月度归档： 2023 年 4 月

统计代写|线性回归分析代写linear regression analysis代考|What does an insignificant estimate tell you

Posted on 2023年4月30日2023年4月25日 by statistics-lab

如果你也在怎样代写线性回归分析linear regression analysis这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

回归分析是一种强大的统计方法，允许你检查两个或多个感兴趣的变量之间的关系。虽然有许多类型的回归分析，但它们的核心都是考察一个或多个自变量对因变量的影响。

statistics-lab™ 为您的留学生涯保驾护航在代写线性回归分析linear regression analysis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。我们的专家在代写线性回归分析linear regression analysis代写方面经验极为丰富，各种代写线性回归分析linear regression analysis相关的作业也就用不着说。

我们提供的线性回归分析linear regression analysis及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(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|线性回归分析代写linear regression analysis代考|What does an insignificant estimate tell you

The basic reason why the lack of evidence is not proof of non-existence is that there are alternative reasons for the lack of evidence. As mentioned earlier, when a jury in a criminal trial deliberates on whether a defendant is guilty, the jury members are not directed to conclude that the defendant has been proven innocent. Rather, they are supposed to determine whether there is significant evidence (beyond a reasonable doubt) that indicates the defendant was guilty. Thus, one reason why a defendant may be found “not guilty” is that there was not enough evidence.

The same concept is supposed to be used for statistical analysis. We are often testing whether a coefficient estimate is different from zero. Let’s say we are examining how class-size affects elementary-school students’ test scores, and let’s say that we find an insignificant estimate on the variable for class-size. In a study of mine (Arkes 2016), I list four general possible explanations for an insignificant estimate:

There is actually no effect of the explanatory variable on the outcome in the population.
There is an effect in one direction, but the model is unable to detect the effect due to a modeling problem (e.g., omitted-factors bias or measurement error – see Chapter 6) biasing the coefficient estimate in a direction opposite to the actual effect.

There is a small effect that cannot be detected with the available data due to inadequate power i.e., not a large enough sample given the size of the effect.
There are varying effects in the population (or sample); some people’s outcomes may be affected positively by the treatment, others’ outcomes may be affected negatively, and others’ outcomes may not be affected; and the estimated effect (which is the average effect) is insignificantly different from zero due to the positive and negative effects canceling each other out or being drowned out by those with zero effects.

So what can you conclude from the insignificant estimate on the class-size variable? You cannot conclude that class size does not affect test scores. Rather, as with the hot hand and the search for aliens, the interpretation should be: “There is no evidence that class-size affects test scores.”

Unfortunately, a very common mistake made in the research world is that the conclusion would be that there is no effect. This is important for issues such as whether there are side effects from pharmaceutical drugs or vaccines. The lack of evidence for a side effect does not mean that there is no effect, particularly if confidence intervals for the estimates include values that would represent meaningful side effects of the drug or vaccine.

All that said, there are sometimes cases in which an insignificant estimate has a $95 \%$ or $99 \%$ confidence interval with a fairly narrow range and outer boundary that, if the boundary were the true population parameter, it would be “practically insignificant” (see Section 5.3.9). If this were the case and the coefficient estimate were not subject to any meaningful bias, then it would be safe to conclude that “there is no meaningful effect.”

统计代写|线性回归分析代写linear regression analysis代考|Statistical significance is not the goal

As we conduct research, our ultimate goal should be to advance knowledge. Our goal should not be to find a statistically-significant estimate. Advancing knowledge occurs by conducting objective and honest research.

A statistically insignificant coefficient estimate on a key-explanatory variable is just as valid as a significant coefficient estimate. The problem, many believe, is that an insignificant estimate may not provide as much information as a significant estimate. As described in the previous section, an insignificant estimate does not necessarily mean that there is no meaningful relationship, and so it could have multiple possible interpretations. If the appropriate confidence intervals for the coefficient were narrow (which would indicate adequate power), the methods were convincing for ruling out modeling problems, and the effects would likely go in just one direction, then it would be more reasonable to conclude that an insignificant estimate indicates there is no meaningful effect of the treatment. But meeting all those conditions is rare, and so there are multiple possible conclusions that cannot be distinguished.

As mentioned in the previous section, a statistically-significant estimate could also be subject to the various interpretations of insignificant estimates. But these are often ignored and not deemed as important, to most people, as long as there is statistical significance.

Statistical significance is valued more, perhaps, because it is evidence confirming, to some extent, the researcher’s theory and/or hypothesis. I conducted a quick, informal review of recent issues of leading economic, financial, and education journals. As it has been historically, almost all empirical studies had statistically-significant coefficient estimates on the key-explanatory variable. Indeed, I had a difficult time finding an insignificant estimate. This suggests that the pattern continues that journals are more likely to publish studies with significant estimates on the key-explanatory variables.

The result of statistical significance being valued more is that it incentivizes researchers to make statistical significance the goal of research. This can lead to $\mathbf{p}$-hacking, which involves changing the set of control variables, the method (e.g. Ordinary Least Squares (OLS) vs. an alternative method, such as in Chapters 8 and 9), the sample requirements, or how the variables (including the outcome) are defined until one achieves a p-value below a major threshold. (I describe p-hacking in more detail in Section 13.3.)

It is unfortunate that insignificant estimates are not accepted more. But, hopefully, this book will be another stepping stone for the movement to be more accepting of insignificant estimates. I personally trust insignificant estimates more than significant estimates (except for the hot hand in basketball).

The bottom line is that, as we conduct research, we should be guided by proper modeling strategies and not by what the results are saying.

线性回归代写

统计代写|线性回归分析代写linear regression analysis代考|What does an insignificant estimate tell you

缺乏证据不能证明不存在的基本原因是缺乏证据还有其他原因。如前所述，当陪审团在刑事审判中审议被告是否有罪时，陪审团成员并没有被指示得出被告已被证明无罪的结论。相反，他们应该确定是否有重要证据（排除合理怀疑）表明被告有罪。因此，被告人可能被判“无罪”的原因之一是证据不足。

同样的概念应该用于统计分析。我们经常测试系数估计值是否不同于零。假设我们正在研究班级规模如何影响小学生的考试成绩，假设我们发现班级规模变量的估计值不显着。在我的一项研究中（Arkes 2016），我列出了对微不足道的估计的四种一般可能解释：

实际上，解释变量对总体结果没有影响。
在一个方向上有影响，但由于建模问题（例如，遗漏因素偏差或测量误差——参见第 6 章），模型无法检测到影响系数估计值在与实际影响相反的方向上的偏差。

由于功效不足，即没有足够大的样本给定效应大小，因此无法用可用数据检测到一个小效应。
总体（或样本）有不同的影响；某些人的结果可能会受到治疗的积极影响，其他人的结果可能会受到负面影响，而其他人的结果可能不会受到影响；由于正负效应相互抵消或被零效应淹没，因此估计效应（即平均效应）与零无显着差异。

那么，从对班级规模变量的微不足道的估计中，你能得出什么结论呢？您不能断定班级规模不会影响考试成绩。相反，就像热手和寻找外星人一样，解释应该是：“没有证据表明班级规模会影响考试成绩。”

不幸的是，研究界常犯的一个错误是得出的结论是没有效果。这对于诸如药物或疫苗是否有副作用等问题很重要。缺乏副作用的证据并不意味着没有影响，特别是如果估计的置信区间包括代表药物或疫苗有意义的副作用的值。

综上所述，有时在某些情况下，微不足道的估计会产生95%或者99%具有相当窄的范围和外部边界的置信区间，如果边界是真实的人口参数，它将“实际上微不足道”（见第 5.3.9 节）。如果情况确实如此，并且系数估计不受任何有意义的偏差影响，那么可以安全地得出“没有有意义的影响”的结论。

统计代写|线性回归分析代写linear regression analysis代考|Statistical significance is not the goal

当我们进行研究时，我们的最终目标应该是增进知识。我们的目标不应该是找到具有统计意义的估计值。通过进行客观和诚实的研究来提高知识。

对关键解释变量的统计上不显着的系数估计与显着的系数估计一样有效。许多人认为，问题在于微不足道的估计可能无法提供与重要估计一样多的信息。如前一节所述，一个无关紧要的估计并不一定意味着没有有意义的关系，因此它可能有多种可能的解释。如果系数的适当置信区间较窄（这表明有足够的功效），这些方法对于排除建模问题是有说服力的，并且效果可能只朝一个方向发展，那么得出不显着的结论会更合理估计表明治疗没有有意义的效果。但满足所有这些条件是罕见的，

如前一节所述，具有统计意义的估计也可能受到对不重要估计的各种解释的影响。但对大多数人来说，只要具有统计显着性，这些往往会被忽略并且不被视为重要。

统计显着性更受重视，也许是因为它是在某种程度上证实研究人员的理论和/或假设的证据。我对最近几期领先的经济、金融和教育期刊进行了快速、非正式的审查。从历史上看，几乎所有的实证研究都对关键解释变量进行了统计显着的系数估计。事实上，我很难找到一个微不足道的估计。这表明该模式继续存在，即期刊更有可能发表对关键解释变量进行重大估计的研究。

统计显着性被更加重视的结果是它激励研究者将统计显着性作为研究的目标。这会导致p-hacking，涉及更改控制变量集、方法（例如普通最小二乘法 (OLS) 与替代方法，例如第 8 章和第 9 章中的方法）、样本要求或变量（包括结果）的变化方式被定义，直到一个人达到低于主要阈值的 p 值。（我在第 13.3 节中更详细地描述了 p-hacking。）

不幸的是，微不足道的估计不被更多人接受。但是，希望这本书将成为该运动更多地接受微不足道的估计的另一个垫脚石。我个人更相信无关紧要的估计而不是重要的估计（除了篮球中的热手）。

底线是，在我们进行研究时，我们应该以适当的建模策略为指导，而不是以结果的说法为指导。

统计代写|线性回归分析代写linear regression analysis代考请认准statistics-lab™

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

随机过程代考

在概率论概念中，随机过程是随机变量的集合。若一随机系统的样本点是随机函数，则称此函数为样本函数，这一随机系统全部样本函数的集合是一个随机过程。实际应用中，样本函数的一般定义在时间域或者空间域。 随机过程的实例如股票和汇率的波动、语音信号、视频信号、体温的变化，随机运动如布朗运动、随机徘徊等等。

贝叶斯方法代考

贝叶斯统计概念及数据分析表示使用概率陈述回答有关未知参数的研究问题以及统计范式。后验分布包括关于参数的先验分布，和基于观测数据提供关于参数的信息似然模型。根据选择的先验分布和似然模型，后验分布可以解析或近似，例如，马尔科夫链蒙特卡罗 (MCMC) 方法之一。贝叶斯统计概念及数据分析使用后验分布来形成模型参数的各种摘要，包括点估计，如后验平均值、中位数、百分位数和称为可信区间的区间估计。此外，所有关于模型参数的统计检验都可以表示为基于估计后验分布的概率报表。

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。

statistics-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。

机器学习代写

随着AI的大潮到来，Machine Learning逐渐成为一个新的学习热点。同时与传统CS相比，Machine Learning在其他领域也有着广泛的应用，因此这门学科成为不仅折磨CS专业同学的“小恶魔”，也是折磨生物、化学、统计等其他学科留学生的“大魔王”。学习Machine learning的一大绊脚石在于使用语言众多，跨学科范围广，所以学习起来尤其困难。但是不管你在学习Machine Learning时遇到任何难题，StudyGate专业导师团队都能为你轻松解决。

多元统计分析代考

基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量

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

统计代写|线性回归分析代写linear regression analysis代考|What model diagnostics should you do?

