统计代写|应用统计代写applied statistics代考|BEST PRACTICES FOR DATA ANALYSIS

如果你也在 怎样代写应用统计applied statistics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。

应用统计学包括计划收集数据,管理数据,分析、解释和从数据中得出结论,以及利用分析结果确定问题、解决方案和机会。本专业培养学生在数据分析和实证研究方面的批判性思维和解决问题的能力。

statistics-lab™ 为您的留学生涯保驾护航 在代写应用统计applied statistics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。我们的专家在代写应用统计applied statistics代写方面经验极为丰富,各种代写应用统计applied statistics相关的作业也就用不着说。

我们提供的应用统计applied statistics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
统计代写|应用统计代写applied statistics代考|BEST PRACTICES FOR DATA ANALYSIS

统计代写|应用统计代写applied statistics代考|BEST PRACTICES FOR DATA ANALYSIS

Once you have started analyzing your data, how should you actually go about the process? By that I don’t mean “sit at your computer and type code into $R$,” but what is the best way to think about running your analyses and coming to conclusions about your data?

The most important rule of the road is to have clear hypotheses at the outset of analyzing your data. You ran the experiment (presumably) and so you should have an idea of what questions you are trying to answer. It is important that you do not just try to measure everything under the

sun and then look for possible relationships between your variables. If you measure enough things, you are more likely to find a statistically significant relationship that may or may not be meaningful. This is a process known as $p$-hacking or data dredging. Looking for patterns in your data is certainly an okay thing to do, but you should avoid testing every possible predictor you can imagine in the hope of finding something significant that will make your data more publishable. This is particularly true when you have many possible predictor variables.

A second dubious practice which is to be avoided at all costs is HARKing, which stands for “hypothesizing after the results are known.” HARKing basically goes like this: 1) You start with a hypothesis that you want to test; 2) you test it and find a non-significant effect; 3) while doing your series of analyses you come across some other variable which does have a significant explanatory effect; and 4), you omit your original hypothesis and concoct a new hypothesis which fits the results of the study. Essentially, you are coming up with a prediction after you know the answer. It is certainly important to realize that you might learn something new when you analyze your data. Perhaps the unforeseen result causes you to see a new explanatory relationship which you had not considered before. That’s fine, but it is important to be transparent about your original hypothesis versus the new hypothesis. Moreover, you should not be testing all sorts of predictors that may or may not seem important and then trying to come up with explanatory hypotheses after the fact.

The last piece of advice regarding best practices is dont obsess over statistical significance. It has been said many times, but it bears repeating, that the $0.05$ cutoff that is traditionally used to demarcate “significance” is a completely arbitrary line in the sand. Why isn’t it $0.01$ or $0.10$ ? Obviously, the idea of statistical significance is still an oft-used metric in many fields, and we will certainly talk about significance in this book, but don’t obsess over it. It isn’t the be-all and end-all of your research endeavors, because lots of things affect your ability to detect “significance” beyond the validity of your hypotheses, the magnitude of biological affects you measure, or the strength of your data. The more data you have, the greater your ability to detect very small affects. Similarly, with very little data, you will have a hard time detecting even moderate affects. The more variable your data are from individual to individual, the harder a time you will have detecting significance. So, focus your energy on understanding and estimating the explanatory power of your predictor variables. What you want to know is if there is a relationship between your predictor and response variables and how confident you can be in any relationship you’ve tried to quantify, so try to keep that in your mind at all times while analyzing your data.

统计代写|应用统计代写applied statistics代考|HOW TO DECIDE BETWEEN COMPETING ANALYSES

Something we will explore later in this book with practical, real-world examples is how to choose amongst different possible ways to analyze your data. However, it is still useful to think about this issue up front, because it is an important one. Many times, you will be faced with different possible ways to analyze your data and different methods might have certain pros and cons.

At the end of the day, your job is to pick the statistical analysis that does the best job of representing and interpreting your data. Getting you to the point where you know how to evaluate your data and make that decision is one of the major goals of this book. Sometimes there will be two different types of models that may be essentially equivalent. They may fit equally well, and they may give you very similar answers about the significance of your predictors. Sometimes you might have to choose amongst competing models that give you different answers about the supposed significance (or not) of your predictors. How do you choose which model to go with?

