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澳洲代写｜MATH3102｜Methods & Models of Applied Mathematics应用数学方法与模型昆士兰大学

Posted on 2023年10月13日2023年10月13日 by statistics-lab

statistics-lab^TM为您提供昆士兰大学（The University of Queensland）Methods & Models of Applied Mathematics应用数学方法与模型澳洲代写代考和辅导服务！

课程介绍：

Classical methods and models in applied mathematics: dimensional analysis, perturbation methods, traffic models (continuity equation, shocks, Rankine-Hugoniot condition), introduction to continuum mechanics (elasticity, visco-elasticity, fluid dynamics, Navier-Stokes equation).

澳洲代写｜MATH3102｜Methods & Models of Applied Mathematics应用数学方法与模型昆士兰大学

Methods & Models of Applied Mathematics应用数学方法与模型相关

问题 1.

In each case compute $y^{\prime}=\frac{d y}{d p}$ as a function of $y$ and $p$, given that $y=y(p)$ satisfies:

$p^3+p y+2=0$.
$y=\sin (y+p)$.
$\ln (y)=p$.
$\cos ^2(y)=p$, for $p>0$.
$y=f(c-y p)$.
$y=f(p-c y)$.
Note: in (5) and (6) $f$ is an arbitrary function, and $c$ is a constant.

问题 2. Two variable dependence implicit differentiation.
In each case compute $u_x=\frac{\partial u}{\partial x}$ and $u_y=\frac{\partial u}{\partial y}$ (as functions of $u, x$, and $y$ ), given that $u=u(x, y)$ satisfies:

$x^3 y+2 x u+y u^3=0$.
$y=f(y+u x)$.
$\ln (1+y)=u e^{x u}$.
Note: In (2) $f$ is an arbitrary function of a single variable, $f=f(\zeta)$. Assume that $f^{\prime} \neq 0$.

问题 3.

In each case compute $u_x=\frac{\partial u}{\partial x}$ and $u_p=\frac{\partial u}{\partial p}$ (as functions of $u, x$, and $p$ ), given that $u=u(x, p)$ satisfies:

$\cos \left(p^2 u\right)=p e^{-x^2}$.
$p=\cos (x+u)$.
$u=p f(x+u)$.
Note: In (3) $f$ is an arbitrary function of a single variable, $f=f(\zeta)$.

问题 4.

Consider the wave equation for $u=u(x, t)$, where $c>0$ is a constant,
$$
u_{t t}-c^2 u_{x x}=0 .
$$
Introduce the new independent variables $\boldsymbol{\eta}=\boldsymbol{x}-\boldsymbol{c t}$, and $\boldsymbol{\xi}=\boldsymbol{x}+\boldsymbol{c t}$. Change variables to write the equation for $u$ as a function of these new variables: $\boldsymbol{u}=\boldsymbol{u}(\boldsymbol{\eta}, \boldsymbol{\xi})$. Using this transformed form of the equation, integrate it twice to show that it must be
$$
u=f(\eta)+g(\xi),
$$
for some arbitrary functions $f$ and $g$. This shows that any solution of the wave equation (1.1) must have the form $u=f(x-c t)+g(x+c t)$.

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题，以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语广义线性模型（GLM）通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归，以及方差分析和方差分析（仅含固定效应）。

有限元方法代写

有限元方法（FEM）是一种流行的方法，用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法，用于解决两个或三个空间变量的偏微分方程（即一些边界值问题）。为了解决一个问题，有限元将一个大系统细分为更小、更简单的部分，称为有限元。这是通过在空间维度上的特定空间离散化来实现的，它是通过构建对象的网格来实现的：用于求解的数值域，它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统，以模拟整个问题。然后，有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

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。

随机分析代写

随机微积分是数学的一个分支，对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程，是依赖于参数的一组随机变量的全体，参数通常是时间。随机变量是随机现象的数量表现，其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值（如1秒，5分钟，12小时，7天，1年），因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中，往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录，以得到其自身发展的规律。

回归分析代写

多元回归分析渐进（Multiple Regression Analysis Asymptotics）属于计量经济学领域，主要是一种数学上的统计分析方法，可以分析复杂情况下各影响因素的数学关系，在自然科学、社会和经济学等多个领域内应用广泛。

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，工具箱允许您学习和应用专业技术。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。

