### 统计代写|linear regression代写线性回归代考|Our Doubts are Traitors and Make Us Lose the Good We Oft Might Win ${ }^{2}$

statistics-lab™ 为您的留学生涯保驾护航 在代写linear regression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linear regression代写方面经验极为丰富，各种代写linear regression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|linear regression代写线性回归代考|Introduction

Think about how often we’re exposed to data of some sort. Reports of studies in newspapers, magazines, and online provide data about people, animals, or even abstract entities such as cities, counties, or countries. Life expectancies, crime rates, pollution levels, the prevalence of diseases, unemployment rates, election results, and numerous other phenomena are presented with overwhelming frequency and in painful detail. Understanding statistics-or at least being able to talk intelligently about percentages, means, and margins of error-has become nearly compulsory for the well-informed person. Yet, few people understand enough about statistics to fully grasp not only the strengths but also the weaknesses of the way data are collected and analyzed. What does it mean to say that the life expectancy in the U.S. is $78.7$ years? Should we trust exit polls that claim that Wexton will win the election over Comstock by $5 \%$ (with a “margin of error” of $\pm 2 \%$ ? When someone claims that “taking calcium supplements is not associated with a significantly lower risk of bone fractures in elderly women,” what are they actually saying? These questions, as well as many others, are common in today’s world of statistical analysis and numeracy.

For the budding social or behavioral scientist, whether sociologist, psychologist, geographer, political scientist, or economist, avoiding quantitative analyses that move beyond simple statistics such as percentages, means, standard deviations, and $t$-tests is almost impossible. A large proportion of studies found in professional journals employ statistical models that are designed to predict or explain the occurrence of one variable with information about other variables. The most common type of prediction tool is a regression model. Many books and articles describe, for example, how to conduct a linear regression analysis (LRA) or estimate an LRM, ${ }^{1}$ which, as noted in the Preface, is designed to account for or predict the values of a single outcome variable with information from one or more explanatory variables. Students are usually introduced to this model in a second course on applied statistics, and it is the main focus of this book. Before beginning a detailed description of LRMs, though, let’s address some general issues that all researchers and consumers of statistics should bear in mind.

## 统计代写|linear regression代写线性回归代考|Our Doubts are Traitors and Make Us Lose the Good We Oft Might Win ${ }^{2}$

A critical issue I hope readers will ponder as they study the material in the following chapters involves perceptions of quantitative research. Statistics has, for better or worse, been maligned by a variety of observers in recent years. For one thing, the so-called “replication crisis” has brought to light the problem that the results of many studies in the social and behavioral sciences cannot be confirmed by subsequent studies. ${ }^{3}$ Books with titles such as How to Lie with Statistics are also popular ${ }^{4}$ and can lend an air of disbelief to many studies that use statistical models. Researchers and statistics educators are often to blame for this disbelief. We frequently fail to impart some important caveats to students and consumers, including:

1. A single study is never the end of the story; multiple studies are needed before we can (or should) reach defensible conclusions about social and behavioral phenomena.
2. Consumers and researchers need to embrace a healthy dose of skepticism when considering the results of research studies. ${ }^{5}$ They should ask questions about how data were collected, how variables were measured, and whether the appropriate statistical methods were used. We should also realize that random or sampling “error” (see Chapter 2 ) affects the results of even the best designed studies.
3. People should be encouraged to use their common sense and reasoning skills when assessing data and the results of analyses. Although it’s important to minimize confirmation bias and similar cognitive tendencies that (mis)shape how we process and interpret information, we should still consider whether research findings are based on sound premises and follow a logical pattern given what we already know about a phenomenon.

## 统计代写|linear regression代写线性回归代考|Best Statistical Practices ${ }^{6}$

In the spirit of these three admonitions, it is wise to heed the following advice regarding data analysis in general and regression analysis in particular.

1. Plot your data-early and often.
2. Understand that your dataset is only one of many possible sets of data that could have been observed.
3. Understand the context of your dataset-what is the background science and how were measurements taken (for example, survey questions or direct measures)? What are the limitations of the measurement tools used to collect the data? Are some data missing? Why?
4. Be thoughtful in choosing summary statistics.
5. Decide early which parts of your analysis are exploratory and which parts are confirmatory, and preregister ${ }^{7}$ your hypotheses, if not formally then at least in your own mind.
6. If you use $p$-values, ${ }^{8}$ which can provide some evidence regarding statistical results, follow these principles:
a. Report effect sizes and confidence intervals (CIs);
b. Consider providing graphical evidence of predicted values or effect sizes to display for your audience the magnitude of differences furnished by the analysis;
c. Report the number of tests you conduct (formal and informal);
d. Interpret the $p$-value in light of your sample size (and power);
e. Don’t use $p$-values to claim that the null hypothesis of no difference is true; and

f. Consider the $p$-value as, at best, only one source of evidence regarding your conclusion rather than the conclusion itself.

