### 统计代写|应用统计代写applied statistics代考|Before You Begin

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|aka Thoughts on Proper Data Analysis

Before we embark on the journey that is learning $R$ and how to use it to analyze your data and make fantastic figures, it is useful to stop and think a little bit about best practices for data analysis.
$2.2$ BASIC PRINCIPLES OF EXPERIMENTAL DESIGN
If there are three words to remember when thinking about experimental design, they are balance, randomization, and replication. In a nutshell, what you are trying to prevent with these three factors is your data being correlated in some way that is unhelpful to your analysis. You are also trying to ensure that your data are independent from one another and that you have enough data to actually determine if your treatments did anything or not (see Boxes 2.1, 2.2, and 2.3).

Obviously, it is not always possible to control these aspects of your data, particularly if you have observational data (as compared to a controlled experiment which you design and run yourself). But, even in the case of observational studies, these principles are important to keep in mind and consider.

## 统计代写|应用统计代写applied statistics代考|BLOCKED EXPERIMENTAL DESIGNS

Consider the four experimental setups shown in Figure 2.1. Imagine that we are now testing the effects of four fertilizers on plant growth (labelled A, B, C, and D), each with 12 individuals. The experiment is conducted in four separate “blocks” What is a block? It could be many things. Maybe it is a physical way of setting up the experiment, for example, four shelves in an incubator that contain the experimental units or four rooms that contain the cages our individuals live in. Maybe, due to space or time limitations, only 12 individuals can be tested or measured at a time, and thus the experiment has to be run four separate times. Each of these can be considered a “block” so you can hopefully imagine how this idea relates to your own research. Blocks are only important to consider if there is some systematic difference among them.

In the first example, the four treatments will be perfectly correlated with the four blocks. Thus, if we imagine a significant difference is detected in one treatment, there is no way to know if it is because of the experimental treatment or if there was something else going on in that block (or room, or time point, or whatever you want to imagine that block represents). Once again, this is an example of pseudoreplication because it seems like we have a large sample size but in reality, we have a sample size of $\mathrm{N}=1$ in each of our treatments. Despite growing 48 different plants, this design is unreplicated.
The second example is a fully randomized design, where the four treatments are allocated across the four blocks completely at random. The third example is a fully balanced design, where each of the four treatments is assigned to each block in the same manner. Each of these setups has its own advantages and disadvantages.

The fully randomized design is good, and in theory should lead to the highest degree of replication, with all experimental units being truly independent. In reality it can actually work against the principle of balance, since some treatments might end up overrepresented in some blocks and underrepresented in others (e.g., in Figure 2.1, there are six individuals from treatment B in Block 2, but only one in Block 3). In the extreme, leaving your entire setup to random chance could lead to a horribly unbalanced and biased design, but this would be very rare. In general, fully randomizing your experiment is a very good idea!

## 统计代写|应用统计代写applied statistics代考|YOU CAN (AND SHOULD) PLAN YOUR ANALYSES BEFORE YOU HAVE THE DATA!

In addition to the aspects of experimental design described previously, the other most important thing to do is to have a clear idea of your predictor and response variables before you even start the experiment. Before you ever put a mouse in a testing box or a seed in growth chamber, you should identify what it is you are going to measure. Hopefully, if you know your study system pretty well or perhaps have some preliminary data, you can estimate what the data are going look like which will allow you to think about and plan for what type of analyses you will do. Maybe that sounds like wishful thinking, but this whole book is about the importance of knowing what your data look like, so don’t worry-you’ll get there!

Sir Ronald Fisher, one of the founders of modern statistics, offered one of the best statements about this issue in $1938 .$ Fisher’s point is that because your experimental design directly effects your data analysis, you should think about your analysis up front when planning the experiment.

2.2实验设计的基本原则

## 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。