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

澳洲代写｜STAT3007｜Deep Learning深度学习昆士兰大学

Posted on 2023年10月13日2023年10月13日 by statistics-lab

statistics-lab^TM为您提供昆士兰大学（The University of Queensland）Deep Learning深度学习澳洲代写代考和辅导服务！

课程介绍：

Deep learning has become a much sought-after game-changing technology that has enabled breakthroughs in applications such as intelligent virtual assistants, medical diagnosis, recommender systems, and autonomous driving. This course provides a comprehensive and rigorous coverage of deep learning from both applied and theoretical perspectives. Students taking this course will understand how, why and when the algorithms work, and be able to effectively apply deep learning methods to practical problems. This course begins with the basics of machine learning, followed by a broad coverage of deep neural networks, including some major deep neural network architectures, optimization of network parameters, and applications in classification, regression and reinforcement learning. This course is suitable for both students who want to build data-driven enabling applications with deep learning, and students who want to develop a solid foundation for doing research in deep learning in particular, and machine learning or artificial intelligence more broadly. To maximise the learning outcomes, students are expected to have a solid foundation in statistics, calculus, linear algebra, and programming. Python will be used for this course.

Detail	Information
Course Code	STAT3007
Prerequisite	(STAT2004 or STAT2203 or equivalent) + programming experience (e.g., MATH2504 or CSSE2002 or equivalent)
School	Mathematics & Physics School
Course Enquiries	Dr Nan Ye
Units	2

Multiplying Matrices and Vectors矩阵和向量相乘相关

Matrix product operations have many useful properties that make mathematical analysis of matrices more convenient. For example, matrix multiplication is distributive:
$$
A(B+C)=A B+A C .
$$
It is also associative:
$$
\boldsymbol{A}(\boldsymbol{B C})=(\boldsymbol{A B}) \boldsymbol{C} .
$$
Matrix multiplication is not commutative (the condition $\boldsymbol{A B}=\boldsymbol{B A}$ does not always hold), unlike scalar multiplication. However, the dot product between two vectors is commutative:
$$
\boldsymbol{x}^{\top} \boldsymbol{y}=\boldsymbol{y}^{\top} \boldsymbol{x} .
$$
The transpose of a matrix product has a simple form:
$$
(\boldsymbol{A B})^{\top}=\boldsymbol{B}^{\top} \boldsymbol{A}^{\top} \text {. }
$$
This allows us to demonstrate equation 2.8 , by exploiting the fact that the value of such a product is a scalar and therefore equal to its own transpose:
$$
\boldsymbol{x}^{\top} \boldsymbol{y}=\left(\boldsymbol{x}^{\top} \boldsymbol{y}\right)^{\top}=\boldsymbol{y}^{\top} \boldsymbol{x} .
$$
Since the focus of this textbook is not linear algebra, we do not attempt to develop a comprehensive list of useful properties of the matrix product here, but the reader should be aware that many more exist.

We now know enough linear algebra notation to write down a system of linear equations:
$$
A x=b
$$
where $\boldsymbol{A} \in \mathbb{R}^{m \times n}$ is a known matrix, $\boldsymbol{b} \in \mathbb{R}^m$ is a known vector, and $\boldsymbol{x} \in \mathbb{R}^n$ is a vector of unknown variables we would like to solve for. Each element $x_i$ of $\boldsymbol{x}$ is one of these unknown variables. Each row of $\boldsymbol{A}$ and each element of $\boldsymbol{b}$ provide another constraint. We can rewrite equation 2.11 as:
$$
\begin{gathered}
\boldsymbol{A}{1,:} \boldsymbol{x}=b_1 \ \boldsymbol{A}{2,:} \boldsymbol{x}=b_2 \
\ldots \
\boldsymbol{A}{m, x} \boldsymbol{x}=b_m \end{gathered} $$ or, even more explicitly, as: $$ \boldsymbol{A}{1,1} x_1+\boldsymbol{A}{1,2} x_2+\cdots+\boldsymbol{A}{1, n} x_n=b_1
$$

Identity and Inverse Matrices恒等矩阵和逆矩阵相关

Linear algebra offers a powerful tool called matrix inversion that allows us to analytically solve equation 2.11 for many values of $\boldsymbol{A}$.