2. Use simulations to understand the performance of your statistical plan on datasets like yours and to test various assumptions.
3. Read results with skepticism, remembering that patterns can easily occur by chance (especially with small samples), and that unexpected results based on small sample sizes are often wrong.
4. Interpret statistical results or patterns in data as being consistent or inconsistent with a conceptual model or hypothesis instead of claiming that they reveal or prove some phenomenon or relationship (see Chapter 2 for an elaboration of this recommendation).

The material presented in the following chapters is not completely faithful to these practices. For example, we don’t cover how variables are measured, hypothesis generation, or simulations (but see Appendix B), and we are at times too willing to trust $p$-values (see Chapter 2). These practices should, nonetheless, be at the forefront of all researchers’ minds as they consider how to plan, execute, and report their own research.

I hope readers of subsequent chapters will be comfortable thinking about the results of quantitative studies as they consider this material and as they embark on their own studies. In fact, I never wish to underemphasize the importance of careful reasoning among those assessing and using statistical techniques. Nor should we suspend our common sense and knowledge of the research literature simply because a set of numbers supports some unusual conclusion. This is not to say that statistical analysis is not valuable or that the results are generally misleading. Numerous findings from research studies that did not comport with accepted knowledge have been shown valid in subsequent studies. Statistical analyses have also led to many noteworthy discoveries in social, behavioral, and health sciences, as well as informed policy in a productive way. The point I wish to impart is that we need a combination of tools-including statistical methods, a clear comprehension of previous research, and our own ideas and reasoning abilities-to help us understand social and behavioral issues.

## 统计代写|linear regression代写线性回归代考|Our Doubts are Traitors and Make Us Lose the Good We Oft Might Win 2

1. 一项研究永远不会结束。在我们能够（或应该）就社会和行为现象得出合理的结论之前，需要进行多项研究。
2. 在考虑研究结果时，消费者和研究人员需要接受健康的怀疑态度。5他们应该询问有关如何收集数据、如何测量变量以及是否使用了适当的统计方法的问题。我们还应该认识到，即使是设计最好的研究，随机或抽样“误差”（见第 2 章）也会影响结果。
3. 应该鼓励人们在评估数据和分析结果时使用他们的常识和推理能力。尽管最大限度地减少确认偏差和类似的认知倾向（错误）影响我们处理和解释信息的方式很重要，但我们仍然应该考虑研究结果是否基于合理的前提，并遵循我们已经了解的现象的逻辑模式。

## 统计代写|linear regression代写线性回归代考|Best Statistical Practices 6

1. 尽早且经常地绘制数据。
2. 了解您的数据集只是可以观察到的许多可能的数据集之一。
3. 了解您的数据集的背景——什么是背景科学以及如何进行测量（例如，调查问题或直接测量）？用于收集数据的测量工具有哪些限制？是否缺少某些数据？为什么？
4. 在选择汇总统计数据时要深思熟虑。
5. 尽早确定分析的哪些部分是探索性的，哪些部分是确认性的，并预先注册7您的假设，如果不是正式的，那么至少在您自己的脑海中。
6. 如果你使用p-价值观，8可以提供一些关于统计结果的证据，遵循以下原则
：报告效应量和置信区间 (CI)；
湾。考虑提供预测值或效应大小的图形证据，以向您的听众展示分析提供的差异幅度；
C。报告您进行的测试数量（正式和非正式）；
d。解释p-根据您的样本量（和功率）的值；
e. 不要使用p- 声称没有差异的原假设为真的值；和

F。考虑p- 充其量仅将价值视为关于您的结论的一种证据来源，而不是结论本身。

1. 考虑创建定制的、基于模拟的统计测试，以使用您的特定数据集回答您的特定问题。
2. 使用模拟来了解您的统计计划在像您这样的数据集上的性能，并测试各种假设。
3. 以怀疑的态度阅读结果，记住模式很容易偶然出现（尤其是对于小样本），并且基于小样本量的意外结果通常是错误的。
4. 将数据中的统计结果或模式解释为与概念模型或假设一致或不一致，而不是声称它们揭示或证明了某些现象或关系（有关该建议的详细说明，请参见第 2 章）。

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。