Posted on 2023年4月30日2023年4月25日 by statistics-lab

如果你也在怎样代写线性回归分析linear regression analysis这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的线性回归分析linear regression analysis及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等楖率论
Advanced Mathematical Statistics 高等数理统计学
(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|线性回归分析代写linear regression analysis代考|What model diagnostics should you do?

None! Well, most of the time, none. This is my view, and I might be wrong. Others (including your professor) may have valid reasons to disagree. But here is the argument why, in most cases, there is no need to perform any model diagnostics.
The two most common model diagnostics that are conducted are:

checking for heteroskedasticity
checking for non-normal error terms (Assumption A3 in Section 2.10).
One problem is that the tests are not great. The tests will indicate whether there is statisticallysignificant evidence for heteroskedasticity or non-normal error terms, but they certainly cannot prove that there is not any heteroskedasticity or non-normal error terms.

Regarding heteroskedasticity, your regression probably has heteroskedasticity. And, given that it is costless and painless to fix, you should probably include the heteroskedasticity correction.

Regarding non-normal error terms, recall that, due to the Central Limit Theorem, error terms will be approximately normal if the sample size is large enough (i.e., at least 200 observations at worst, and perhaps only 15 observations would suffice). However, this is not necessarily the case if: (1) the dependent variable is a dummy variable; and (2) there is not a large-enough set of explanatory variables. That said, a problem is that the test for non-normality (which tests for skewness and kurtosis) is highly unstable for small samples.

Having non-normal error terms means that the t-distribution would not apply to the standard errors, so the $t$-stats, standard levels of significance, confidence intervals, and $\mathrm{p}$-values would be a little off-target. The simple solution for cases in which there is the potential for non-normal errors is to require a lower $\mathrm{p}$-value than you otherwise would to conclude that there is a relationship between the explanatory and dependent variables.

I am not overly concerned by problems with non-normal errors because they are small potatoes when weighed against the Bayes critique of $\mathrm{p}$-values and the potential biases from PITFALLS (Chapter 6). If you have a valid study that is pretty convincing in terms of the PITFALLS being unlikely and having low-enough p-values in light of the Bayes critique, then having non-normal error terms would most likely not matter.

One potentially-useful diagnostic would be to check for outliers having large effects on the coefficient estimates. This would likely not be a concern with dependent variables that have a compact range of possible values, such as academic achievement test scores. But it could be the case with dependent variables on individual/family income or corporate profits/revenue, among other such outcomes with potentially large-outlying values of the dependent variable. Extreme values of explanatory variables could also be problematic. In these situations, it could be worth a diagnostic check of outliers for the dependent variable or the residuals. One could estimate the model without the big outliers to see how the results are affected. Of course, the outliers are supposedly legitimate observations, so any results without the outliers are not necessarily more correct. The ideal situation would be that the direction and magnitude of the estimates are consistent between the models with and without the outliers.
Outliers, if based on residuals, could be detected by residual plots. Alternatively, one potential rule that could be used for deleting outliers is based on calculating the standardized residual, which is the actual residual divided by the standard deviation of the residual-there is no need to subtract the mean of the residual since it is zero. The standardized residual indicates how many standard deviations away from zero a residual is. One could use an outlier rule, such as deleting observations with the absolute value of the standardized residual greater than some value, say, 5. With the adjusted sample, one would re-estimate a model to determine how stable the main results are.

统计代写|线性回归分析代写linear regression analysis代考|What the research on the hot hand in basketball tells us about

A friend of mine, drawn to the larger questions on life, called me recently and said that we are all alone – that humans are the only intelligent life in the universe. Rather than questioning him on the issue I have struggled with (whether humans, such as myself, should be categorized as “intelligent” life), I decided to focus on the main issue he raised and asked how he came to such a conclusion. Apparently, he had installed one of those contraptions in his backyard that searches for aliens. Honestly, he has so much junk in his backyard that I hadn’t even noticed. He said that he hadn’t received any signals in two years, so we must be alone.

While I have no idea whether we are alone in the universe, I know that my curious friend is not alone in his logic. A recent Wall Street Journal article made a similar logical conclusion in an article with some plausible arguments on why humans may indeed be alone in the universe. One of those arguments was based on the “deafening silence” from the 40-plus-year Search for Extraterrestrial Intelligence (SETI) project, with the conclusion that this is strong evidence that there is no other intelligent life (Metaxas, 2014). Never mind that SETI only searches our galaxy (of the estimated 170-plus billion galaxies in the universe) and that for us to find life on some planet, we have to be aiming our SETI at that planet (instead of the other 100 billion or so planets in our galaxy) at the same time (within the 13.6 billion years our galaxy has been in existence) that the alien geeks on that planet are emitting strong-enough radio signals in our direction (with a 600-plus-year lag for the radio signals to reach us). It may be that some form of aliens sent radio signals our way 2.8 billion years ago (before they went extinct after elininating their Environmental Protection Ageney), purposefully-striked-through and our amoeba-like ancestors had not yet developed the SETI technology to detect the signals.

The flawed logic here, as you have probably determined, is that lack of evidence is not proof of non-existence. This is particularly the case when you have a weak test for what you are looking for.
This logic flaw happens to be very common among academics. One line of research that has been subject to such faulty logic is that on the hot hand in basketball. The “hot hand” is a situation in which a player has a period (often within a single game) with a systematically higher probability of making shots (adjusting for the difficulty of the shot) than the player normally would have. The hot hand can occur in just about any other sport or activity, such as baseball, bowling, dance, test-taking, etc. In basketball, virtually all players and fans believe in the hot hand, based on witnessing players such as Stephen Curry go through stretches in which they make a series of high-difficulty shots. Yet, from 1985 to 2009 , plenty of researchers tested for the hot hand in basketball by using various tests to essentially determine whether a player was more likely to make a shot after a made shot (or consecutive made shots) than after a missed shot. They found no evidence of the hot hand. Their conclusion was “the hot hand is a myth.”
But then a few articles, starting in 2010, found evidence for the hot hand. And, as Stone (2012), Arkes (2013), and Miller and Sanjurjo (2018) show, the tests for the studies in the first 25 years were pretty weak tests for the hot hand because of some modeling problems, one of which I will describe in Box 6.4 in the next chapter.

The conclusions from those pre-2010 studies should not have been “the hot hand is a myth,” but rather “there is no evidence for the hot hand in basketball.” The lack of evidence was not proof of the non-existence of the hot hand. Using the same logic, in the search for aliens, the lack of evidence is not proof of non-existence, especially given that the tests have been weak. ${ }^5 \mathrm{I}$ ‘d bet my friend’s SETI machine that the other life forms out there, if they exist, would make proper conclusions on the basketball hot hand (and that they won’t contact us until we collectively get it right on the hot hand).

线性回归代写

统计代写|线性回归分析代写linear regression analysis代考|What model diagnostics should you do?

没有任何！好吧，大多数时候，没有。这是我的观点，我可能是错的。其他人（包括你的教授）可能有正当理由不同意。但这是为什么在大多数情况下不需要执行任何模型诊断的论点。
进行的两种最常见的模型诊断是：

检查异方差
检查非正态误差项（第 2.10 节中的假设 A3）。
一个问题是测试不是很好。检验将表明是否存在异方差或非正态误差项的统计显着证据，但它们当然不能证明不存在任何异方差或非正态误差项。

关于异方差性，您的回归可能具有异方差性。而且，鉴于修复起来既无成本又无痛，您可能应该包括异方差校正。

关于非正态误差项，回想一下，根据中心极限定理，如果样本量足够大（即最坏情况下至少有 200 个观测值，也许只有 15 个观测值就足够了），误差项将近似为正态。但是，如果出现以下情况则不一定如此： (1) 因变量是虚拟变量；(2) 没有足够大的解释变量集。也就是说，一个问题是非正态性测试（测试偏度和峰度）对于小样本来说非常不稳定。

具有非正态误差项意味着 t 分布不适用于标准误差，因此吨-统计、显着性标准水平、置信区间和p-values 会有点偏离目标。对于可能出现非正态错误的情况，简单的解决方案是要求较低的p-value 比你否则得出的结论是解释变量和因变量之间存在关系。

我并不过分担心非正态误差的问题，因为当与贝叶斯对p-来自 PITFALLS（第 6 章）的价值观和潜在偏见。如果您有一项有效的研究，就 PITFALLS 不太可能并且根据贝叶斯批判具有足够低的 p 值而言，该研究非常有说服力，那么具有非正态误差项很可能无关紧要。

一种可能有用的诊断方法是检查对系数估计值有很大影响的异常值。对于具有紧凑可能值范围的因变量（例如学业成绩测试分数），这可能不是一个问题。但是，个人/家庭收入或公司利润/收入的因变量可能就是这种情况，以及其他具有因变量的潜在大异常值的结果。解释变量的极值也可能有问题。在这些情况下，可能值得对因变量或残差的异常值进行诊断检查。人们可以在没有大异常值的情况下估计模型，看看结果是如何受到影响的。当然，离群值应该是合法的观察结果，所以没有异常值的任何结果都不一定更正确。理想情况是估计的方向和大小在有和没有异常值的模型之间是一致的。
如果基于残差，则可以通过残差图检测异常值。或者，可用于删除离群值的一个潜在规则是基于计算标准化残差，即实际残差除以残差的标准差——无需减去残差的均值，因为它为零。标准化残差表示残差与零的标准差有多少。可以使用异常值规则，例如删除标准化残差的绝对值大于某个值（例如 5）的观测值。使用调整后的样本，可以重新估计模型以确定主要结果的稳定性。

统计代写|线性回归分析代写linear regression analysis代考|What the research on the hot hand in basketball tells us about