To describe matrix inversion, we first need to define the concept of an identity matrix. An identity matrix is a matrix that does not change any vector when we multiply that vector by that matrix. We denote the identity matrix that preserves $n$-dimensional vectors as $\boldsymbol{I}_n$. Formally, $\boldsymbol{I}_n \in \mathbb{R}^{n \times n}$, and
$$
\forall x \in \mathbb{R}^n, I_n x=x .
$$
The structure of the identity matrix is simple: all of the entries along the main diagonal are 1 , while all of the other entries are zero. See figure 2.2 for an example.
The matrix inverse of $\boldsymbol{A}$ is denoted as $\boldsymbol{A}^{-1}$, and it is defined as the matrix such that
$$
\boldsymbol{A}^{-1} \boldsymbol{A}=\boldsymbol{I}_n
$$
We can now solve equation 2.11 by the following steps:
$$
\begin{gathered}
A x=b \
A^{-1} A x=A^{-1} b \
I_n x=A^{-1} b
\end{gathered}
$$
$$
\boldsymbol{x}=\boldsymbol{A}^{-1} \boldsymbol{b} .
$$
Of course, this process depends on it being possible to find $\boldsymbol{A}^{-1}$. We discuss the conditions for the existence of $\boldsymbol{A}^{-1}$ in the following section.

When $\boldsymbol{A}^{-1}$ exists, several different algorithms exist for finding it in closed form. In theory, the same inverse matrix can then be used to solve the equation many times for different values of $\boldsymbol{b}$. However, $\boldsymbol{A}^{-1}$ is primarily useful as a theoretical tool, and should not actually be used in practice for most software applications. Because $\boldsymbol{A}^{-1}$ can be represented with only limited precision on a digital computer, algorithms that make use of the value of $\boldsymbol{b}$ can usually obtain more accurate estimates of $\boldsymbol{x}$.

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

澳洲代写｜ECON2070｜Introduction to Strategic Thinking战略思维导论昆士兰大学

Posted on 2023年10月13日2023年10月13日 by statistics-lab

statistics-lab^TM为您提供昆士兰大学（The University of Queensland）Introduction to Strategic Thinking战略思维导论澳洲代写代考和辅导服务！

课程介绍：

The way that economists think about strategic situations is through the application of game theory. One aim of the course is to teach you some strategic considerations to take into account when making your own choices. A second aim is to predict how other people or organizations behave when they are in strategic settings. We will see that these aims are closely related. We will learn new concepts, methods and terminology. A third aim is to apply these tools to settings from economics and other disciplines. The course will emphasize examples.

澳洲代写｜ECON2070｜Introduction to Strategic Thinking战略思维导论昆士兰大学

Detail	Information
Course Code	ECON2070
Course Title	Introduction to Strategic Thinking
Coordinating Unit	School of Economics
Pre-Requisites	ECON1010 or 1011
Course Coordinator	Dr Metin Uyanik
Number of Units	2

Introduction to Strategic Thinking战略思维导论相关

Later GM leaders’ preoccupation with financial strategy had exacerbated a lack of attention to other business policies, such as labor relations, safety, and product quality. GM leaders’ antagonistic attitude toward labor fostered continuing labor troubles for GM (that greatly contributed to the deterioration of GM’s capability for efficient, high-quality, and low-cost production). GM leaders resisted providing leadership in automobile safety and environment (eventually fostering U.S. government intervention to mandate safety and environmental standards for the auto industry). GM’s different automobile brands lost distinctiveness, quality, competitiveness, and performance – particularly compared to imported foreign-produced automobiles. By the 1970s, GM had failed to compete effectively in the low-end automobile market. It also lost quality leadership in the middle and high end. German automobile makers dominated high-end sector quality; whereas Japanese automobile makers dominated lower and middle sector quality. The holding company strategy as a focus on finance had not encouraged the portfolio companies to maintain quality and brand distinctiveness.

As we saw in the GM decline and bankruptcy, the leadership of a diversified firm needs to attend to providing adequate resources to its portfolio businesses to keep them competitive. Otherwise market share of its portfolio firms decline. Profits decline, and eventually the whole firm can go bankrupt.

The decision to maintain a portfolio business or to buy or sell a business are strategic decisions by a CEO of a diversified firm – which have major impacts upon the presidents of the businesses in the firm’s business portfolio. There are six critical factors for successful strategic management that need to be recognized:
(1) The need for relationships of trust between levels of management;
(2) The impact of unequal power relationships between a holding company and the businesses of its portfolio;
(3) The effect of long-term and short-term differences of control over finances between the firm and its portfolio businesses;
(4) The possible results of differing incentives and rewards for levels of management;
(5) The inherent conflicts of interest of different levels of management;
(6) The influence of external forces on business valuation.

