### 数学代写|线性代数代写linear algebra代考|MTH 2106

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• 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

## 数学代写|线性代数代写linear algebra代考|A Sample of Linear Algebra in Our World

We began this text with a very bold claim. We stated that linear algebra is the most useful subject you will encounter. In this section, we show how linear algebra provides a whole host of important tools that can be applied to exciting real-world problems and as well as to advance your mathematical skills. We also discuss just a few interesting areas of active research for which linear algebra tools are essential. Each of these problems can be expressed using the language of linear algebra.

The word “dynamic” is just a fancy way of describing something that is changing. All around us, from microscopic to macroscopic scales, things are changing. Scientists measure the spread of disease and how to mitigate such spread. Biologists use population models to show how population dynamics change when new species are introduced to an ecosystem. Astronomers use orbital mechanics to study the interactions of celestial bodies in our universe. Manufacturers are interested in the cooling process after diffusion welding is complete. Chemists and nuclear physicists study atomic and subatomic

interactions. And, geologic and atmospheric scientists study models that predict the effects of geologic or atmospheric events. These are only a small subset of the larger set of dynamic models being studied today.

Differential equations provide the mathematical interpretation of dynamic scenarios. Solutions to the differential equations provide scientists deeper understanding of their respective problem. Solutions help scientists determine the best orbit for space telescopes like Hubble or the James Webb Space telescope, determine how different mitigation strategies might affect disease spread, and predict the location of a tsunami as it spreads across the Pacific Ocean.

## 数学代写|线性代数代写linear algebra代考|Signals and Data Analysis

In our world, data is collected on just about everything. Researchers are interested in determining ways to glean information from this data. They might want to find patterns in the data that are unique to a particular scenario. How often does the apparent luminosity of a distant star fall because of the transit of an orbiting planet? When is the best time to advertise your product to users on a social media platform? How do we determine climate change patterns? Researchers are also interested in how to determine what the data tells them about grouping like subjects in their study. Which advertisement type matches with which social media behavior? How can we classify animal fossils to link extinct animals to the animals we see around us? Scientists are interested in determining the division between unlike subjects. Will a particular demographic vote for a particular candidate based on key features in an ad campaign? They might want to predict outcomes based on data already collected. What can downstream flow and turbidity data reveal about forest health and snow pack in the upstream mountains? Image analysts are interested in recovering images, determining image similarity, and manipulating images and videos. How can we recover the 3D structure of an object from many 2D pictures? How can we view brain features from CAT scans using fewer scans? How do we create smooth transitions between images in a video?

Machine learning is an active area of data analysis research. The overarching goal of machine learning is to create mathematical tools that instruct computers on classification of research subjects or how to predict outcomes. Some key tools of machine learning are Fourier analysis (for pattern recognition), regression analysis (for predictions), support vector machines (for classification), and principle component analysis (for finding relevant features or structure).

## 数学代写|线性代数代写linear algebra代考|Optimal Design and Decision-Making

Optimization is the mathematical process of making optimal decisions. Every day we are confronted with an endless stream of decisions which we must make. What should I eat for breakfast? Should I check my email? What music will I listen to? Pet the cat or not? Most decisions are made easily and quickly without much attention to making a “best” choice and without significantly affecting the world around us. However, researchers, business owners, engineers, and data analysts cannot often ignore best choices. Mathematical optimization seeks a best choice when such a choice is crucial. For example, an engineer designing a tail fin for a new aircraft cannot produce a design based on a personal desire for it to “look cool.” Rather, the relevant criteria should include performance, structural integrity, cost, manufacturing capability, etc.

Optimization is ubiquitous in the sciences and business. It is the tool for engineering design, multiperiod production planning, personnel and/or event scheduling, route-planning for visits or deliveries, linear and nonlinear data fitting, manufacturing process design, packaging, designing facility layouts, partitioning data into classes, bid selection (tendering), activities planning, division into teams, space probe trajectory planning, highway improvements for traffic flow, allocation of limited resources, security camera placement, and many others.

## 数学代写|线性代数代写linear algebra代考|A Sample of Linear Algebra in Our World

“动态”一词只是描述正在发生变化的事物的一种奇特方式。在我们周围，从微观到宏观，一切都在变化。科学家测量疾病的传播以及如何减轻这种传播。生物学家使用种群模型来展示当新物种被引入生态系统时种群动态如何变化。天文学家使用轨道力学来研究我们宇宙中天体的相互作用。制造商对扩散焊接完成后的冷却过程很感兴趣。化学家和核物理学家研究原子和亚原子

## 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。