我的一个朋友被生命中更大的问题所吸引，最近给我打电话说我们都是孤独的——人类是宇宙中唯一的智慧生命。我没有就我一直在纠结的问题（人类，比如我自己，是否应该被归类为“智能”生命）质疑他，而是决定专注于他提出的主要问题，并询问他是如何得出这样的结论的。显然，他在自家后院安装了其中一个搜索外星人的装置。老实说，他的后院有那么多垃圾，我什至都没注意到。他说他已经两年没有收到任何信号了，所以我们肯定是一个人。

虽然我不知道我们在宇宙中是否是孤独的，但我知道我好奇的朋友在他的逻辑上并不孤单。《华尔街日报》最近的一篇文章在一篇文章中得出了类似的逻辑结论，并就为什么人类在宇宙中确实可能是孤独的提出了一些似是而非的论据。其中一个论点是基于 40 多年的搜寻外星智能 (SETI) 项目的“震耳欲聋的沉默”，得出的结论是，这是不存在其他智慧生命的有力证据（Metaxas，2014 年）。不要介意 SETI 只搜索我们的星系（宇宙中估计有 170 多亿个星系），为了让我们在某个星球上找到生命，我们必须将 SETI 瞄准那个星球（而不是其他 1000 亿或所以我们银河系中的行星）同时（在 13. 我们的银河系已经存在了 60 亿年），那个星球上的外星极客正在向我们的方向发射足够强的无线电信号（无线电信号到达我们有 600 多年的滞后）。可能是某种形式的外星人在 28 亿年前（在他们取消环境保护署后灭绝之前）向我们发送了无线电信号，有目的地通过，而我们的变形虫类祖先尚未开发出 SETI 技术来检测信号。

正如您可能已经确定的那样，这里有缺陷的逻辑是缺乏证据并不能证明不存在。当您对要查找的内容进行弱测试时尤其如此。
这种逻辑缺陷恰好在学术界非常普遍。受制于这种错误逻辑的一项研究是关于篮球的热手。“热手”是这样一种情况，在这种情况下，一名球员有一段时间（通常在一场比赛中）比球员通常有更高的投篮概率（根据投篮难度进行调整）。热手几乎可以发生在任何其他运动或活动中，例如棒球、保龄球、舞蹈、应试等。在篮球运动中，几乎所有球员和球迷都相信热手，这是基于斯蒂芬库里等球员的见证经历他们进行一系列高难度投篮的阶段。然而，从 1985 年到 2009 年，许多研究人员通过各种测试来测试篮球中的热手，从根本上确定一名球员在投篮命中（或连续投篮命中）后是否比投丢后更有可能投篮。他们没有发现热手的证据。他们的结论是“热手是一个神话”。
但是从 2010 年开始的几篇文章找到了热手的证据。而且，正如 Stone（2012 年）、Arkes（2013 年）以及 Miller 和 Sanjurjo（2018 年）所表明的那样，由于某些建模问题，前 25 年的研究测试对热手的测试非常薄弱，其中之一是我将在下一章专栏 6.4 中描述。

那些 2010 年之前的研究得出的结论不应该是“热手是一个神话”，而是“篮球中没有热手的证据”。缺乏证据并不能证明热手不存在。使用相同的逻辑，在寻找外星人时，缺乏证据并不是不存在的证据，特别是考虑到测试一直很薄弱。5我我敢打赌我朋友的 SETI 机器，如果存在其他生命形式，他们会在篮球热手上做出正确的结论（并且他们不会联系我们，直到我们共同在热手上找到它）。

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

随机过程代考

贝叶斯方法代考

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。

机器学习代写

多元统计分析代考

基础数据: $N$ 个样本， $P$ 个变量数的单样本，组成的横列的数据表
变量定性: 分类和顺序；变量定量：数值
数学公式的角度分为: 因变量与自变量

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

统计代写|STAT311 Regression Analysis

Posted on 2023年4月30日2023年4月25日 by statistics-lab

Statistics-lab™可以为您提供metrostate.edu STAT311 Regression Analysis回归分析的代写代考和辅导服务！

STAT311 Regression Analysis课程简介

This graduate level course offers an introduction into regression analysis. A
Credits 3 researcher is often interested in using sample data to investigate relationships, with an ultimate goal of creating a model to predict a future value for some dependent variable. The process of finding this mathematical model that best fits the data involves regression analysis.
STAT 501 is an applied linear regression course that emphasizes data analysis and interpretation. Generally, statistical regression is collection of methods for determining and using models that explain how a response variable (dependent variable) relates to one or more explanatory variables (predictor variables).

PREREQUISITES

This graduate level course covers the following topics:

Understanding the context for simple linear regression.
How to evaluate simple linear regression models
How a simple linear regression model is used to estimate and predict likely values
Understanding the assumptions that need to be met for a simple linear regression model to be valid
How multiple predictors can be included into a regression model
Understanding the assumptions that need to be met when multiple predictors are included in the regression model for the model to be valid
How a multiple linear regression model is used to estimate and predict likely values
Understanding how categorical predictors can be included into a regression model
How to transform data in order to deal with problems identified in the regression model
Strategies for building regression models
Distinguishing between outliers and influential data points and how to deal with these
Handling problems typically encountered in regression contexts
Alternative methods for estimating a regression line besides using ordinary least squares
Understanding regression models in time dependent contexts
Understanding regression models in non-linear contexts

STAT311 Regression Analysis HELP（EXAM HELP， ONLINE TUTOR）

问题 1.

Use the cig_1st_diff data set. This is based on the changes from 1990 to 2000 , and it is extracted from the data set used in Question #3. Estimate a first-difference model, as follows: regress cigch on taxch, uratech, and beertaxch. Weight the model by pop 2000 , and use robust standard errors. Interpret the estimate on taxch.

问题 2.

From the example in Section 8.5 from Card and Krueger (1994) on estimating the effects of minimum-wage increases on employment, write out the regression equation for the difference-in-difference model.

问题 3.

Use the data set oecd_gas_demand. From Question #7 in Chapter 3, add fixed effects for the country, along with a heteroskedasticity correction.
a. How does the coefficient estimate on lrpmg change from Question #7 in Chapter 3 with the fixed effects added?
b. How does this change which observations are compared to which observations?

问题 4.

Return to the tv-bmi-ecls data set used for Exercise $# 5$ in Chapter 6 . From that exercise, along with other descriptions of the research issue, there is much potential omitted-factors bias.
a. Explore the data description and variable list (from the file “Exercises data set descriptions” on the book’s website). Design a model to address the omitted-factors bias.
b. Are there any shortcomings to your approach?

Textbooks

• An Introduction to Stochastic Modeling, Fourth Edition by Pinsky and Karlin (freely
available through the university library here)
• Essentials of Stochastic Processes, Third Edition by Durrett (freely available through
the university library here)
To reiterate, the textbooks are freely available through the university library. Note that
you must be connected to the university Wi-Fi or VPN to access the ebooks from the library
links. Furthermore, the library links take some time to populate, so do not be alarmed if
the webpage looks bare for a few seconds.

此图像的alt属性为空；文件名为%E7%B2%89%E7%AC%94%E5%AD%97%E6%B5%B7%E6%8A%A5-1024x575-10.png — 统计代写|STAT311 Regression Analysis

Statistics-lab™可以为您提供metrostate.edu STAT311 Regression Analysis回归分析的代写代考和辅导服务！请认准Statistics-lab™. Statistics-lab™为您的留学生涯保驾护航。

经济代写|计量经济学代写Econometrics代考|Dummy Variables

Posted on 2023年4月29日2023年10月17日 by statistics-lab

如果你也在怎样代写计量经济学Econometrics这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

计量经济学，对经济关系的统计和数学分析，通常作为经济预测的基础。这种信息有时被政府用来制定经济政策，也被私人企业用来帮助价格、库存和生产方面的决策。

statistics-lab™ 为您的留学生涯保驾护航在代写计量经济学Econometrics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。我们的专家在代写计量经济学Econometrics代写方面经验极为丰富，各种代写计量经济学Econometrics相关的作业也就用不着说。

我们提供的计量经济学Econometrics及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(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

经济代写|计量经济学代写Econometrics代考|The nature of qualitative information

So far, we have examined the equation specifications employed in econometric analysis, as well as techniques used to obtain estimates of the parameters in an equation and procedures for assessing the significance, accuracy and precision of those estimates. An assumption made implicitly up to this point has been that we can always obtain a set of numerical values for all the variables we want to use in our models. However, there are variables that can play a very important role in the explanation of an econometric model but are not numerical or easy to quantify. Examples of these are:
(a) gender may be very important in determining salary levels;
(b) different ethnic groups may follow diverse patterns regarding consumption and savings;
(c) educational levels can affect earnings from employment; and/or
(d) being a member of a labour union may imply different treatment/attitudes than not belonging to the union.
All these are cases for cross-sectional analysis.
Not easily quantifiable (or, in general, qualitative) information could also arise within a time series econometric framework. Consider the following examples:
(a) changes in a political regime may affect production processes or employment conditions;
(b) a war can have an impact on all aspects of economic activity;
(c) certain days in a week or certain months in a year can have different effects on stock prices; and
(d) seasonal effects are frequently observed in the demand for particular products; for example, ice cream in the summer, furs during the winter.

The aim of this chapter is to show the methods used to include information from qualitative variables in econometric models. This is done by using ‘dummy’ or ‘dichotomous’ variables. The next section presents the possible effects of qualitative variables in regression equations and how to use them. We then present special cases of dummy variables and the Chow test for structural stability.

经济代写|计量经济学代写Econometrics代考|Constant dummy variables

Consider the following cross-sectional regression equation:
$$
Y_i=\beta_1+\beta_2 X_{2 i}+u_i
$$

The constant term $\left(\beta_1\right)$ in this equation measures the mean value of $Y_i$ when $X_{2 i}$ is equal to zero. The important thing here is that this regression equation assumes that the value of $\beta_0$ will be the same for all the observations in the data set. However, the coefficient might be different, depending on different aspects of the data set. For example, regional differences might exist in the values of $Y_i ;$ or $Y_i$ might represent the growth of GDP for European Union (EU) countries. Differences in growth rates are quite possible between core and peripheral countries. The question is, how can we quantify this information in order to enter it in the regression equation and check for the validity of this possible difference? The answer is: with the use of a special type of variable – a dummy (or fake) that captures qualitative effects by coding the different possible outcomes with numerical values.

This can usually be done quite simply by dichotomizing the possible outcomes and arbitrarily assigning the values of 0 and 1 to the two possibilities. So, for the EU countries example, we can have a new variable, $D$, which can take the following values:
$$
D= \begin{cases}1 & \text { for core country } \ 0 & \text { for peripheral country }\end{cases}
$$
Note that the choice of which of the alternative outcomes is to be assigned the value of 1 does not alter the results in an important way, as we shall show later.