后来通用汽车领导人对财务战略的关注加剧了对其他业务政策的缺乏关注，例如劳动关系、安全和产品质量。通用汽车领导人对劳工的敌对态度，导致通用汽车公司持续陷入劳工困境（这极大地导致了通用汽车公司高效、高质量、低成本生产能力的恶化）。通用汽车领导人拒绝在汽车安全和环境方面发挥领导作用（最终促成美国政府干预，强制汽车行业制定安全和环境标准）。通用汽车的不同汽车品牌失去了独特性、质量、竞争力和性能——特别是与进口的外国生产的汽车相比。到了 20 世纪 70 年代，通用汽车未能在低端汽车市场进行有效竞争。它还失去了中高端的质量领先地位。德国汽车制造商主导高端领域品质；而日本汽车制造商在中低质量行业占据主导地位。以金融为重点的控股公司战略并没有鼓励投资组合公司保持质量和品牌独特性。

正如我们在通用汽车的衰落和破产中看到的那样，多元化公司的领导层需要为其投资组合业务提供足够的资源，以保持其竞争力。否则，其投资组合公司的市场份额会下降。利润下降，最终整个公司可能破产。

维持投资组合业务或购买或出售业务的决定是多元化公司首席执行官的战略决策，这对公司业务组合中的业务总裁产生重大影响。成功的战略管理需要认识到六个关键因素：
(1) 管理层之间需要建立信任关系；
(2) 控股公司与其投资组合业务之间不平等权力关系的影响；
(3) 公司及其投资组合业务之间长期和短期财务控制权差异的影响；
（四）对管理层实行不同的激励和奖励可能产生的结果；
（五）各级管理人员固有的利益冲突；
(6)外部力量对企业估值的影响。

External Forces on Business Valuation
Stocks create a value of a return from investment either (1) through dividends paid out annually by the company or (2) by any increase in the price of the stock. Accordingly, earnings of a corporation can be used for paying out dividends, investments for improving businesses, or acquisitions. Earnings used for dividends provide an immediate return to the shareholder, while earnings used for improvement or acquisitions may provide future capital accumulation to raise share value. There is an important trade-off in optimizing shareholder value of a company, between how earnings are used for (1) immediate returnon-investment or (2) future return-on-investment. Economically, this trade-off should be
made to balance appropriately short-and long-term shareholder value and short- and longterm competitiveness of the company. However, external forces can make an important influence on this balancing, particularly when a government’s tax policies unwittingly bias this balance. And in the U.S. Corporate world of the twentieth century, Federal government tax policy had biased corporate strategy strongly against returning investments in stocks via dividends.

Government policies can often make an important impact upon the environments of a business. In the United States at the close of the twentieth century, Federal government income tax policy had a major external impact upon business policies. The Federal government taxed returns-on-investment from stocks very unequally as dividends or stock appreciation. Wealthy individuals who owned stock would have any dividends from their stocks taxed at a top income tax rate of $36 \%$ in the year 2000 . In contrast, any gains on sales of their stocks held at least one year would be taxed at a lower capital gains tax rate of $28 \%$. This tax rate difference of $36-28=8 \%$ had in effect created a $22 \%$ tax penalty on received stock returns by dividends rather that appreciation. One can see how government policy of the United States in the last part of the twentieth century encouraged corporations to pursue strategies which aimed at continually increasing stock prices, as opposed to traditional business practice of sharing earnings with investors through dividends. Thus earnings were often used to buy growth through acquisitions, even when a company could not properly manage acquired businesses.

The tax policies of the United States biased twentieth-century corporate strategy away from traditional dividend strategies toward quick capital-gain strategies – making it difficult to properly run companies in mature industries with little growth but steady earnings – as all successful companies eventually become. When the twentieth century began, the corporate situation in the United States was one where the U.S. Federal government unwittingly biased the economic playing field through tax policy wherein:
(1) only companies in new and growing industries (such as Cisco) could be properly run and rewarded with high price/earnings $(P / E)$ ratios by the financial structure, and
(2) companies in mature industries (such as Sunbeam) could not be properly run and at the same time rewarded by reasonable $P / E$ ratios by the financial structure.

