### 统计代写|数据可视化代写Data visualization代考|BINF7003

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|Main Objectives of This Book

It goes without saying that the field of second language acquisition (SLA), or second language research more generally, relies on data: test scores, reaction times, grammaticality judgements, certainty levels, categorical preferences, suppliance rates, and so on. Indeed, by the end of the 1990 s, over $90 \%$ of all studies in applied linguistics were quantitative (Cunnings 2012). Different theories have been proposed to explain acquisition patterns that are observed in the data and to predict patterns in unobserved data. To evaluate the validity of any claim in the field, theoretical or not, we must consider empirical evidence, that is, data-much like any scientific field. It is therefore unsurprising that most of what we assume and propose in the field depends on how we analyze our data and, perhaps even more importantly, on how carefully we interpret and generalize the patterns that we find. More often than not, inappropriate analyses lead to incorrect conclusions.

It has been noted in the literature that the field of second language acquisition relies on a precariously narrow range of statistical techniques to quantitatively analyze data (Plonsky 2013, 2014, 2015). $\boldsymbol{t}$-tests and ANOVAs still seem to be the most popular statistical options, even though they are (i) underpowered and (ii) often inappropriate given the data at hand, as will be discussed in Part III. As examples of (i), t-tests can’t handle multiple variables (or groups), and ANOVAs can’t handle complex hierarchical structures in the data (chapter 9), which are essential given how much variation we observe in linguistic data. In addition, both methods focus on $p$-values, not on effect sizes. An example of (ii) would include the use of ANOVAs when we are dealing with binary or scalar responses (chapters 7 and 8 ) – see Jaeger (2008).

## 统计代写|数据可视化代写Data visualization代考|Why Focus on Data Visualization Techniques

Good figures are crucial. When we want to see and understand the data patterns underlying a study, a figure is likely the best option we have. Whether or not you consider yourself a visual person, the truth is that a well-designed plot almost always communicates your results more effectively than huge tables or a series of numbers and percentages within the body of the text. An appropriate figure can help the reader understand your thinking process, your narrative, and your statistical results-needless to say, you want your reader (or your reviewer) to understand exactly what you mean.

Besides helping the reader, figures help us, the researchers. To design the right figure, we must have a clear understanding of the message we want to

communicate, the pattern we want to focus on. Often times, it is by designing a figure that we realize that we don’t know exactly what we want to see-or that we don’t understand the type of data we are trying to visualize. In that way, data visualization can also improve our thinking process.

Despite the importance of data visualization, it is not uncommon to come across experimental papers in L2 research with very limited figures. How many times have you seen pie charts in papers? Or maybe bar plots without error bars? Perhaps a plot showing variables that do not match the variables discussed later, in the actual analysis? As we will see throughout this book, some general “rules of thumb” can already drastically improve one’s data visualization techniques.

## 统计代写|数据可视化代写Data visualization代考|Why Focus on Full-Fledged Statistical Models

Data can be messy, and language data almost always is-especially when we deal with L2 research, where multiple grammars can be at play, generating multiple patterns that often seem contradictory. Different leamers behave differently, and they may respond differently to different stimuli or circumstances. On top of all that, we know that multiple variables can play a role in different phenomena. We naturally want to focus on a particular variable of interest, but we shouldn’t ignore potential confounding factors that could also affect our results.

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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。

## 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。