Thus, entering this dummy variable in the regression model in Equation (9.1) we get:
$$
Y_i=\beta_1+\beta_2 X_{2 i}+\beta_3 D_i+u_i
$$
and in order to obtain the interpretation of $D_i$, consider the two possible values of $D$ and how these will affect the specification of Equation (9.3). For $D=0$ we have:
$$
\begin{aligned}
Y_i & =\beta_1+\beta_2 X_{2 i}+\beta_3(0)i+u_i \ & =\beta_1+\beta_2 X{2 i}+u_i
\end{aligned}
$$

计量经济学代考

经济代写|计量经济学代写Econometrics代考|The nature of qualitative information

到目前为止，我们已经研究了计量经济学分析中使用的方程规范，以及用于获得方程中参数估计值的技术和评估这些估计值的显着性、准确性和精确性的程序。到目前为止，一个隐含的假设是我们总能为我们想要在模型中使用的所有变量获得一组数值。然而，有些变量可以在计量经济模型的解释中发挥非常重要的作用，但不是数值的或易于量化的。这方面的例子是：
(a) 性别在决定工资水平方面可能非常重要；
(b) 不同族裔群体可能遵循不同的消费和储蓄模式；
(c) 教育水平会影响就业收入；和/或
(d) 加入工会可能意味着与不加入工会不同的待遇/态度。
这些都是横断面分析的案例。
不易量化（或一般来说，定性）的信息也可能出现在时间序列计量经济学框架内。考虑以下例子：
(a) 政治体制的变化可能影响生产过程或就业条件；
(b) 战争会对经济活动的各个方面产生影响；
(c) 一周中的某些天或一年中的某些月会对股票价格产生不同的影响；( d
) 在特定产品的需求中经常观察到季节性影响；例如，夏天的冰淇淋，冬天的皮草。

本章的目的是展示用于将来自定性变量的信息包含在计量经济学模型中的方法。这是通过使用“虚拟”或“二分”变量来完成的。下一节介绍回归方程中定性变量的可能影响以及如何使用它们。然后我们介绍虚拟变量的特殊情况和结构稳定性的 Chow 检验。

经济代写|计量经济学代写Econometrics代考|Constant dummy variables

考虑以下横截面回归方程:
$$
Y_i=\beta_1+\beta_2 X_{2 i}+u_i
$$
常数项 $\left(\beta_1\right)$ 在这个等式中测量的平均值 $Y_i$ 什么时候 $X_{2 i}$ 等于零。这里重要的是这个回归方程假设 $\beta_0$ 对于数据集中的所有观察结果都是相同的。但是，系数可能会有所不同，具体取决于数据集的不同方面。例如，区域差异可能存在于值 $Y_i$; 或者 $Y_i$ 可能代表欧盟 (EU) 国家的 GDP 增长。核心国家和外围国家之间的增长率很可能存在差异。问题是，我们如何量化此信息以便将其输入回归方程并检查此可能差异的有效性? 答案是：使用一种特殊类型的变量一一—个虚拟变量 (或假变量)，它通过用数值编码不同的可能结果来捕捉定性影响。
这通常可以非常简单地完成，方法是将可能的结果一分为二，并任意将 0 和 1 的值分配给两种可能性。所以，以欧盟国家为例，我们可以有一个新变量， $D$ ，它可以采用以下值:
$D={1 \quad$ for core country 0 for peripheral country
请注意，选择将哪个备选结果赋值 1 不会以重要方式改变结果，正如我们稍后将展示的那样。
因此，在等式 (9.1) 的回归模型中输入这个虚拟变量，我们得到:
$$
Y_i=\beta_1+\beta_2 X_{2 i}+\beta_3 D_i+u_i
$$
并为了获得对 $D_i$ ，考虑两个可能的值 $D$ 以及这些将如何影响公式 (9.3) 的规范。为了 $D=0$ 我们有:
$$
Y_i=\beta_1+\beta_2 X_{2 i}+\beta_3(0) i+u_i \quad=\beta_1+\beta_2 X 2 i+u_i
$$

经济代写|计量经济学代写Econometrics代考请认准statistics-lab™

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

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

经济代写|计量经济学代写Econometrics代考|Approaches in choosing an appropriate model

Posted on 2023年4月29日2023年4月24日 by statistics-lab

如果你也在怎样代写计量经济学Econometrics这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的计量经济学Econometrics及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(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

经济代写|计量经济学代写Econometrics代考|The traditional view: average economic regression

In the past, the traditional approach to econometric modelling was to start by formulating the simplest possible model to obey the underlying economic theory and, after estimating that model, to perform various tests in order to determine whether it was satisfactory.

A satisfactory model in that sense would be: (a) one having significant coefficients (that is high $t$-ratios), and coefficients whose signs correspond with the theoretical predictions; (b) one with a good fit (that is high $R^2$ ); and (c) one having residuals that do not suffer from autocorrelation or heteroskedasticity.

If one or more of these points is violated, researchers try to find better methods of estimation (that is the Cochrane-Orcutt iterative method of estimation for the case of serial correlation) or to check other possible causes of bias such as whether important variables have been omitted from the model or whether redundant variables have been included, or to consider alternative functional forms, and so on.

This approach, which essentially starts with a simple model and then ‘builds up’ the models as the situation demands, is called the ‘simple to general approach’ or the ‘average economic regression (AER)’, a term coined by Gilbert (1986), because this was the method that most traditional econometric research was following in practice.
The AER approach has been subject to major criticisms:
1 One obvious criticism is that the procedure followed in the AER approach suffers from data mining. Since generally only the final model is presented by the researcher, no information is available regarding the number of variables used in the model before obtaining the ‘final’ model results.
2 Another criticism is that the alterations to the original model are carried out in an arbitrary manner, based mainly on the beliefs of the researcher. It is therefore quite possible for two different researchers examining the same case to arrive at totally different conclusions.
3 By definition, the initial starting model is incorrect as it has omitted variables. This means that all the diagnostic tests on this model are incorrect, so we may consider important variables to be insignificant and exclude them.

经济代写|计量经济学代写Econometrics代考|The Hendry ‘general to specific approach’

Following from these three major criticisms of the AER, an alternative approach has been developed called the ‘general to specific approach’ or the Hendry approach, because it was developed mainly by Professor Hendry of the London School of Economics (see Hendry and Richard, 1983). The approach is to start with a general model that contains – nested within it as special cases – other, simpler, models. Let’s use an example to understand this better. Assume that we have a variable $Y$ that can be affected by two explanatory variables $X$ and $Z$. The general to specific approach proposes as a starting point the estimation of the following regression equation:
$$
\begin{aligned}
Y_t= & a+\beta_0 X_t+\beta_1 X_{t-1}+\beta_2 X_{t-2}+\cdots+\beta_m X_{t-m} \
& +\gamma_0 Z_t+\gamma_1 Z_{t-1}+\gamma_2 Z_{t-2}+\cdots+\gamma_m Z_{t-m} \
& +\delta_1 Y_{t-1}+\delta_2 Y_{t-2}+\cdots+\delta_m Y_{t-m}+u_t
\end{aligned}
$$
that is, to regress $Y_t$ on contemporaneous and lagged terms $X_t$ and $Z_t$ as well as lagged values of $Y_t$. This model is called an autoregressive (because lagged values of the dependent variable appear as regressors as well) distributed lag (because the effect of $X$ and $Z$ on $Y$ is spread over a period of time from $t-m$ to $t$ ) model (ARDL). Models such as that shown in Equation (8.69) are known as dynamic models because they examine the behaviour of a variable over time.

The procedure then is, after estimating the model, to apply appropriate tests and to narrow down the model to the simpler ones that are nested with the previously estimated model.

Consider the above example for $m=2$ to see how to proceed in practice with this approach. We have the original model:
$$
\begin{aligned}
Y_t= & a+\beta_0 X_t+\beta_1 X_{t-1}+\beta_2 X_{t-2} \
& +\gamma_0 Z_t+\gamma_1 Z_{t-1}+\gamma_2 Z_{t-2}+\delta_1 Y_{t-1}+\delta_2 Y_{t-2}+u_t
\end{aligned}
$$
where one restriction may be that all the $X$ s are non-important in the determination of $Y$. For this we have the hypothesis $H_0: \beta_0=\beta_1=\beta_2=0$; and if we accept that, we have a simpler model such as:
$$
Y_t=a+\gamma_0 Z_t+\gamma_1 Z_{t-1}+\gamma_2 Z_{t-2}+\delta_1 Y_{t-1}+\delta_2 Y_{t-2}+u_t
$$
Another possible restriction may be that the second lagged term of each variable is insignificant; that is hypothesis $H_0: \beta_2=\gamma_2=\delta_2=0$. Accepting this restriction will give the following model:
$$
Y_t=a+\beta_0 X_t+\beta_1 X_{t-1}+\gamma_0 Z_t+\gamma_1 Z_{t-1}+\delta_1 Y_{t-1}+u_t
$$
It should be clear by now that the models in Equations (8.71) and ( 8.72$)$ are both nested versions of the initial model in Equation (8.70); but Equation ( 8.72$)$ is not a nested model of Equation (8.71) and therefore we cannot proceed to Equation (8.72) after estimating Equation (8.71).

计量经济学代考

经济代写|计量经济学代写Econometrics代考|The traditional view: average economic regression

过去，传统的计量经济学建模方法是从制定最简单的模型开始，以遵循基本的经济理论，然后在估计该模型后，进行各种测试以确定它是否令人满意。

从这个意义上说，一个令人满意的模型是：(a) 一个具有显着系数（即高吨-比率）和符号与理论预测一致的系数；(b) 一个合适的（即高R2); (c) 具有不受自相关或异方差性影响的残差。

如果违反了其中一个或多个点，研究人员将尝试寻找更好的估计方法（即 Cochrane-Orcutt 迭代估计方法用于序列相关的情况）或检查其他可能的偏差原因，例如重要变量是否具有从模型中省略或是否包含冗余变量，或考虑替代函数形式等。

这种方法本质上是从一个简单的模型开始，然后根据情况的需要“建立”模型，被称为“从简单到通用的方法”或“平均经济回归 (AER)”，这是 Gilbert（1986 年）创造的一个术语), 因为这是大多数传统计量经济学研究在实践中所遵循的方法。
AER 方法一直受到重大批评：
1 一个明显的批评是 AER 方法中遵循的过程受到数据挖掘的影响。由于研究人员通常只提供最终模型，因此在获得“最终”模型结果之前，没有关于模型中使用的变量数量的信息。
2 另一个批评是，对原始模型的改动主要基于研究人员的信念，以任意方式进行。因此，两个不同的研究人员研究同一个案例很可能得出完全不同的结论。
3 根据定义，初始模型是不正确的，因为它遗漏了变量。这意味着在这个模型上的所有诊断测试都是不正确的，所以我们可以认为重要的变量是无关紧要的并排除它们。