企业估值的外部力量
股票通过（1）通过公司每年支付的股息或（2）通过股票价格的上涨创造投资回报价值。因此，公司的收益可用于支付股息、改善业务的投资或收购。用于股息的收益可以立即为股东带来回报，而用于改进或收购的收益则可以提供未来的资本积累以提高股票价值。在优化公司股东价值时，需要在收益用于（1）即时投资回报或（2）未来投资回报之间进行重要权衡。从经济角度来看，这种权衡应该是
适当平衡短期和长期股东价值以及公司短期和长期竞争力。然而，外部力量可以对这种平衡产生重要影响，特别是当政府的税收政策无意中使这种平衡产生偏差时。在二十世纪的美国企业界，联邦政府的税收政策强烈偏向企业战略，反对通过股息返还股票投资。

政府政策通常会对企业环境产生重要影响。在二十世纪末的美国，联邦政府的所得税政策对商业政策产生了重大的外部影响。联邦政府对股票投资回报的征税与股息或股票增值的征税非常不平等。 2000 年，持有股票的富人将按照 36%$ 的最高所得税税率对股票股息征税。相比之下，出售持有至少一年的股票所获得的任何收益都将按较低的资本利得税率征税，即 28 \%$。 $36-28=8 \%$ 的税率差异实际上对通过股息而不是升值获得的股票回报产生了 $22 \%$ 的税收罚款。人们可以看到，二十世纪后半叶美国政府的政策如何鼓励企业采取旨在不断提高股价的战略，而不是通过股息与投资者分享收益的传统商业做法。因此，即使公司无法正确管理所收购的业务，收益也经常被用来通过收购来购买增长。

美国的税收政策使二十世纪的企业战略从传统的股利战略转向快速的资本收益战略——这使得在成长缓慢但盈利稳定的成熟行业中正确经营公司变得困难——就像所有成功的公司最终都会遇到的那样。二十世纪初，美国的企业状况是美国联邦政府无意中通过税收政策扭曲了经济竞争环境，其中：
(1) 只有新兴行业和成长型行业的公司（例如思科）才能正常运营并通过财务结构获得高市盈率$(P / E)$，并且
(2)成熟行业的公司(如Sunbeam)无法通过财务结构得到合理的运营并同时获得合理的$P/E$比率奖励。

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

澳洲代写｜FINM3407｜Behavioural Finance行为金融学昆士兰大学

Posted on 2023年10月13日2023年10月13日 by statistics-lab

statistics-lab^TM为您提供昆士兰大学（The University of Queensland）Behavioural Finance行为金融学澳洲代写代考和辅导服务！

课程介绍：

This course provides an introduction to behavioural finance, a discipline that integrates insights from psychology into the study of finance. There will be a focus on understanding the psychological underpinnings of financial decision-making as well as the institutional frictions that may allow these psychological mechanisms to influence economic outcomes. Applications include the pricing of assets relative to fundamental value, trading strategies, managerial behaviour, and household savings and investment decisions.

澳洲代写｜MATH3404｜Optimisation Theory最优化理论昆士兰大学

Attribute	Detail
Course Code	FINM3407
Course Title	Behavioural Finance
Coordinating Unit	School of Business
Pre-Requisites	(FINM2415 + 2416) or (FINM2411 + 3412) or (ECON1310 + 2010)
Course Coordinator/Lecturer	Associate Professor Kelvin Tan
Number of Units	2

Risk and Return of a Portfolio投资组合的风险和回报

Risk and Return of a Portfolio
A portfolio comprises of two or more assets. Smart investors know that combining several assets in a portfolio usually leads to risk reduction, thanks to the benefit of diversification. Remember the old adage which says, “don’t put all your eggs in the same basket.”

To understand the quantitative impact of diversification on risk (variability), let us consider a portfolio of two assets. As long as the returns on the two assets do not move in perfect lockstep, diversification reduces risk. Covariance and correlation are statistical measures of how random variables are related. If the two variables tend to move in the same (opposite) direction, the covariance and correlation are positive (negative).
The correlation of a sample including $n$ returns for assets 1 and 2 is:
$$
\rho_{12}=\frac{\sigma\left(R_1, R_2\right)}{\sigma_1 \sigma_2}
$$
$\sigma\left(R_1, R_2\right)$ is the covariance between returns on assets 1 and 2 , and $\sigma_1$ and $\sigma_2$ are standard deviations of returns on assets 1 and 2 . While the covariance can take any positive or negative value, the correlation always lies between -1.0 and +1.0 .

