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• 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
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## 统计代写|金融统计代写Financial Statistics代考|Three Other Frequency Tables

In this section, using the frequency table discussed in the Sect. 3.2, we move ahead to cumulative frequency tables, relative frequency tables, and relative cumulative frequency tables.

Example 3.4 Frequency Distributions for Statistics Exam Scores. Suppose that for the data listed in Table 3.3, the professor wants to know how many students receive a $\mathrm{C}$ or below, the proportion of students who receive a $\mathrm{B}$, and the proportion of students who receive a D or an F. To obtain this information, she calculates cumulative, relative, and cumulative relative frequencies.

By constructing cumulative frequencies, the professor determines the number of students who scored in a particular class or in one of the classes before it (Table 3.7). Obviously, the cumulative frequency for the first class is the frequency itself (3): there are no classes before it. The cumulative frequency for the second class is calculated by taking the frequency in the first class and adding it to the frequency in the second class (3) to arrive at a cumulative frequency of 6 . This means that 6 students were in the first two classes. Then 6 is added to the frequency of the third class (6) to derive a cumulative frequency of 12 . Thus, 12 students scored a $\mathrm{C}$ or a worse grade. The remaining cumulative frequencies are calculated in a similar manner. Note that the cumulative observation in the last class equals the total number of sample observations, because all frequencies have occurred in that class or in previous classes.

Another important concept is the relative frequency, which measures the proportion of observations in a particular class. It is calculated by dividing the frequency in that class by the total number of observations. For the data summarized in Table 3.7, the relative frequency for both the first and second classes is $0.15$, and the relative frequencies for the remaining three classes are $0.30,0.20$, and $0.20$, respectively, as shown in Table 3.8. The sum of the relative frequencies always equals 1 .

This table indicates that $15 \%$ of the class received an $\mathrm{F}, 15 \%$ a D, $30 \%$ a C, and so on. The professor can calculate the cumulative relative frequency for any class by adding the appropriate relative frequencies. Cumulative relative frequency measures the percentage of observations in a particular class and all previous classes. Thus, if she wants to determine what percentage of the students scored below a B, our conscientious professor can add the relative frequencies associated with grades $\mathrm{C}, \mathrm{D}$, and $\mathrm{F}$ to arrive at $60 \%$.

## 统计代写|金融统计代写Financial Statistics代考|Stem-and-Leaf Display

An alternative to histograms for the presentation of either nongrouped or grouped data is the stem-and-leaf display. Stem-and-leaf displays were originally developed by John Tukey of Princeton University. They are extremely useful in summarizing data sets of reasonable size (under 100 values as a general rule), and unlike histograms, they result in no loss of information. By this, we mean that it is possible to reconstruct the original data set in a stem-and-leaf display, which we cannot do when using a histogram.

For example, suppose a financial analyst is interested in the amount of money spent by food product companies on advertising. He or she samples 40 of these food product firms and calculates the amount that each spent last year on advertising as a percentage of its total revenue. The results are listed in Table 3.12.

Let’s use this set of data to construct a stem-and-leaf display. In Fig. 3.9, each observation is represented by a stem to the left of the vertical line and a leaf to the right of the vertical line. For example, the stems and leaves for the first three observations in Table $3.12$ can be defined as

In other words, stems are the integer portions of the observations, whereas leaves represent the decimal portions.
The procedure used to construct a stem-and-leaf display is as follows:

1. Decide how the stems and leaves will be defined.
2. List the stems in a column in ascending order.
3. Proceed through the data set, placing the leaf for each observation in the appropriate stem row. (You may want to place the leaves of each stem in increasing order.)

## 统计代写|金融统计代写Financial Statistics代考|Stem-and-Leaf Display

1. 决定如何定义茎和叶。
2. 按升序列出列中的词干。
3. 继续浏览数据集，将每个观察的叶子放在适当的茎行中。（您可能希望按递增顺序放置每个茎的叶子。）

## 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。