经济代写|计量经济学代写Econometrics代考|The Hendry ‘general to specific approach’

在对 AER 的这三个主要批评之后，另一种方法被开发出来，称为“从一般到特定的方法”或亨德利方法，因为它主要是由伦敦经济学院的亨德利教授开发的 (见亨德利和理查德，1983 年) ). 该方法是从一个通用模型开始，该模型包含 – 作为特殊情况嵌套在其中 – 其他更简单的模型。让我们用一个例子来更好地理解这一点。假设我们有一个变量 $Y$ 受两个解释变量的影响 $X$ 和 $Z$. 从一般到具体的方法建议将以下回归方程的估计作为起点:
$$
Y_t=a+\beta_0 X_t+\beta_1 X_{t-1}+\beta_2 X_{t-2}+\cdots+\beta_m X_{t-m} \quad+\gamma_0 Z_t+\gamma_1 Z_{t-1}+\gamma_2 Z_{t-2}+\cdots+
$$
也就是说，回归 $Y_t$ 同期和滞后条款 $X_t$ 和 $Z_t$ 以及滞后值 $Y_t$. 该模型称为自回归 (因为因变量的滞后值也显示为回归变量) 分布滞后 (因为 $X$ 和 $Z$ 在 $Y$ 分布在一段时间内 $t-m$ 到 $t$ ) 模型 (ARDL)。等式 (8.69) 中所示的模型被称为动态模型，因为它们检查变量随时间的行为。
接下来的过程是，在估计模型之后，应用适当的测试并将模型缩小到与先前估计的模型嵌套的更简单的模型。
考虑上面的例子 $m=2$ 看看如何在实践中使用这种方法。我们有原始模型:
$$
Y_t=a+\beta_0 X_t+\beta_1 X_{t-1}+\beta_2 X_{t-2} \quad+\gamma_0 Z_t+\gamma_1 Z_{t-1}+\gamma_2 Z_{t-2}+\delta_1 Y_{t-1}+\delta_2 Y_{t-2}+u_t
$$
其中一个限制可能是所有 $X \mathrm{~s}$ 在确定时不重要 $Y$. 为此，我们有假设 $H_0: \beta_0=\beta_1=\beta_2=0$; 如果我们接受这一点，我们就会有一个更简单的模型，例如:
$$
Y_t=a+\gamma_0 Z_t+\gamma_1 Z_{t-1}+\gamma_2 Z_{t-2}+\delta_1 Y_{t-1}+\delta_2 Y_{t-2}+u_t
$$
另一个可能的限制可能是每个变量的第二个滞后项是微不足道的；那是假设 $H_0: \beta_2=\gamma_2=\delta_2=0$. 接受此限制将给出以下模型:
$$
Y_t=a+\beta_0 X_t+\beta_1 X_{t-1}+\gamma_0 Z_t+\gamma_1 Z_{t-1}+\delta_1 Y_{t-1}+u_t
$$
现在应该清楚方程 (8.71) 和 ( 8.72)都是等式 (8.70) 中初始模型的嵌套版本；但方程式 ( 8.72)不是方程 (8.71) 的嵌套模型，因此我们不能在估计方程 (8.71) 后继续方程 (8.72)。

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

经济代写|ECON335 Econometrics

Posted on 2023年4月29日2023年4月24日 by statistics-lab

Statistics-lab™可以为您提供colostate.edu ECON335 Econometrics计量经济学课程的代写代考和辅导服务！

ECON335 Econometrics课程简介

The objective of this course is to provide the basic knowledge of econometrics that is essential equipment for any serious economist or social scientist. The course introduces statistical tools including regression analysis and its application using cross-sectional data.
The second week onwards will be focused on how various technical problems inherent in economic analysis, including heteroskedasticity, autocorrelation, and endogeneity should be handled. This section of the course will pay special attention to the application of the regression model to time-series data – both stationary and non-stationary.
Using the theories and their application in economics, you will participate in daily workshops to get hands-on experience implementing the various estimators and testing procedures in Stata using real-world data. As a result, you will consider how the theory can be applied to a wide range of questions of economic interest (For example, modelling long-term relationships between prices and exchange rates).
By the end of the course, you will be able to provide proof of the unbiasedness or biasedness and consistency or inconsistency of least squares, and instrumental variable estimators using simple models.

PREREQUISITES

It seems like you are describing a course in econometrics that aims to equip students with basic knowledge and skills in statistical analysis, with a focus on regression analysis and its application to cross-sectional and time-series data in economics. The course also covers various technical problems that can arise in econometric analysis, such as heteroskedasticity, autocorrelation, and endogeneity, and how to address them.

In addition to theoretical instruction, the course provides practical workshops to give students hands-on experience using statistical software (such as Stata) to implement various estimators and testing procedures on real-world data. The course aims to help students apply econometric theory to a wide range of economic questions, such as modelling long-term relationships between prices and exchange rates.

By the end of the course, students should be able to evaluate the unbiasedness or biasedness and consistency or inconsistency of least squares and instrumental variable estimators using simple models.

ECON335 Econometrics HELP（EXAM HELP， ONLINE TUTOR）

问题 1.

Run the following auxiliary regression:
$$
\ln \left(\hat{u}i^2\right)=a_1+a_2 Z{2 i}+a_3 Z_{3 i}+\cdots+a_p Z_{p i}+v_i
$$

问题 2.

Formulate the null and the alternative hypotheses. The null hypothesis of homoskedasticity is:
$$
\mathrm{H}_0: \quad a_1=a_2=\cdots=a_p=0
$$
while the alternative is that at least one of the $a$ is different from zero.

问题 3.

Compute the $L M=n R^2$ statistic, where $n$ is the number of observations used in order to estimate the auxiliary regression in Step 2, and $R^2$ is the coefficient of determination of this regression. The $L M$ statistic follows the $\chi^2$ distribution with $p-1$ degrees of freedom.

问题 4.

Reject the null and conclude that there is significant evidence of heteroskedasticity when $L M$-statistical is greater than the critical value (LM-stat > $\left.\chi_{p-1, \alpha}^2\right)$. Alternatively, compute the $p$-value and reject the null if the $p$-value is less than the level of significance $\alpha$ (usually $\alpha=0.05$ ).

Textbooks

Statistics-lab™可以为您提供colostate.edu ECON335 Econometrics计量经济学课程的代写代考和辅导服务！请认准Statistics-lab™. Statistics-lab™为您的留学生涯保驾护航。

物理代写|流体力学代写Fluid Mechanics代考|One-Equation Model by Prandtl

Posted on 2023年4月28日2023年4月28日 by statistics-lab

如果你也在怎样代写流体力学Fluid Mechanics这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

流体力学是物理学的一个分支，涉及流体（液体、气体和等离子体）的力学和对它们的力。它的应用范围很广，包括机械、土木工程、化学和生物医学工程、地球物理学、海洋学、气象学、天体物理学和生物学。

statistics-lab™ 为您的留学生涯保驾护航在代写流体力学Fluid Mechanics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。我们的专家在代写流体力学Fluid Mechanics代写方面经验极为丰富，各种代写流体力学Fluid Mechanics相关的作业也就用不着说。

我们提供的流体力学Fluid Mechanics及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(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

物理代写|流体力学代写Fluid Mechanics代考|One-Equation Model by Prandtl

A one-equation model is an enhanced version of the algebraic models we discussed in previous sections. This model utilizes one turbulent transport equation originally developed by Prandtl. Based on purely dimensional arguments, Prandtl proposed a relationship between the dissipation and the kinetic energy that reads
$$
\varepsilon=C_D k^{3 / 2} / l_t
$$
where the turbulence length scale $\ell_{\mathrm{t}}$ is set proportional to the mixing length, $\ell_{\mathrm{m}}$, the boundary layer thickness $\delta$ or a wake or a jet width. The velocity scale in Eq. (9.132) is set proportional to the turbulent kinetic energy $V_t \propto k^{1 / 2}$ as suggested independently by Kolmogorov [95] and Prandtl [96]. Thus, the expression for the turbulent viscosity becomes:
$$
\mu_t=C_\mu \ell_m k^{0.5}
$$
with the constant $C_\mu$ to be determined from the experiment. The turbulent kinetic energy, $k$, as a transport equation is taken from Sect. 9.2.2 in the form of Eqs. (9.111) or (9.126) where the dissipation is implemented. For simple two-dimensional flows where no separation occurs, with the mean-flow component $\overline{V_1} \equiv \bar{U}$ as the significant velocity in $x_1 \equiv x$-direction, and the distance from the wall $x_2 \equiv y$, the following approximation by Launder and Spalding [97] may be used
$$
\rho \frac{\mathrm{D} k}{\mathrm{D} t}=\mu_t\left(\frac{\partial \bar{U}}{\partial y}\right)^2+\frac{\partial}{\partial y}\left(\frac{\mu_t}{\sigma_k} \frac{\partial k}{\partial y}\right)-C_D \frac{\rho k^{3 / 2}}{\ell_m},
$$
where $\sigma_k=1$ and $C_D=0.08$ are coefficients determined from experiments utilizing simple flow configurations. The one-equation model provides a better assumption for the velocity scale $V_{\mathrm{t}}$ than $\ell_m|\partial \bar{U} / \partial y|$. Similar to the algebraic model, the oneequation one is not applicable to the general three-dimensional flow cases since a general expression for the mixing length does not exist. Therefore the use of a oneequation model does not offer any improvement compared with the algebraic one. The one-equation models discussed above are based on kinetic energy equations. There are a variety of one-equation models that are based on Prandtl’s concept and discussed in [88].