Given information on how the returns for the two assets are correlated, we can compute the portfolio mean return and portfolio variance for two asset portfolios as follows:
$$
\begin{gathered}
\bar{R}p=w_1 \bar{R}_1+w_2 \bar{R}_2 \ \sigma_p^2=w_1^2 \sigma_1^2+w_2^2 \sigma_2^2+2 w_1 w_2 \rho{12} \sigma_1 \sigma_2
\end{gathered}
$$
where $\bar{R}\rho$ is portfolio mean return, $w_1$ and $w_2$ are the weights associated with assets 1 and $2\left(w_1+w_2=1\right), \bar{R}_1$ and $\bar{R}_2$ are the mean returns for assets 1 and $2, \sigma\rho^2$ is the variance of the portfolio return, $\sigma_1{ }^2$ and $\sigma_2{ }^2$ are the variance of the returns for assets 1 and 2 , and $\rho_{12}$ is the coefficient of correlation between the returns on assets 1 and 2 .

As long as $\rho_{12}$ is less than $1, \sigma_p$ will be less than the weighted average of the standard deviations of returns for the two assets.
For a 3-asset portfolio, the portfolio mean return and portfolio variance are as follows:
$$
\begin{gathered}
\bar{R}p=w_1 \bar{R}_1+w_2 \bar{R}_2+w_3 \bar{R}_3 \ \sigma_p^2=w_1^2 \sigma_1^2+w_2^2 \sigma_2^2+w_3^2 \sigma_3^2+2 w_1 w_2 \rho{12} \sigma_1 \sigma_2+2 w_1 w_3 \rho_{13} \sigma_1 \sigma_3+2 w_2 w_3 \rho_{23} \sigma_2 \sigma_3
\end{gathered}
$$
In general, for a portfolio of $n$ assets
$$
\begin{gathered}
R_p=\sum_{i=1}^n w_i R_i \
\sigma_p^2=\sum_{i=1}^n \sum_{j=1}^n w_i w_j \sigma\left(R_i, R_j\right)
\end{gathered}
$$

Behavioural Finance行为金融学案例

问题 1.

Rakesh Gupta’s utility function for wealth is: $u(w)=w^{2 / 3}$
Suppose Rakesh Gupta has a $20 \%$ chance of wealth of ₹ 3,000,000, 30\% chance of wealth of $₹ 2,000,000$, and $50 \%$ chance of wealth of $₹ 1,000,000$.
a. What is the expected value of wealth?
b. Is Rakesh Gupta risk averse, risk neutral, or risk-seeking?
c. What is Rakesh Gupta’s certainty equivalent for the prospect?

a. The expected value of wealth is:
$$
0.2 \times 3,000,000+0.3 \times 2,000,000+0.5 \times 1,000,000=₹ 1,700,000
$$
b. Given the utility function $u(w)=w^{2 / 3}$ let us look at the utility for four levels of wealth, viz., ₹ 0 , $₹ 1,000,000$, ₹ $2,000,000$, and ₹ $3,000,000$.
$$
\begin{aligned}
& u(0)=0 \
& u(1,000,000)=10,046 \
& u(2,000,000)=15,951 \
& u(3,000,000)=20,905
\end{aligned}
$$
From these numbers, it is clear that Rakesh Gupta’s utility of wealth function is concave, implying that he is risk-averse.

c. The expected utility of the prospect is:
$$
0.2 \times 20,905+0.3 \times 15,951+0.3 \times 10,046=13,989
$$
The expected utility is 13,989 . Since the utility of wealth is $w^{2 / 3}$, the certain amount that provides an expected utility of 13,989 is:
$$
(13,989)^{3 / 2}=₹ 1,654,550
$$

问题 2.

Neha has the following utility function:
$$
\mathrm{u}(w)=\ln w \quad \text { where } w=\text { wealth }
$$
a. What is the expected utility of the following prospects:
$$
\begin{aligned}
& \text { P1 }(0.6,400,800) \
& \text { P2 }(0.8,5000,2,000) \
& \text { P3 }(0.4,6,000,3,000)
\end{aligned}
$$
b. What is the certainty equivalent of P2?

