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

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。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

统计代写|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

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。

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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。

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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。