物理代写|流体力学代写Fluid Mechanics代考|Two-Equation k − ε Model

The two equations utilized by this model are the transport equations of kinetic energy $k$ and the transport equation for dissipation $\varepsilon$. These equations are used to determine the turbulent kinematic viscosity $v_t$. For fully developed high Reynolds number turbulence, the exact transport equations for $k(9.126)$ can be used. The transport equation for $\varepsilon(9.129)$ includes triple correlations that are almost impossible to measure. Therefore, relative to $\varepsilon$, we have to replace it with a relationship that approximately resembles the terms in Eq. (9.129). To establish such a purely empirical relationship, dimensional analysis is heavily used. Launder and Spalding [98] used the following equations for kinetic energy
$$
\frac{\mathrm{D} k}{\mathrm{D} t}=\frac{1}{\rho} \frac{\partial}{\partial x_j}\left(\frac{\mu_t}{\sigma_k} \frac{\partial k}{\partial x_j}\right)+\frac{\mu_t}{\rho}\left(\frac{\partial \bar{V}i}{\partial x_j}+\frac{\partial \bar{V}_j}{\partial x_i}\right) \frac{\partial \bar{V}_i}{\partial x_j}-\varepsilon $$ and for dissipation $$ \frac{\mathrm{D} \varepsilon}{\mathrm{D} t}=\frac{1}{\rho} \frac{\partial}{\partial x_j}\left(\frac{\mu_t}{\sigma{\varepsilon}} \frac{\partial \varepsilon}{\partial x_j}\right)+C_{\varepsilon 1} \frac{\mu_t}{\rho} \frac{\varepsilon}{k}\left(\frac{\partial \bar{V}i}{\partial x_j}+\frac{\partial \bar{V}_j}{\partial x_i}\right) \frac{\partial \bar{V}_i}{\partial x_j}-\frac{C{\varepsilon 2} \varepsilon^2}{k},
$$
and the turbulent viscosity, $\mu_t$, can be expressed as
$$
\mu_t=v_t \rho=\frac{C_\mu \rho k^2}{\varepsilon}
$$
The constants $\sigma_{\mathrm{k}}, \sigma_{\varepsilon}, C_{\varepsilon_1}, C_{\varepsilon_2}$ and $C_\mu$ listed in Table 9.2 are calibration coefficients that are obtained from simple flow configurations such as grid turbulence. The models are applied to such flows and the coefficients are determined to make the model simulate the experimental behavior. The values of the above constants recommended by Launder and Spalding [83] are given in Table 9.2.
As seen, the simplified Eqs. (9.165) and (9.166) do not contain the molecular viscosity. They may be applied to free turbulence cases where the molecular viscosity is negligibly small compared to the turbulence viscosity. However, one cannot expect to obtain reasonable results by simulation of the wall turbulence using these equations.

流体力学代写

物理代写|流体力学代写Fluid Mechanics代考|One-Equation Model by Prandtl

单方程模型是我们在前面部分讨论的代数模型的增强版本。该模型使用了一个最初由 Prandtl 开发的湍流输运方程。基于纯量纲论证，普朗特提出了耗散和动能之间的关系，即
$$
\varepsilon=C_D k^{3 / 2} / l_t
$$
其中湍流长度尺度 $\ell_{\mathrm{t}}$ 与混合长度成正比， $\ell_{\mathrm{m}}$ ，边界层厚度 $\delta$ 或尾流或射流宽度。等式中的速度标度。(9.132) 设置为与湍流动能成正比 $V_t \propto k^{1 / 2}$ 正如 Kolmogorov [95] 和 PrandtI [96] 独立建议的那样。因此，湍流粘度的表达式变为:
$$
\mu_t=C_\mu \ell_m k^{0.5}
$$
与常数 $C_\mu$ 由实验确定。湍动能， $k$ ，因为传输方程取自 Sect。9.2.2 以方程式的形式。(9.111) 或 (9.126) 实现耗散的地方。对于没有发生分离的简单二维流，具有平均流分量 $\overline{V_1} \equiv \bar{U}$ 作为显着速度 $x_1 \equiv x$-方向，以及与墙的距离 $x_2 \equiv y$ ，可以使用 Launder 和 Spalding [97] 的以下近似值
$$
\rho \frac{\mathrm{D} k}{\mathrm{D} t}=\mu_t\left(\frac{\partial \bar{U}}{\partial y}\right)^2+\frac{\partial}{\partial y}\left(\frac{\mu_t}{\sigma_k} \frac{\partial k}{\partial y}\right)-C_D \frac{\rho k^{3 / 2}}{\ell_m},
$$
在哪里 $\sigma_k=1$ 和 $C_D=0.08$ 是通过使用简单流量配置的实验确定的系数。单方程模型为速度尺度提供了更好的假设 $V_{\mathrm{t}}$ 比 $\ell_m|\partial \bar{U} / \partial y|$. 与代数模型类似，一个方程不适用于一般的三维流动情况，因为混合长度的一般表达式不存在。因此，与代数模型相比，单方程模型的使用没有提供任何改进。上面讨论的单方程模型基于动能方程。有多种基于 Prandtl 概念并在 [88] 中讨论的单方程模型。

物理代写|流体力学代写Fluid Mechanics代考|Two-Equation k − ε Model

该模型使用的两个方程是动能的输运方程 $k$ 和耗散的传输方程 $\varepsilon$. 这些方程式用于确定湍流运动粘度 $v_t$. 对于完全发展的高雷诺数湍流，精确的输运方程为 $k(9.126)$ 可以使用。输运方程为 $\varepsilon(9.129)$ 包括几乎无法测量的三重相关性。因此，相对于 $\varepsilon$ ，我们必须将其替换为近似类似于方程式中的项的关系。(9.129)。为了建立这种纯粹的经验关系，量纲分析被大量使用。Launder 和 Spalding [98] 使用以下方程计算动能
$$
\frac{\mathrm{D} k}{\mathrm{D} t}=\frac{1}{\rho} \frac{\partial}{\partial x_j}\left(\frac{\mu_t}{\sigma_k} \frac{\partial k}{\partial x_j}\right)+\frac{\mu_t}{\rho}\left(\frac{\partial \bar{V} i}{\partial x_j}+\frac{\partial \bar{V}j}{\partial x_i}\right) \frac{\partial \bar{V}_i}{\partial x_j}-\varepsilon $$ 和消散 $$ \frac{\mathrm{D} \varepsilon}{\mathrm{D} t}=\frac{1}{\rho} \frac{\partial}{\partial x_j}\left(\frac{\mu_t}{\sigma \varepsilon} \frac{\partial \varepsilon}{\partial x_j}\right)+C{\varepsilon 1} \frac{\mu_t}{\rho} \frac{\varepsilon}{k}\left(\frac{\partial \bar{V} i}{\partial x_j}+\frac{\partial \bar{V}j}{\partial x_i}\right) \frac{\partial \bar{V}_i}{\partial x_j}-\frac{C \varepsilon 2 \varepsilon^2}{k}, $$ 和湍流粘度， $\mu_t$ ，可以表示为 $$ \mu_t=v_t \rho=\frac{C\mu \rho k^2}{\varepsilon}
$$
常量 $\sigma_{\mathrm{k}}, \sigma_{\varepsilon}, C_{\varepsilon_1}, C_{\varepsilon_2}$ 和 $C_\mu$ 表 9.2 中列出的是从简单的流动配置（例如网格湍流) 中获得的校准系数。将模型应用于此类流动并确定系数以使模型模拟实验行为。Launder 和 Spalding [83] 推荐的上述常数值在表 9.2 中给出。
如图所示，简化的方程式。(9.165) 和 (9.166) 不包含分子粘度。它们可以应用于分子粘度与湍流粘度相比小到可以忽略不计的自由湍流情况。然而，不能期望通过使用这些方程模拟壁湍流来获得合理的结果。

物理代写|流体力学代写Fluid Mechanics代考请认准statistics-lab™

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

物理代写|流体力学代写Fluid Mechanics代考|Cebeci–Smith Model

Posted on 2023年4月28日2023年4月28日 by statistics-lab

如果你也在怎样代写流体力学Fluid Mechanics这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

我们提供的流体力学Fluid Mechanics及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(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

物理代写|流体力学代写Fluid Mechanics代考|Cebeci–Smith Model

Another algebraic model is the Cebeci-Smith [92] which has been used primarily in external high speed aerodynamics with attached thin boundary layer. It is a two-layer algebraic zero-equation model which gives the eddy viscosity by separate expressions in each layer, as a function of the local boundary layer velocity profile. The model is not suitable for cases with large separated regions and significant curvature/rotation effects. The turbulent kinematic viscosity for the inner layer is calculated from
$$
v_{t i}=l_m^2\left[\left(\frac{\partial U}{\partial y}\right)^2+\left(\frac{\partial U}{\partial x}\right)^2\right]^{\frac{1}{2}} .
$$
For the outer layer kinematic viscosity is
$$
v_{t_0}=\alpha U_e \delta_1 F_{K l}(y ; \delta)
$$
with
$$
F_{K l}(y ; \delta)=\left[1+5.5\left(\frac{y}{\delta}\right)^6\right]^{-1} \text { and } \delta_1=\int_0^\delta\left(1-U / U_e\right) d y
$$
$\alpha=0.0168, U_e$ the velocity at the edge of the boundary layer, $\delta_1$ the boundary layer displacement thickness and $F_{K l}$ as the Klebanoff intermittency function [93]. The mixing length in Eq. (9.151) is determined by combining Eqs. (9.143) and (9.144)
$$
l_m=\kappa y\left(1-e^{-y^{+} / A^{+}}\right)
$$
with $\kappa=0.4$ and $A^{+}=26\left(1+y \frac{d p / d x}{\rho u_\tau^2}\right)^{-1 / 2}$.

物理代写|流体力学代写Fluid Mechanics代考|Baldwin–Lomax Algebraic Model

The third algebraic model is the Baldwin-Lomax model [94]. The basic structure of this model is essentially the same as the Cebeci-Smith model with the exception of a few minor changes. Similar to Cebeci-Smith, this model is a two-layer algebraic zero-equation model which gives the eddy kinematic viscosity $v_t$ as a function of the local boundary layer velocity profile. The model is suitable for high-speed flows with thin attached boundary-layers, typically present in aerospace and turbomachinery applications. While this model is quite robust and provides quick results, it is not capable of capturing details of the flow field. Since this model is not suitable for calculating flow situations with separation, its applicability is limited. We briefly summarize the structure of this model as follows. The kinematic viscosity for the inner layer is
$$
v_{t_i}=l_m^2|\Omega|
$$
with
$$
l_m=\kappa y\left(1-e^{-y^{+} / A_0}\right)
$$
and $\Omega=e_i e_j \omega_{i j}$ as the rotation tensor. The outer layer is described by
$$
v_{t 0}=\alpha C_{\mathrm{cp}} F_{\mathrm{wake}} F_{\mathrm{kl}}\left(y, y_{\max } / C_{\mathrm{Kleb}}\right)
$$
with the wake function $F_{\text {wake }}$
$$
F_{\text {wake }}=\min \left(y_{\max } F_{\max } ; C_{\mathrm{wk}} y_{\max } U_{\mathrm{diff}} / F_{\max }\right)
$$
and $F_{\max }$ and $y_{\max }$ as the maximum of the function
$$
F(y)=y|\Omega|\left(1-e^{-y^{+} / A_0}\right)
$$
The velocity difference $U_{\text {diff }}$ is defined as the difference of the velocity at $y_{\max }$ and $y_{\min }$ :
$$
U_{\mathrm{diff}}=\operatorname{Max}\left(\sqrt{U_i U_i}\right)-\operatorname{Min}\left(\sqrt{U_i U_i}\right)
$$
with the closure coefficients listed in Table 9.1.
The above zero-equation models are applied to cases of free turbulent flow such as wake flow, jet flow, and jet boundaries.