Solution
a. The expected utility of $\mathrm{P} 1$ is:
$$
0.6 U(400)+0.4 U(800)=0.6 \times 5.99+0.4 \times 6.68=6.27
$$
The expected utility of $\mathrm{P} 2$ is:
$$
0.8 \times 8.52+0.2 \times 7.60=8.34
$$
The expected utility of $\mathrm{P} 3$ is:
$$
0.4 \times 8.70+0.6 \times 8.01=8.29
$$
b. The expected utility of $P 2$ is 8.34 . Since the utility of wealth is $\ln w$, the certain wealth that provides an expected utility of 8.34 is:
$$
e^{8.34}=4,188
$$

问题 3.

The risk-free return is 7 per cent and the expected return on market portfolio is 14 per cent. If the required return on a stock is 16 per cent, what is its beta?

Solution
We have: Required return $=$ Risk-free return + Beta(Expected return on market portfolio – Risk-free return)
$$
\begin{aligned}
& 16=7+\operatorname{Beta}(14-7) \
& \text { Beta }=9 / 7=1.29
\end{aligned}
$$

澳洲代写｜FINM3407｜Behavioural Finance行为金融学昆士兰大学

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

MATH0057｜Probability and Statistics概率与统计伦敦大学学院

Posted on 2023年9月25日2023年10月6日 by statistics-lab

statistics-lab^TM为您提供伦敦大学学院（University College London，简称：UCL）MATH0057Probability and Statistics概率与统计英国代写代考和辅导服务！

课程介绍：

The aim of the course is to introduce students to the theory of probability and some of the
statistical methods based upon it. Many physical processes involve random components which
can only be modelled using probabilistic methods. Statistical theory is vital for analysing
scientific data where it is necessary to distinguish genuine patterns from random fluctuations.

MATH0057｜Probability and Statistics概率与统计伦敦大学学院

Probability and Statistics概率与统计作业案例

问题 1.

Ignoring leap days, the days of the year can be numbered 1 to 365 . Assume that birthdays are equally likely to fall on any day of the year. Consider a group of $n$ people, of which you are not a member. An element of the sample space $\Omega$ will be a sequence of $n$ birthdays (one for each person).
(a) Define the probability function $P$ for $\Omega$..

answer: The sample space $\Omega$ is the set of all sequences of $n$ birthdays. That is, all sequences
$$
\omega=\left(b_1, b_2, b_3, \ldots, b_n\right),
$$

where each entry is a number between 1 and 365 .

问题 2.

(b) Consider the following events:
A: “someone in the group shares your birthday”
B: “some two people in the group share a birthday”
C: “some three people in the group share a birthday”
Carefully describe the subset of $\Omega$ that corresponds to each event.

(b) Event $A$ : Suppose my birthday is on day $b$. Then “an outcome $\omega$ is in $A$ ” is equivalent to ” $b$ is in the sequence for $\omega$ “, i.e. $b=b_k$ for some index $k$ between 1 and $n$. More symbolically,
an outcome $\omega$ is in $A$ if and only if $b_k=b$ for some index $k$ in $1, \ldots, n$.
Event B: “An outcome $\omega$ is in $B$ ” is equivalent to “two of the entries in $\omega$ are the same”. That is, an outcome $\omega$ is in $B \quad$ if and only if $\quad b_j=b_k$ for two (different) indices $j, k$ in $1, \ldots, n$.
Event $C$ : an outcome $\omega$ is in $C$ if and only if $\quad b_j=b_k=b_l$ for three (distinct) indices $j, k, l$ in $1, \ldots, n$.

问题 3.

(c) It’s easier to calculate $P\left(A^c\right)$. There are $364^n$ outcomes in $A^c$ since there are 364 choices for each birthday. So
$$
P(A)=1-P\left(A^c\right)=1-\frac{364^n}{365^n} .
$$
We can find the size of the group needed for $P(A)>.5$ by trial and error, plugging in different values of $n$. Or we can set $P(A)=.5$ and solve for $n$.
$$
1-\frac{364^n}{365^n}=.5 \Rightarrow\left(\frac{364}{365}\right)^n=.5 \Rightarrow n \cdot \ln \left(\frac{364}{365}\right)=\ln (.5) \Rightarrow n \approx 252.65
$$
So there needs to be at least 253 people for it to be more likely than not that one of them shares your birthday.

