### 统计代写|数据可视化代写Data visualization代考|DECO3100

statistics-lab™ 为您的留学生涯保驾护航 在代写数据可视化Data visualization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。我们的专家在代写数据可视化Data visualization代写方面经验极为丰富，各种代写数据可视化Data visualization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|数据可视化代写Data visualization代考|Figures should be Aesthetically Appropriate

It’s surprisingly common to see figures that are pixelated, whose labels are too small (or too large) relative to the text, or whose font family is inconsistent with the text. Another common issue (especially in presentations) is the choice of colors: if you have a dark background in your figure, the choice of colors must take that into account, otherwise it will be much harder for the audience to read your slides-especially considering that projectors often don’t have great contrast and resolution.

Your figures should look crisp even when you zoom in, and they should not distract the reader from what really matters (again: less is more). You should have clear labels along the axes (when needed) and an informative caption, and your use of color should serve a purpose-remember that using colors can actually be a problem in some journals. In reality, it’s usually possible to remove the colors from a figure and use some other dimension to convey the same information-we will exercise that throughout the book.

Fig. 2.4, like the figure we discussed earlier in this chapter (Fig. 2.3), shows a bar plot. Here, however, we have three dimensions: the $y$-axis shows the change in score (e.g., pre- and post-test) of the hypothetical study mentioned earlier; the $x$-axis shows the different native languages of the participants in said study; and the fill of the bars discriminates the two methods under examination. Note that the font sizes are appropriate (not too small, not too large). In addition, the key is positioned at the top of the figure, not at the side. This allows us to better use the space (i.e., increase the horizontal size of the figure itself). No colors are needed here, since the fill of the bars represents a factor with only two levels (“In-person” and “Online”). Finally, the bars represent the mean improvement, but the plot also shows standard errors from the mean. If the plot only showed means, it would be considerably less informative, as we would know nothing about how certain we are about the means in question (i.e., we would not incorporate information about the variance in the data).

## 统计代写|数据可视化代写Data visualization代考|Basic Statistics in R

So far in this chapter we have covered a lot. We installed $\mathrm{R}$ and RStudio, discussed RStudio’s interface, and explored some $R$ basics with a number of code blocks, which led us to ggplot2 and figures in the previous section. Once we have a figure, the natural next step is to statistically analyze the patterns shown in said figure. Thus, in this section of the chapter, we will turn to some basic statistical concepts-this will be a good opportunity to review some concepts that will be used throughout the book. We’ll first start with one of the most basic concepts, namely, sampling.

Assume we want to collect data on proficiency scores from 20 learners. To simulate an entire population of learners containing, say, 1 million data points, we can use the function rnorm(), which randomly generates numbers following a normal (Gaussian) distribution ${ }^{25}$ (there’s no reason to assume that proficiency scores are not normally distributed here). Create a variable called pop and assign to it $\operatorname{rnorm}(\mathrm{n}=1000000$, mean $=85, \mathrm{sd}=8)$-you can even do this in the console, without creating a new script, since it will be a simple exercise. This will generate one million scores normally distributed around a mean $(\mu)$ of 85 points with a standard deviation $(\sigma)$ of 8 points. In reality, we never know what the mean and standard deviations are for our population, but here we do since we are the ones simulating the data. You can check that both $\mu$ and $\sigma$ are roughly 85 and 8 , respectively, by running mean(pop) and sd(pop) you won’t get exact numbers, but they should be very close to the parameter values we set using rnorm().

Next, let’s sample 20 values from pop-this is equivalent to collecting data from 20 learners from a population of one million learners. To do that, create a new variable, sam, and assign to it sample $(x=$ pop, size $=20)$. Now, run mean( ) and sd() on sam, and you should get a sample mean $(\bar{x})$ and a sample standard deviation $(s)$ that should be very similar to the true population parameters $(\mu, \sigma)$ we defined earlier. This is a quick and easy way to see sampling at work: we define the population ourselves, so it’s straightforward to check how representative our sample is.

## 统计代写|数据可视化代写Data visualization代考|What’s Your Research Question

If you already have actual data, that means you have long passed the stages of formulating a research question and working on your study design. Having a relevant research question is as important as it is difficult-see Mackey and Gass (2016, ch. 1). Great studies are in part the result of great research questions. Your question will dictate how you will design your study, which in turn will dictate how you will analyze your data. Understanding all three components is essential. After all, you may have an interesting question but fail to have an adequate study design, or you may have both a relevant question and an appropriate study design but realize that you don’t know how to analyze the data.

The hypothetical dataset in question (long) consists of two groups of participants (control and target) as well as three sets of test scores-we will examine more complex and realistic datasets later on, but for now our tibble long will be sufficient. Suppose that our study examines the effects of a particular pedagogical approach on students’ scores, as briefly mentioned earlier. For illustrative purposes, let’s assume that we want to compare a student-centered approach (e.g., workshop-based), our targets (treatment group), to a teacher-centered approach (e.g., lecture-based), our controls, so we are comparing two clearly defined approaches (two groups). Next, we must be able to measure scores. To do that, we have decided to administer three tests (e.g., a pre-test (testA), a post-test (testB), and a delayed post-test (testC), as alluded to earlier). With that in mind, we can ask specific questions: is the improvement from test $A$ to testB statistically different between controls and targets? How about from testA to testC? These questions must speak directly to your overarching research question. Finally, in addition to a research question, you may have a directional hypothesis, for example, that a lecture-based approach will lead to lower scores overall, or a non-directional hypothesis, for example, that there will be some difference in learning between the two approaches, but which one will be better is unknown. Let’s now go over some basic stats assuming the hypothetical context in question.

## 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。