alert management when performances deviate from goals so that corrective actions can be taken.
Visual data exploration is also critical for ensuring that model assumptions hold in predictive and prescriptive analytics. Understanding the data before using that data in modeling builds trust and can be important in determining and explaining which type of model is appropriate.

## CS代写|数据可视化代写Data visualization代考|Data Visualization for Explanation

Data visualization is also important for explaining relationships found in data and for explaining the results of predictive and prescriptive models. More generally, data visualization is helpful in communicating with your audience and ensuring that your audience understands and focuses on your intended message.

Let us consider the article, “Check Out the Culture Before a New Job,” which appeared in The Wall Street Journal. ${ }^3$ The article discusses the importance of finding a good cultural fit when seeking a new job. Difficulty in understanding a corporate culture or misalignment with that culture can lead to job dissatisfaction. Figure $1.3$ is a re-creation of a bar chart that appeared in this article. A bar chart shows a summary of categorical data using the length of horizontal bars to display the magnitude of a quantitative variable.

The chart shown in Figure $1.3$ shows the percentage of the 10,002 survey respondents who listed a factor as the most important in seeking a job. Notice that our attention is drawn to the dark blue bar, which is “Company culture” (the focus of the article). We immediately see that only “Salary and bonus” is more frequently cited than “Company culture.” When you first glance at the chart, the message that is communicated is that corporate culture is the second most important factor cited by job seekers. And as a reader, based on that message, you then decide whether the article is worth reading.

## 有限元方法代写

tatistics-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。

## 统计代写|数据可视化代写Data visualization代考|INFO2001

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

## 统计代写|数据可视化代写Data visualization代考|Another Asymmetry

There is still one more small, but nagging, problem with this description of Galton’s development of regression and the idea of correlation. In Figure 6.13, which shows Galton’s sweet pea data, we were careful to plot the size of child seeds on the vertical $y$ axis against that of their parent seeds on the horizontal $x$ axis, as is the modern custom for a scatterplot, whose goal is to show how $y$ depends on, or varies with, $x$. Modern statistical methods that flow from Galton and Pearson are all about directional relationships, and they try to predict $y$ from $x$, not vice-versa. It makes sense to ask how a child’s height is related to that of its parents, but it stretches the imagination to go in the reverse direction and contemplate how a child’s height might influence that of its parents.

So, why didn’t Galton put child height on the $y$ axis and parent height on the $x$ axis in Figure 6.16, as one would do today? One suggestion is that such graphs were in their infancy, so the convention of plotting the outcome variable on the ordinate had not yet been established. Yet in Playfair’s timeseries graphs (Plate 10) and in all other not-quite-scatterplots such as Halley’s (Figure 6.2), the outcome variable was always shown on the $y$ axis.

The answer is surely that Galton’s Figure $6.16$ started out as a table, listing mid-parent heights in the rows and heights of children in the columns. Parent height was the first grouping variable, and he tallied the heights of their children in the columns.

In a table, the rows are typically displayed in increasing order (of $y$ ) from top to bottom; a plot does the reverse, showing increasing values of $y$ from bottom to top. Hence, it seems clear that Galton constructed his Table I (Figure 6.14) and figures based on it (Figure $6.15$ and Figure 6.16) as if he thought of them as plots.

## 统计代写|数据可视化代写Data visualization代考|Some Remarkable Scatterplots

As Galton’s work shows, scatterplots had advantages over earlier graphic forms: the ability to see clusters, patterns, trends, and relations in a cloud of points. Perhaps most importantly, it allowed the addition of visual annotations (point symbols, lines, curves, enclosing contours, etc.) to make those relationships more coherent and tell more nuanced stories. This $2 \mathrm{D}$ form of the scatterplot allows these higher-level visual explanations to be placed firmly in the foreground. John Tukey later expressed this as, “The greatest value of a picture is when it forces us to notice what we never expected to see” (1977, p. vi).

In the first half of the twentieth century, data graphics entered the mainstream of science, and the scatterplot soon became an important tool in new discoveries. Two short examples must serve to illustrate applications in physical science and economics.

One key feature was the idea that discovery of something interesting could come from the perception-and understanding-of classifications of objects based on clusters, groupings, and patterns of similarity, rather than direct relations, linear or nonlinear. Observations shown in a scatterplot could belong to different groups, revealing other laws. The most famous example concerns the Hertzsprung-Russell (HR) diagram, which revolutionized astrophysics.
The original version of the Hertzsprung-Russell diagram, shown here in Figure 6.17, is not a graph of great beauty, but nonetheless it radically changed thinking in astrophysics by showing that scatterplots of measurements of stars could lead to a new understanding of stellar evolution.

Astronomers had long noted that stars varied, not only in brightness (luminosity), but also in color, from blue-white to orange, yellow, and red. But until the early 1900 s, they had no general way to classify them or interpret variations in color. In 1905, the Danish astronomer Ejnar Hertzsprung presented tables of luminosity and star color. He noted some apparent correlations and trends, but the big picture-an interpretable classification, leading to theory-was lacking, probably because his data were displayed in tables.

## 统计代写|数据可视化代写数据可视化代考|一些显著的散点图

Galton的工作表明，散点图比早期的图形形式有优势:能够在点云中看到集群、模式、趋势和关系。也许最重要的是，它允许添加视觉注释(点符号、线、曲线、外围轮廓等)，使这些关系更连贯，讲述更微妙的故事。这种$2 \mathrm{D}$形式的散点图可以让这些更高层次的视觉解释牢牢地放在前景中。约翰·杜克(John Tukey)后来将其表达为:“一幅画的最大价值在于它迫使我们注意到我们从未期望看到的东西”(1977,p. vi)

## 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。