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

非参数统计代写

非参数统计指的是一种统计方法，其中不假设数据来自于由少数参数决定的规定模型；这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型（GLM）归属统计学领域，是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

有限元方法代写

随机分析代写

时间序列分析代写

回归分析代写

MATLAB代写

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写

ECON2131｜Public Sector Economics公共部门经济学澳洲国立大学

Posted on 2023年9月25日2023年10月6日 by statistics-lab

statistics-lab^TM为您提供澳大利亚国立大学（The Australian National University）ECON2131Public Sector Economics公共部门经济学澳洲代写代考和辅导服务！

课程介绍：

This course uses the basic tools of microeconomics to analyse the role of government and the rationale for and design of taxation and expenditure policy. Emphasis is placed on attaining a solid grasp of the basic theory of public economics. The topics to be covered include a review of basic microeconomics, welfare economics, cost-benefit analysis, public goods, externalities, public choice, public expenditure programs (including education, health, pensions and welfare payments), and taxation (including efficiency and equity issues, tax incidence, Australian tax policy, and fiscal federalism).

ECON2131｜Public Sector Economics公共部门经济学澳洲国立大学

Public Sector Economics公共部门经济学作业案例

问题 1.

For each of the examples below, please answer the following:

Does an externality exist? If so, classify the externality as positive/negative (or both).

British Petroleum drills for oil in the gulf coast
Yes; accidents on oil rigs that cause spills impose a negative externality on others (e.g.
inhabitants of gulf states). (optional answer: Oil drilling may also yield a positive externality if the identiÖcation of the location of oil allows other companies to drill for oil
more e§ectively b/c they know where the oil is).
If oil spills only damage property, and these property owners can costlessly recoup costs
in the legal system, then the drillers will internalize the impact of their drilling on the
social cost of the oil spill. But, if it is hard to determine the true costs from an oil
spill (e.g. may be hard to Ögure out whether someone lost their job b/c of an oil spill
or b/c of some other reason), then the coase theorem may not apply. (Also, in the
positive externality case: may be di¢ cult to assign property rights to an oil Öeld after it
is identiÖed, so coase theorem may not apply). Quantity regulation on the amount of safety/advanced drilling technology investmentseems feasible. One could also argue for subsidies for safer drilling technologies (or taxeson less safe technologies). Tradeable permits seems di¢ cult here, since itís not clearwhat the permits would specify (but Iím open to a good suggestion!).

问题 2.

If an externality exists, determine whether the Coase theorem applies (i.e. is it possible/reasonably feasible to asign property rights and solve the problem?)

Carbon emissions from vehicles

Yes; Classic negative externality: I drive my car and emit gases that harm others (whose harm I don’t pay for).
Coase theorem is unlikely to apply in this case, since it would require assigning property rights to those that are harmed. Since many of the harmed are very dispersed (e.g. driving in boston theoretically harms everyone in the world a small amount) and in some cases involves the “unborn” (future generations facing global warming), the feasibility of negotiated private contracts is highly questionable.
As discussed in class, if we believe that the benefit curve is flat, we would want to price the carbon using a tax. Tradeable permits may be more desirable politically though. Quantity regulation would require differential quantities for each producer of carbon, since they all have differing marginal costs; therefore quantity regulation seems subopti$\mathrm{mal} /$ difficult without instituting tradeable permits.

问题 3.

If an extenality exists and the Coase theorem does not apply, argue which of the government’s tools are best suited to address the issue: quantity regulation, taxes/subsidies, tradeable permits, or something else.

Your upstairs neighbors throwing an awesome, but loud party

Yes; but the externality is either positive or negative, depending on your taste for parties.
Coase theorem would require the neighbors to own the rights to holding the party. Then the neighbor would pay the other neighbor to have (or not have) the party. This could work (so an answer of “yes” is fine). But, in reality, there are likely many different people who are affected by the throwing of the party (e.g. multiple neighbors hate the noise). Bargaining with all parties may allow one party to “hold-up” the others, rendering the coase theorem inapplicable.

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

R语言代写	问卷设计与分析代写
PYTHON代写	回归分析与线性模型代写
MATLAB代写	方差分析与试验设计代写
STATA代写	机器学习/统计学习代写
SPSS代写	计量经济学代写
EVIEWS代写	时间序列分析代写
EXCEL代写	深度学习代写
SQL代写	各种数据建模与可视化代写