You should be completely neutral about the idea of “significance” and only consider if the model is doing a good job and is appropriate for the data. As we will see in Chapter 7, there are times when you might have a model which might seem appropriate and gives you a highly significant result, but which actually fits very poorly. The opposite can be true as well, where you choose a model that fits poorly and gives you an underwhelming sense of the importance of your predictors. At the end of the day, you need to be able to justify your choice of model to yourself, your reviewers, your supervisors, your colleagues, and anyone else that might look at your analysis. You need to be able to stand behind your decision and explain why you chose one analysis over another. If you can do that, you’ll be in good shape.

统计代写|应用统计代写applied statistics代考|DATA ARE DATA ARE DATA

The data that we will work within this book come from a study I conducted as a postdoc working in the rainforests of Panama at the Smithsonian Tropical Research Institute. I have studied frogs and their eggs and tadpoles since I began my PhD in 2002. I love frogs and their eggs and tadpoles. I think they are the greatest. But, maybe you dont, and maybe you are thinking to yourself: “How do these weird tadpole data relate to me and my analyses? I’m never going to study tadpoles in Panama!” While that might be true, I think that data are data are data, and the spatially nested design of the study we will work through is extremely similar to many other study systems (Figure 2.2).

The shorthand name of the dataset we will work with is “RxP” which stands for “Resource X Predation,” which are the two main things we were manipulating in the study (Chapter 3 will describe the study in more detail). In the RxP dataset, we have individuals that are nested (or grouped, if you will) within tanks, and those tanks are themselves nested within blocks. This is the same as, for example, having multiple mice housed together in a single cage, and then having cages grouped together on different shelves. Or, you might imagine multiple fruit flies measured per vial, and vials are grouped together based on the date they were set up. Similarly, we can think about having multiple genetic strains (i.e., genotypes, families, etc.) that cluster our data into discrete groups. This sort of nested design can also be used to think about repeated measures data, where individuals are measured or sampled repeatedly over time.

Beyond the idea of physically clustered data, we can just think about the tanks as different replicates of an experiment, which is no different from any other experiment. The response variables (also called the “dependent variables”) that were measured during the RxP experiment-things like number of days until metamorphosis or size at metamorphosis-are no different than any other continuous variable that might be measured, be it the length of time a rat freezes after seeing a light flash or the number of pecks a pigeon makes before correctly opening a lever or the number of new leaves produced after adding fertilizer to a growing plant. The predictor variables (also called the “independent variables”) are generally categorical in their nature (i.e., they have several discrete categories), and could easily be replaced by drug treatment or maternal enrichment environment or fertilization regime. As I said earlier, data are data are data. See Figure $2.2$ for a visual depiction of the similarities in data structure.

统计代写|应用统计代写applied statistics代考|BEST PRACTICES FOR DATA ANALYSIS

应用统计代写

统计代写|应用统计代写applied statistics代考|BEST PRACTICES FOR DATA ANALYSIS

一旦你开始分析你的数据,你应该如何实际进行这个过程?我的意思不是“坐在你的电脑前输入代码”R,”但是考虑运行分析并对数据得出结论的最佳方式是什么?

最重要的规则是在分析数据的一开始就有明确的假设。您进行了实验(大概),因此您应该知道您要回答什么问题。重要的是,您不要仅仅尝试在

sun,然后寻找变量之间的可能关系。如果你测量了足够多的东西,你更有可能找到一个可能有意义也可能没有意义的统计显着关系。这是一个被称为p-黑客攻击或数据挖掘。在你的数据中寻找模式当然是一件好事,但你应该避免测试你能想象到的每一个可能的预测变量,以期找到一些重要的东西,让你的数据更容易发布。当您有许多可能的预测变量时尤其如此。

不惜一切代价避免的第二种可疑做法是 HARKing,它代表“知道结果后的假设”。HARKing 基本上是这样的: 1)你从一个你想要检验的假设开始;2)你测试它并发现一个不显着的效果;3)在进行一系列分析时,您会遇到一些其他变量,这些变量确实具有显着的解释效果;4),你省略了你原来的假设,并编造了一个符合研究结果的新假设。从本质上讲,您是在知道答案后提出预测。意识到在分析数据时可能会学到一些新东西,这一点当然很重要。或许意料之外的结果让你看到了一个你以前没有考虑过的新的解释关系。没关系,但重要的是要对您的原始假设与新假设保持透明。此外,您不应该测试所有可能看起来重要或可能不重要的预测变量,然后在事后试图提出解释性假设。

