统计代写|数据可视化代写Data visualization代考|BISM3204

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|Statistical Concepts

Because the present book focuses on regression analysis, it’s helpful to be familiar with basic statistical concepts and terms. Don’t worry: each model explored in this book will be interpreted in detail, and we will revisit most concepts as we go along. These are some of the most important terms we will use throughout this book: p-values, effect sizes, confidence intervals, and standard errors. This section provides a brief (and applied) review of these concepts. Bear in mind that we will simply review the concepts for now. Later we will revisit them in $\mathrm{R}$, so don’t worry if you don’t know how to actually find $p$-values or confidence intervals: we will get to that very soon.

Before we proceed, it’s important to review two basic concepts, namely, samples and populations. Statistical inference is based on the assumption that we can estimate characteristics of populations by randomly ${ }^{1}$ sampling from them. When we collect data from 20 second language learners of English whose first language is Spanish (i.e., our sample), we wish to infer something about all learners who fit that description-that is, the entire population, to which we will never have access. At the end of chapter 2, we will see how to simulate some data and verify how representative a sample is of a given population.

Throughout this book, much like in everyday research, we will analyze samples of data and will infer population parameters from sample parameters. You may be familiar with the different symbols we use to represent these two sets of parameters, but if you’re not, here they are: we calculate the sample mean $(\bar{x})$ to infer the population mean $(\mu)$; we calculate the sample standard deviation $(s)$ to infer the population standard deviation $(\sigma)$. Finally, our sample size $(n)$ is contrasted with the population size $(N)$.

统计代写|数据可视化代写Data visualization代考|p-Values

The notion of a $p$-value is associated with the notion of a null hypothesis $\left(H_{0}\right)$, for example that there’s no difference between the means of two groups. $p$-values are everywhere, and we all know about the magical number: $0.05$. Simply put, $p$-values mean the probability of finding data at least as extreme as the data we haveassuming that the null hypothesis is true. ${ }^{2}$ Simply put, they measure the extent to which a statistical result can be attributed to chance. For example, if we compare the test scores of two groups of learners and we find a low (=significant) $p$-value $(p=0.04)$, we reject the null hypothesis that the mean scores between the groups are the same. We then conclude that these two groups indeed come from different populations, that is, their scores are statistically different because the probability of observing the difference in question if chance alone generated the data is too low given our arbitrarily set threshold of $5 \%$.

How many times have you heard or read that a $p$-value is the probability that the null hypothesis is true? This is perhaps one of the most common misinterpretations of $p$-values-the most common on the list of Greenland et al. (2016). This interpretation is incorrect because $p$-values assume that the null hypothesis is true: low $p$-values indicate that the data is not close to what the null hypothesis predicted they should be; high(er) $p$-values indicate that the data is more or less what we should expect, given what the null hypothesis predicts.

统计代写|数据可视化代写Data visualization代考|Effect Sizes

Effect sizes tell us how large the effect of a given variable is on the response variable. There are different ways to measure such effects. If you are familiar with $t$-tests and ANOVAs, you may remember that Cohen’s $d$ and $\eta^{2}$ are two ways to calculate effect sizes. In the models discussed in this book, effect sizes will be given as coefficients, or $\hat{\beta}$ values. The larger a $\hat{\beta}$ value is, the larger the effect size.

Here’s a simple example that shows how effect sizes should be the center of our attention most of the time. Suppose you are interested in recording your classes and making them available to your students. That will require a lot of time and work, but your hypothesis is that by doing so, you will positively affect your students’ learning. You then decide to test it, measuring their learning progress on the basis of their grades. You divide students into two groups. The control group (let’s call it $\mathcal{C}$ ) will not have access to recorded classes, whereas the treatment group $(\mathcal{T})$ will. Each group has 100 students (our sample size), who were randomly assigned to either $\mathcal{C}$ or $\mathcal{T}$ at the beginning of the term. Assuming that all important variables are controlled for, you then spend the entire term on your research project.

At the end of the term, you analyze the groups’ grades and find that the mean grade for group $\mathcal{C}$ was $83.05$ and the mean grade for group $\mathcal{T}$ was $85.11$. Let’s assume that both groups come from populations that have the same standard deviation $(s=3)$. You run a simple statistical test and find a significant result $(p$ $<0.0001)$. Should you conclude that recording all classes was worth it? If you only consider $p$-values, the answer is certainly yes-indeed, given that we are simulating the groups here, we can be sure that they do come from different populations. But take into consideration that the difference between the two groups was only $2.06$ points (over 100) -sure, this tiny difference could mean going from a B+ to an A depending on how generous you are with your grading policy, but let’s ignore that here. Clearly, the effect size here should at least make you question whether all the hours invested in recording all of your classes were worth it.

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。