### 统计代写|应用统计代写applied statistics代考|Basic Statistical Analyses using R

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|Basic Statistical Analyses using R

The purpose of this chapter is to introduce you to some basic statistical analyses using $R$. This chapter assumes you are using the RxP.clean dataset that was created in the Chapter 3 .
We will cover the following topics:

1. Assessing data normality
2. Some basic non-parametric statistics
3. Student’s t-test
4. One-way Analysis of Variance (ANOVA)
5. We have now seen how to use a function like qplot() to look at your data in various ways. For example, you can plot a histogram of your response variable and see how it is distributed. However, as you move beyond the initial steps of data exploration and start to think about data analysis, there are several questions you should ask yourself. The most important of which is just what kind of data do you have?

This might seem like a simple question at first, but it is paramount for determining the analyses you will conduct. When we talk about data, are we talking about your response or your predictor variables? The answer is both of course. Knowing the shape of your response and predictor variables will determine what sort of analysis you do. In addition to simply knowing if the data are normal or not, you should be mindful of if you have one predictor or multiple predictors, and if your predictors are continuous (a bunch of numbers) or discrete (different categories). For this chapter and the next, we will just concern ourselves with analyzing data where the response variable is normally distributed.

## 统计代写|应用统计代写applied statistics代考|AVOIDING PSEUDOREPLICATION

Although we have data on nearly 2500 individual metamorphs, those data are not all independent from one another. This is because groups of tadpoles were raised in common environments (the mesocosms, aka tanks) and variation between tanks may make certain individuals more similar than others. If we treat all individuals as independent, we are committing pseudoreplication, which is when you artificially inflate your sample size of independent observations. This was first mentioned in Chapter 2 , but for more details, see the classic article Hurlbert, S.H. 1984. “Pseudoreplication and the design of ecological field experiments.” Ecological Monographs $54(2): 187-211$. It’s a little long, but it’s a great read!

One easy way to avoid pseudoreplication is to utilize the mean value for each tank instead of that of individuals. We can summarize our entire dataset with relative ease using the summarize() function that was introduced in Chapter 3. We will have to get rid of column 1 on Individual ID and column 3 which shows the tank within each block because those would not be meaningful to average at the tank level. Recall that in order to use summarize() we first: 1) define our dataset, then 2) define the different variables we want to group our data by with the group_by() function, and lastly 3) use summarize() to create the variables that are the mean values from the raw dataset. At each of the three steps you pipe your data from one line to the next. You could also use the aggregate() function by defining the 7 columns to summarize (our response variables) and binding them together in a single object using the function cbind(), which binds two or more columns together, then give it the various predictor variables we want to use to summarize the data. Here, we will use summarize(), which just makes more sense. Recall that these functions are found in the dplyr package.

One other thing to notice in the following code is that there are more variables than necessary in the group_by() function. All we need to uniquely identify each tank is the variable “Tank.Unique” or the three treatments in combination. Including all of them, as well as the “Block” variable, we will merely carry more columns through to our new summarized dataset. Lastly, notice that we are storing the output of this set of code as a new object called “RxP.byTank.”

## 统计代写|应用统计代写applied statistics代考|Looking at the data

The first and easiest thing to do if you want to see if your data are normal or not is to plot the histogram of values. You could use either the hist() function or the $q$ plot() function found in the ggplot2 package. Either way, a histogram should give you a general sense of what your data look like. Remember that data which are normally distributed will have a relatively even spread of values above and below the mean, like that shown in Figure 5.1. Let’s look at the variables “Age.FromEmergence” and “SVL.final” and plot them using qplot() from the ggplot2 package. Remember, you have to load the package first using the library() function. Notice in the following code that we can use the “bins=” argument to specify how fine we want the histogram to break up the data. Also notice I’ve loaded the package cowplot which contains the function plot_grid() which allows us to plot multiple different figures together in a single window.

Looking at these two histograms (Figure $5.2$ ) is quite informative. The SVL data appear almost normal, although the data have a slight tail to the right. The Age data are, however, very skewed; most individuals emerged very early in the period of metamorphosis, but the tail is very long with individuals still emerging from tanks more than 100 days after the first metamorphs. Many biological patterns can be made normal with logtransformation, so a good first step is to see what effect that has on our data. Data that can be made normal upon log-transformation are referred to as “lognormal.” As we explored in Chapter 3, plotting data as a density plot can also be useful, and so both will now be shown in Figure 5.3.

## 统计代写|应用统计代写applied statistics代考|Basic Statistical Analyses using R

1. 评估数据正态性
2. 一些基本的非参数统计
3. 学生 t 检验
4. 单因素方差分析 (ANOVA)
5. 我们现在已经了解了如何使用 qplot() 之类的函数以各种方式查看数据。例如，您可以绘制响应变量的直方图并查看其分布情况。但是，当您超越数据探索的初始步骤并开始考虑数据分析时，您应该问自己几个问题。其中最重要的是您拥有什么样的数据？

## 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。