关于最佳实践的最后一条建议是不要沉迷于统计意义。已经说过很多次了,但值得重复的是,0.05传统上用来划分“重要性”的界限是一条完全任意的线。为什么不是0.01或者0.10? 显然,统计显着性的概念仍然是许多领域中经常使用的度量标准,我们肯定会在本书中讨论显着性,但不要太执着于它。这不是您研究工作的全部和最终目的,因为很多事情都会影响您检测“重要性”的能力,超出了假设的有效性、您测量的生物学影响的大小或数据的强度. 您拥有的数据越多,您检测非常小的影响的能力就越大。同样,如果数据很少,即使是中等影响也很难检测到。您的数据在个体之间的差异越大,您就越难发现重要性。因此,将精力集中在理解和估计预测变量的解释力上。

统计代写|应用统计代写applied statistics代考|HOW TO DECIDE BETWEEN COMPETING ANALYSES

我们将在本书后面通过实际的真实示例探讨如何在不同的可能方法中进行选择来分析您的数据。但是,提前考虑这个问题仍然很有用,因为它是一个重要的问题。很多时候,您将面临分析数据的不同可能方法,不同的方法可能有一定的优缺点。

归根结底,您的工作是选择最能代表和解释数据的统计分析。让你知道如何评估数据并做出决定是本书的主要目标之一。有时会有两种不同类型的模型,它们可能本质上是等效的。它们可能同样适合,并且它们可能会就预测变量的重要性给出非常相似的答案。有时,您可能必须在竞争模型中进行选择,这些模型会给您关于预测变量的假设意义(或不重要)的不同答案。您如何选择要搭配的型号?

您应该对“重要性”的概念完全保持中立,只考虑模型是否做得很好并且适合数据。正如我们将在第 7 章中看到的那样,有时您的模型可能看起来很合适,并且给您提供了非常重要的结果,但实际上它的拟合度很差。反之亦然,您选择的模型拟合不佳,并且对预测变量的重要性没有印象。归根结底,您需要能够向您自己、您的审阅者、您的主管、您的同事以及任何可能查看您的分析的其他人证明您选择模型的合理性。您需要能够支持您的决定并解释为什么您选择一种分析而不是另一种分析。如果你能做到这一点,你的身体就会很好。

统计代写|应用统计代写applied statistics代考|DATA ARE DATA ARE DATA

我们将在本书中使用的数据来自我作为博士后在史密森尼热带研究所的巴拿马热带雨林中进行的一项研究。自 2002 年开始攻读博士学位以来,我一直在研究青蛙及其卵和蝌蚪。我喜欢青蛙及其卵和蝌蚪。我认为他们是最伟大的。但是,也许你不知道,也许你在想:“这些奇怪的蝌蚪数据与我和我的分析有什么关系?我永远不会去巴拿马研究蝌蚪!” 虽然这可能是真的,但我认为数据就是数据就是数据,我们将研究的空间嵌套设计与许多其他研究系统极为相似(图 2.2)。

我们将使用的数据集的简写名称是“RxP”,代表“资源 X 捕食”,这是我们在研究中操作的两个主要内容(第 3 章将更详细地描述该研究)。在 RxP 数据集中,我们将个体嵌套(或分组,如果你愿意的话)在坦克中,而这些坦克本身也嵌套在块中。例如,这与将多只老鼠放在一个笼子里,然后将笼子放在不同的架子上是一样的。或者,您可能会想象每个小瓶测量多个果蝇,并且小瓶根据它们的设置日期分组在一起。同样,我们可以考虑拥有多个遗传品系(即基因型、家族等),将我们的数据聚集到离散的组中。

除了物理聚类数据的想法之外,我们可以将坦克视为实验的不同复制品,这与任何其他实验没有什么不同。在 RxP 实验期间测量的响应变量(也称为“因变量”)——比如变态前的天数或变态时的大小——与任何其他可能测量的连续变量没有什么不同,无论是老鼠看到闪光后冻结的时间或鸽子在正确打开杠杆之前的啄食次数或向生长中的植物添加肥料后产生的新叶子的数量。预测变量(也称为“自变量”)在本质上通常是分类的(即,它们有几个离散的类别),并且可以很容易地被药物治疗或母体富集环境或受精方案所取代。正如我之前所说,数据就是数据。见图2.2用于直观描述数据结构的相似性。

统计代写|应用统计代写applied statistics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

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

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