流体力学代写

物理代写|流体力学代写Fluid Mechanics代考|Cebeci–Smith Model

另一个代数模型是 Cebeci-Smith [92]，它主要用于具有附加薄边界层的外部高速空气动力学。它是一个双层代数零方程模型，通过每层中的单独表达式给出涡粘性，作为局部边界层速度剖面的函数。该模型不适用于分离区域较大且曲率/旋转效应显着的情况。内层的湍流运动粘度由下式计算
$$
v_{t i}=l_m^2\left[\left(\frac{\partial U}{\partial y}\right)^2+\left(\frac{\partial U}{\partial x}\right)^2\right]^{\frac{1}{2}}
$$
对于外层运动粘度为
$$
v_{t_0}=\alpha U_e \delta_1 F_{K l}(y ; \delta)
$$
和
$$
F_{K l}(y ; \delta)=\left[1+5.5\left(\frac{y}{\delta}\right)^6\right]^{-1} \text { and } \delta_1=\int_0^\delta\left(1-U / U_e\right) d y
$$
$\alpha=0.0168, U_e$ 边界层边缘的速度， $\delta_1$ 边界层位移厚度和 $F_{K l}$ 作为 Klebanoff 间歇函数 [93]。方程式中的混合长度。(9.151) 是通过结合等式来确定的。(9.143) 和 (9.144)
$$
l_m=\kappa y\left(1-e^{-y^{+} / A^{+}}\right)
$$
$$
\text { 和 } \kappa=0.4 \text { 和 } A^{+}=26\left(1+y \frac{d p / d x}{\rho u_\tau^2}\right)^{-1 / 2} \text {. }
$$

物理代写|流体力学代写Fluid Mechanics代考|Baldwin–Lomax Algebraic Model

第三个代数模型是 Baldwin-Lomax 模型 [94]。该模型的基本结构与 Cebeci-Smith 模型基本相同，只是有一些小的变化。类似于 Cebeci-Smith，该模型是一个双层代数零方程模型，给出了渗流运动粘度 $v_t$ 作为局部边界层速度剖面的函数。该模型适用于具有薄附看边界层的高速流动，通常存在于航空航天和涡轮机械应用中。虽然此模型非常稳健并且可以快速提供结果，但它无法捕获流场的细节。由于该模型不适用于计算有分离的流动情况，其适用性受到限制。我们简要总结该模型的结构如下。内层的运动粘度为
$$
v_{t_i}=l_m^2|\Omega|
$$
和
$$
l_m=\kappa y\left(1-e^{-y^{+} / A_0}\right)
$$
和 $\Omega=e_i e_j \omega_{i j}$ 作为旋转张量。外层描述为
$$
v_{t 0}=\alpha C_{\mathrm{cp}} F_{\text {wake }} F_{\mathrm{kl}}\left(y, y_{\max } / C_{\mathrm{Kleb}}\right)
$$
带唤醒功能 $F_{\text {wake }}$
$$
F_{\text {wake }}=\min \left(y_{\max } F_{\max } ; C_{\mathrm{wk}} y_{\max } U_{\mathrm{diff}} / F_{\max }\right)
$$
和 $F_{\max }$ 和 $y_{\max }$ 作为函数的最大值
$$
F(y)=y|\Omega|\left(1-e^{-y^{+} / A_0}\right)
$$
速度差 $U_{\text {diff }}$ 被定义为速度的差异 $y_{\max }$ 和 $y_{\text {min }}$ :
$$
U_{\mathrm{diff}}=\operatorname{Max}\left(\sqrt{U_i U_i}\right)-\operatorname{Min}\left(\sqrt{U_i U_i}\right)
$$
与表 9.1 中列出的闭合系数。
上述零方程模型适用于尾流、射流和射流边界等自由湍流的情况。

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

物理代写|广义相对论代写General relativity代考|Derivation of Hubble’s Law

Posted on 2023年4月28日2023年4月28日 by statistics-lab

如果你也在怎样代写广义相对论General relativity这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

广义相对论是阿尔伯特-爱因斯坦在1907至1915年间提出的引力理论。广义相对论说，观察到的质量之间的引力效应是由它们对时空的扭曲造成的。

statistics-lab™ 为您的留学生涯保驾护航在代写广义相对论General relativity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。我们的专家在代写广义相对论General relativity代写方面经验极为丰富，各种代写广义相对论General relativity相关的作业也就用不着说。

我们提供的广义相对论General relativity及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(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

物理代写|广义相对论代写General relativity代考|Derivation of Hubble’s Law

Consider a light ray propagation from a distant galaxy at $\left(r_1, \theta_1, \phi_1\right)$ towards $r=0$. The equations of a null geodesic imply that this light ray moves along the path $\theta=\theta_1, \phi=\phi_1$. Suppose the present epoch is denoted by $t=t_0$ and let a right ray leave the source at $t=t_1$. Then the condition for the ray to reach at $r=0$, at $t=t_0$
$$
\int_{t_1}^{t_0} \frac{c d t}{a(t)}=\int_0^{r_1} \frac{d r}{\sqrt{1-k r^2}}=f\left(r_1\right) .
$$
Assuming that $r_1$ is small for nearby objects, we then get approximately,
$$
f\left(r_1\right) \cong r_1 \cong \frac{c\left(t_0-t_1\right)}{a\left(t_0\right)}
$$

Now Taylor’s expansion near $t_0$,
$$
\begin{aligned}
a\left(t_1\right) & \cong a\left(t_0\right)+\left(t_1-t_0\right) \dot{a}\left(t_0\right)=a\left(t_0\right)\left[1-\left(t_0-t_1\right) \frac{\dot{a}\left(t_0\right)}{a\left(t_0\right)}\right], \
& =a\left(t_0\right)\left[1-\left(t_0-t_1\right) H_0\right] \text { where } H_0=\frac{\dot{a}\left(t_0\right)}{a\left(t_0\right)} .
\end{aligned}
$$
Also, we know
$$
(1+z)^{-1}=\frac{a\left(t_1\right)}{a\left(t_0\right)} \cong 1-\left(t_0-t_1\right) H_0 .
$$
For, small redshift $z$, we have
$$
1-z \cong 1-\left(t_0-t_1\right) H_0
$$
This implies
$$
\begin{gathered}
c z \cong r_1 a\left(t_0\right) H_0=D_1 H_0, \
\text { where } D_1=r_1 a\left(t_0\right),
\end{gathered}
$$
may be defined as a proper distance at the epoch $t_0$.
From a Doppler shift point of view, cz may be identified with the velocity of recession of a galaxy in proportion to its distance from us. This is Hubble’s law and $H_0$ is the Hubble’s constant given by
$$
H_0=\frac{\dot{a}\left(t_0\right)}{a\left(t_0\right)}
$$

物理代写|广义相对论代写General relativity代考|Angular Size

The angular measurement describing how a large sphere or circle looks from a given point of view is the angular diameter or angular size. In Euclidean geometry, the diameter $d$ is related to the observed angle $\Delta \theta$ as
$$
\Delta \theta=\frac{d}{r},
$$
where $r$ is its distance. However, for curved spacetime, one needs to use $\mathrm{R}-\mathrm{W}$ spacetime.
Let us consider a galaxy $G_1$ having linear extend $d(\overline{A B})$ and the angle subtended by this galaxy $G_1$ at the observer $\mathrm{O}$ is $\Delta \theta_1$ (see Fig. 101). Consider two neighboring null geodesics (representing light rays) from the two points $A, B$ at the two extremities of $G_1$. Without any loss of generality, we can select the coordinates of $A$ and $B$ as $\left(\theta_1, \phi_1\right)$ and $\left(\theta_1+\Delta \theta_1, \phi_1\right)$, respectively. For the curved space, we use R-W line element to find the proper distance between $A$ and $B$. Now, plugging $t=t_1=$ constant, $r=r_1=$ constant, $\phi=\phi_1=$ constant, and $d \theta=\Delta \theta_1$ in the R-W line element, we get
$$
d s^2=-r_1^2 a^2\left(t_1\right)\left(\Delta \theta_1\right)^2=-d^2
$$
[in $G_1$, the space-like separation $A B=d$, which is a rest frame] Thus,
$$
\begin{aligned}
\Delta \theta_1 & =\frac{d}{r_1 a\left(t_1\right)}=\frac{d(1+z)}{r_1 a\left(t_0\right)}, \quad \text { (using Eq. (11.30)) } \
& =\frac{d(1+z)}{D_1} .[\text { using Eq. (11.36)] }
\end{aligned}
$$
Note that in Euclidean space, $\Delta \theta_1$ is decreasing with an increase in the distance of the galaxy from us. However, in curved space, the result is different. For expanding universe, the scale factor $a(t)$ is increasing with time and consequently, $z$ will be increasing (by Eq. (11.31)). Therefore, it is not always true that $\Delta \theta_1$ decreases with time. Light from a galaxy takes much time to reach us for expanding universe.

广义相对论代考

物理代写|广义相对论代写General relativity代考|Derivation of Hubble’s Law

考虑来自遥远星系的光线传播 $\left(r_1, \theta_1, \phi_1\right)$ 向 $r=0$. 零测地线的方程意味着这条光线沿着路径移动 $\theta=\theta_1, \phi=\phi_1$. 假设当前纪元表示为 $t=t_0$ 并让右光线离开光源 $t=t_1$. 那么光线到达的条件是 $r=0$ ，在 $t=t_0$
$$
\int_{t_1}^{t_0} \frac{c d t}{a(t)}=\int_0^{r_1} \frac{d r}{\sqrt{1-k r^2}}=f\left(r_1\right)
$$
假如说 $r_1$ 对于附近的物体来说很小，然后我们得到大约，
$$
f\left(r_1\right) \cong r_1 \cong \frac{c\left(t_0-t_1\right)}{a\left(t_0\right)}
$$
现在泰勒的扩张临近 $t_0$ ，
$$
a\left(t_1\right) \cong a\left(t_0\right)+\left(t_1-t_0\right) \dot{a}\left(t_0\right)=a\left(t_0\right)\left[1-\left(t_0-t_1\right) \frac{\dot{a}\left(t_0\right)}{a\left(t_0\right)}\right], \quad=a\left(t_0\right)\left[1-\left(t_0-t_1\right) H_0\right]
$$
另外，我们知道
$$
(1+z)^{-1}=\frac{a\left(t_1\right)}{a\left(t_0\right)} \cong 1-\left(t_0-t_1\right) H_0
$$
对于，小红移 $z$ ，我们有
$$
1-z \cong 1-\left(t_0-t_1\right) H_0
$$
这意味着
$$
c z \cong r_1 a\left(t_0\right) H_0=D_1 H_0, \text { where } D_1=r_1 a\left(t_0\right),
$$
可以定义为纪元的适当距离 $t_0$.
从多普勒频移的角度来看， $c z$ 可以等同于星系的后退速度与其与我们的距离成正比。这是哈勃定律 $H_0$ 是哈勃常数
$$
H_0=\frac{\dot{a}\left(t_0\right)}{a\left(t_0\right)}
$$

