统计代写|贝叶斯分析代写Bayesian Analysis代考|STAT4102

statistics-lab™ 为您的留学生涯保驾护航 在代写贝叶斯分析Bayesian Analysis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。我们的专家在代写贝叶斯分析Bayesian Analysis代写方面经验极为丰富，各种代写贝叶斯分析Bayesian Analysis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|贝叶斯分析代写Bayesian Analysis代考|Correlation Coefficient and p-Values

The correlation coefficient is a number between $-1$ and 1 that determines whether two paired sets of data (such as those for height and intelligence of a group of people) are related. The closer to 1 the more “confident” we are of a positive linear correlation and the closer to-1 the more confident we are of a negative linear correlation (which happens when, for example, one set of numbers tends to decrease when the other set increases as you might expect if you plotted a person’s age against the number of toys they possess). When the correlation coefficient is close to zero there is little evidence of any relationship.

Confidence in a relationship is formally determined not just by the correlation coefficient but also by the number of pairs in your data. If there are very few pairs then the coefficient needs to be very close to 1 or $-1$ for it to be deemed “statistically significant,” but if there are many pairs then a coefficient closer to 0 can still be considered “highly significant.”

The standard method that statisticians use to measure the “significance” of their empirical analyses is the $p$-value. Suppose we are trying to determine if the relationship between height and intelligence of people is significant and have data consisting of various pairs of values (height, intelligence) for a set of people; then we start with the “null hypothesis,” which, in this case is the statement “height and intelligence of people are unrelated.” The $p$-value is a number between 0 and 1 representing the probability that the data we have arisen if the null hypothesis were true. In medical trials the null hypothesis is typically of the form that “the use of drug X to treat disease $\mathrm{Y}$ is no better than not using the drug.”

The calculation of the $p$-value is based on a number of assumptions that are beyond the scope of this discussion, but people who need $p$-values can simply look them up in standard statistical tables (they are also computed automatically in Excel when you run Excel’s regression tool). The tables (or Excel) will tell you, for example, that if there are 100 pairs of data whose correlation coefficient is $0.254$, then the $p$-value is $0.01$. This means that there is a 1 in 100 chance that we would have seen these observations if the variables were unrelated.
A low $p$-value (such as $0.01$ ) is taken as evidence that the null hypothesis can be “rejected.” Statisticians say that a $p$-value of $0.01$ is “highly significant” or say that “the data is significant at the $0.01$ level.”

A competent researcher investigating a hypothesized relationship will set a $p$-value in advance of the empirical study. Typically, values of either $0.01$ or $0.05$ are used. If the data from the study results in a $p$-value of less than that specified in advance, the researchers will claim that their study is significant and it enables them to reject the null hypothesis and conclude that a relationship really exists.

统计代写|贝叶斯分析代写Bayesian Analysis代考|Spurious Correlations

Although the preceding examples illustrate the danger of reading too much into dubious correlations between variables, the relationships we saw there did not arise purely by chance. In each case some additional common factors helped explain the relationship.

But many studies, including unfortunately many taken seriously, result in claims of causal relationships that are almost certainly due to nothing other than pure chance.

Although nobody would seriously take measures to stop Americans drinking beer in order to reduce Japanese child mortality, barely a day goes by when some decision maker or another somewhere in the world takes just as irrational a decision based on correlations that turn out to be just as spurious.

For example, on the day we first happened to be drafting this section (16 March 2009) the media was buzzing with the story that working night shifts resulted in an increased risk of breast cancer. This followed a World Health Organization study and it triggered the Danish government to make compensation awards to breast cancer sufferers who had worked night shifts. It is impossible to state categorically whether this result really is an example of a purely spurious correlation. But it is actually very simple to demonstrate why and how you will inevitably find a completely spurious correlation in such a study-which you might then wrongly claim is a causal relationship-if you measure enough things.

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。