物理代写|广义相对论代写General relativity代考|Angular Size

描述大球体或圆从给定角度看起来如何的角度测量是角直径或角大小。在欧氏几何中，直径 $d$ 与观察角度有关 $\Delta \theta$ 作为
$$
\Delta \theta=\frac{d}{r}
$$
在哪里 $r$ 是它的距离。然而，对于弯曲时空，需要使用 $\mathrm{R}-\mathrm{W}$ 时空。
让我们考虑一个星系 $G_1$ 有线性延伸 $d(\overline{A B})$ 以及这个星系所夹的角度 $G_1$ 在观察者 $\mathrm{O}$ 是 $\Delta \theta_1$ （见图 101）。从两个点考虑两个相邻的零测地线 (代表光线) $A, B$ 在两端 $G_1$. 不失一般性，我们可以选择坐标 $A$ 和 $B$ 作为 $\left(\theta_1, \phi_1\right)$ 和 $\left(\theta_1+\Delta \theta_1, \phi_1\right)$ ，分别。对于弯曲空间，我们使用RW线元来找到合适的距离 $A$ 和 $B$. 现在，堵 $t=t_1$ =持续的， $r=r_1=$ 持续的， $\phi=\phi_1=$ 常数，和 $d \theta=\Delta \theta_1$ 在 RW 行元素中，我们得到
$$
d s^2=-r_1^2 a^2\left(t_1\right)\left(\Delta \theta_1\right)^2=-d^2
$$
[在 $G_1$ ，类似空间的分离 $A B=d$ ，这是一个休息框架]因此，
$$
\Delta \theta_1=\frac{d}{r_1 a\left(t_1\right)}=\frac{d(1+z)}{r_1 a\left(t_0\right)}, \quad \text { (using Eq. (11.30)) } \quad=\frac{d(1+z)}{D_1} \cdot[\text { using Eq. (11.36)] }
$$
请注意，在欧几里德空间中， $\Delta \theta_1$ 随着银河系离我们的距离增加而减小。然而，在弯曲空间中，结果是不同的。对于膨胀的宇宙，比例因子 $a(t)$ 随时间增加，因此， $z$ 将增加（通过等式 (11.31)）。因此，这并不总是正确的 $\Delta \theta_1$ 随时间减少。来自星系的光需要很长时间才能到达我们以扩大宇宙。

物理代写|广义相对论代写General relativity代考请认准statistics-lab™

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

物理代写|广义相对论代写General relativity代考|Newtonian Cosmology

Posted on 2023年4月28日2023年4月28日 by statistics-lab

如果你也在怎样代写广义相对论General relativity这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。

广义相对论是阿尔伯特-爱因斯坦在1907至1915年间提出的引力理论。广义相对论说，观察到的质量之间的引力效应是由它们对时空的扭曲造成的。

我们提供的广义相对论General relativity及其相关学科的代写，服务范围广, 其中包括但不限于:

Statistical Inference 统计推断
Statistical Computing 统计计算
Advanced Probability Theory 高等概率论
Advanced Mathematical Statistics 高等数理统计学
(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

物理代写|广义相对论代写General relativity代考|Newtonian Cosmology

Let us consider the universe to be an immensely large sphere of gas (that means larger than we can imagine but not infinite). Treating the gas particles as galaxies, i.e., the universe is a huge sphere filled with the gas of galaxies and its volume is very large. Further, we consider that the gaseous sphere is isotropic and homogeneous. An observer or a point which is carried along with the expansion is said to be comoving. As the sphere is isotropic and homogeneous, the expansion is regulated by a single function of time and as a result, we can write the distance between any two comoving points at a time $t$ as
$$
r(t)=R(t) r_0
$$
where $r_0$ is a constant for the pair and $R(t)$, called the scale factor, is the universal expansion factor. Differentiating (11.18) with respect to time, we get,
$$
v(t)=\dot{r}(t)=H(t) r(t)
$$
where
$$
H(t)=\frac{\dot{R}(t)}{R(t)}
$$
$H(t)$ is called the Hubble’s parameter. Equation (11.19) is called Hubble’s law. Note that $H$ is a function of time.
It is customary to denote its present value by $H_0$, i.e.,
$$
H_0=\frac{\dot{R}\left(t_0\right)}{R\left(t_0\right)}
$$
where $t_0$ is the present moment.
Hubble’s law is consistent with the observation that all other galaxies are moving away from us. This indicates that the distance between two galaxies is increasing with the time that means the velocity of separation $v$ is a function of time. Let at the present time the separation distance be $r$, then there must have been a time $\tau$ in the past when the distance between them was very small. Thus, according to Eq. (11.19), we have
$$
\tau=\frac{r}{v}=\frac{1}{H_0}
$$

物理代写|广义相对论代写General relativity代考|Cosmological Redshift

The observed wavelengths of the spectral lines from a star are not the same as the original wavelengths of the spectral lines of the star. The lines are shifted to the red or blue due to the relative velocity between the earth and the star. If the star is approaching the earth then we get blue-shift and if the star is receding then one gets redshift. We will discuss how the shifted spectral lines are related to the scale factor.

Consider a distant galaxy situated at a point whose coordinates are $\left(r_1, \theta_1, \phi_1\right)$. It emits a light ray that propagates and reaches us $(r=0$ ). Light ray travels along a null geodesic. Without any loss of generality, we consider that the path of the light lies on the plane $\left(\theta=\theta_1, \phi=\phi_1\right)$. Suppose the present epoch is denoted by $t=t_0$ and let a light ray leave the source at $t=t_1$. For null geodesic, we have $d s=0$. Now using $d \theta=0, d \phi=0$, the R-W metric yields the following condition for the ray to arrive at $r=0$ at $t=t_0$
$$
\int_{t_1}^{t_0} \frac{c d t}{a(t)}=\int_0^{r_1} \frac{d r}{\left(1-k r^2\right)^{\frac{1}{2}}} .
$$
[In the null geodesics (with $d s=0, d \theta=d \phi=0$ ),
$$
\frac{c d t}{a(t)}= \pm \frac{d r}{\left(1-k r^2\right)^{\frac{1}{2}}}
$$
we should take minus sign in this relation as $r$ decreases as $t$ increases along this null geodesic]
Light wave starts at $r=r_1$ and reaches us at $r=0$. Let two successive crests of the wave leave at $t_1$ and $t_1+\Delta t_1$ and arrive at $t_0$ and $t_0+\Delta t_0$, respectively. Equation (11.27) yields
$$
\int_{t_1+\Delta t_1}^{t_0+\Delta t_0} \frac{c d t}{a(t)}=\int_0^{t_1} \frac{d r}{\sqrt{1-k r^2}}=\int_{t_1}^{t_0} \frac{c d t}{a(t)}
$$

广义相对论代考

物理代写|广义相对论代写General relativity代考|Newtonian Cosmology

让我们把宇宙想象成一个非常大的气体球体 (这意味着比我们想象的要大，但不是无限大)。把气体粒子看成星系，即宇宙是一个巨大的球体，里面充满了星系的气体，体积非常大。此外，我们认为气态球体是各向同性和均匀的。与膨胀一起携带的观察者或点被称为是同动的。由于球体是各向同性和均匀的，膨胀由时间的单一函数调节，因此，我们可以一次写出任意两个同动点之间的距离 $t$ 作为
$$
r(t)=R(t) r_0
$$
在哪里 $r_0$ 是一对常数，并且 $R(t)$ ，称为比例因子，是通用膨胀因子。对 (11.18) 关于时间微分，我们得到，
$$
v(t)=\dot{r}(t)=H(t) r(t)
$$
在哪里
$$
H(t)=\frac{\dot{R}(t)}{R(t)}
$$
$H(t)$ 称为哈勃参数。方程 (11.19) 称为哈勃定律。注意 $H$ 是时间的函数。通常用以下方式表示其现值 $H_0$ ，那是，
$$
H_0=\frac{\dot{R}\left(t_0\right)}{R\left(t_0\right)}
$$
在哪里 $t_0$ 是当下。
哈勃定律与所有其他星系都在远离我们的观察结果是一致的。这表明两个星系之间的距离随着时间的增加而增加，这意味着分离速度 $v$ 是时间的函数。让目前的间隔距离为 $r$ ，那么一定有一段时间 $\tau$ 在过去他们之间的距离很小的时候。因此，根据等式。(11.19)，我们有
$$
\tau=\frac{r}{v}=\frac{1}{H_0}
$$

物理代写|广义相对论代写General relativity代考|Cosmological Redshift

观测到的恒星光谱线波长与恒星光谱线的原始波长不同。由于地球和恒星之间的相对速度，这些线会移动到红色或蓝色。如果恒星正在接近地球，那么我们就会发生蓝移，如果恒星正在后退，那么我们就会发生红移。我们将讨论移动谱线如何与比例因子相关。
考虑一个位于坐标为 $\left(r_1, \theta_1, \phi_1\right)$. 它发出的光线传播并到达我们 $(r=0)$. 光线沿着零测地线传播。不失一般性，我们认为光的路径位于平面上 $\left(\theta=\theta_1, \phi=\phi_1\right)$. 假设当前纪元表示为 $t=t_0$ 让光线离开光源 $t=t_1$. 对于零测地线，我们有 $d s=0$. 现在使用 $d \theta=0, d \phi=0$ ，RW 度量产生以下光线到达的条件 $r=0$ 在 $t=t_0$
$$
\int_{t_1}^{t_0} \frac{c d t}{a(t)}=\int_0^{r_1} \frac{d r}{\left(1-k r^2\right)^{\frac{1}{2}}} .
$$
[在零测地线（与 $d s=0, d \theta=d \phi=0 ），$
$$
\frac{c d t}{a(t)}= \pm \frac{d r}{\left(1-k r^2\right)^{\frac{1}{2}}}
$$
我们应该在这个关系中取负号 $r$ 减少为 $t$ 沿着这个零测地线增加
光波开始于 $r=r_1$ 到达我们 $r=0$. 让两个连续的波峰离开 $t_1$ 和 $t_1+\Delta t_1$ 并到达 $t_0$ 和 $t_0+\Delta t_0$ ，分别。方程 (11.27) 产生
$$
\int_{t_1+\Delta t_1}^{t_0+\Delta t_0} \frac{c d t}{a(t)}=\int_0^{t_1} \frac{d r}{\sqrt{1-k r^2}}=\int_{t_1}^{t_0} \frac{c d t}{a(t)}
$$

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