## 统计代写|统计计算代写Statistical calculation代考|Measures of shape

statistics-lab™ 为您的留学生涯保驾护航 在代写统计计算Statistical calculation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。我们的专家在代写统计计算Statistical calculation代写方面经验极为丰富，各种代写统计计算Statistical calculation相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|统计计算代写Statistical calculation代考|Skewness (SK)

Skewness relates to the symmetry or lack thereof in the shape of the histogram, polygon, stem-and-leaf or dot plot that you can draw from the data. The shape influences the locations of the mean, median and mode in the data set, for example, whether the mean is larger or smaller than the median.

In symmetrical or normal distributions the left half is a mirror image of the right.

When a symmetrical distribution has a single mode, the mode will be in the centre of the distribution. Furthermore, the mean and the median will be equal to the mode. There are no outliers on the one side to pull the mean away from the bulk of the data. The skewness coefficient will have a zero value.

To portray the shape of a distribution you can make use of the histogram or a smooth polygon.

A distribution is skewed if the curve appears skewed either to the left or to the right, meaning that the one tail extends more to one side than the other. The mode stays at the peak of the distribution because outliers do not influence the mode at all. The influence of outliers is highest on the arithmetic mean because the mean is affected by all values in the data set, including the extreme ones, and tends to be located toward the tail of the skewed distribution. The median, being dependent on the number of values in the data set rather than on the size of those values, is less sensitive than the mean, since only the middle measurements are used for its calculation. It is located somewhere between the mode and the mean. Positive skewness (or skewed to the right) occurs when the majority of the data values are concentrated on the left. There are a few data values that are substantially larger than others and these larger values cause the mean to increase while having little, if any, effect on the median. The mean will exceed the median, and both the mean and the median will be greater than the mode. The tail to the right will be longer than to the left.

## 统计代写|统计计算代写Statistical calculation代考|Interpreting centre and variability

1. Dispersion is the amount of spread or scatter that occurs in a data set. It can be interpreted as the size of a ‘typical’ deviation from the mean. If the values in the data set are clustered tightly about their mean, the standard deviation is small, but if the values are widely dispersed about their mean, the standard deviation is large.
2. In comparing two data sets with the same unit of measure, the one with the larger standard deviation has the greater amount of variability and the one with the smaller standard deviation is more consistent, with less variability among the numbers in the data set.
3. If you have a single data set, the mean can be combined with the standard deviation to obtain information about how values in a data set are distributed along a number line. To do this we describe how far away a particular observation is from the mean in terms of the standard deviation. For example, we might say that an observation is two standard deviations above the mean or one standard deviation below the mean. The number of standard deviations is known as the $z$-score or the Standardised value.
$$z=\frac{x-\bar{x}}{s}$$
Consider a data set with a mean of 100 and a standard deviation of 15 .
• The mean minus one standard deviation $=100-15=85$. This means that 85 is one standard deviation below the mean. The $z$-score $=\frac{85-100}{15}=-1$. A $z$-score is negative if the data value is less than the mean.
• The $z$-score for a value of $115=\frac{115-100}{15}=1$. This means that 115 is one standard deviation above the mean. A $z$-score is positive if a data value is greater than the mean.
• All observations that fall between 85 and 115 are within one standard deviation from the mean.
• Two standard deviations $=2 \times 15=30,100-30=70$ and $100+30=$ 130. All observations that fall between 70 and 130 are within two standard deviations from the mean.
• $100+3(15)=145$. Observations above 145 exceed the mean by more than three standard deviations.
1. The following two rules can be applied, depending on the shape of the distribution.
• If the distribution is symmetrical, you can make a statement about the proportion of data values that fall within a specified number of standard deviations of the mean by making use of the empirical rule.
• A more general interpretation of the proportion of data values that fall within a specified number of standard deviations of the mean is derived from Chebysheff ‘s theorem, which applies to distributions of all shapes.

# 统计计算代考

## 统计代写|统计计算代写Statistical calculation代考|Interpreting centre and variability

1. 分散度是数据集中发生的传播或分散的量。它可以解释为与平均值的“典型”偏差的大小。如果数据 集中的值围绕其均值紧密聚集，则标准差很小，但如果这些值广泛分布在其均值附近，则标准差很 大。
2. 在比较具有相同度量单位的两个数据集时，标准偏差较大的数据集具有较大的变异性，标准偏差较 小的数据集更一致，数据集中的数字之间的变异性较小。
3. 如果您只有一个数据集，则可以将均值与标准差组合以获得有关数据集中的值如何沿数字轴分布的 信息。为此，我们根据标准差描述特定观察值与平均值的距离。例如，我们可以说观察值是高于均 值两个标准差或低于均值一个标准差。标准差的数量被称为 $z$-分数或标准化值。
$$z=\frac{x-\bar{x}}{s}$$
考虑一个均值为 100 且标准差为 15 的数据集。
-平均值减去一个标准偏差 $=100-15=85$. 这意味着 85 比平均值低一个标准差。这 $z$-分数 $=\frac{85-100}{15}=-1 . A z$ – 如果数据值小于均值，则得分为负。
• 这 $z$ – 值的分数 $115=\frac{115-100}{15}=1$. 这意味着 115 比平均值高一个标准差。 $A z$ – 如果数据值大于平 均值，则得分为正。
• 落在 85 到 115 之间的所有观测值都在与平均值的一个标准差范围内。
• 两个标准偏差 $=2 \times 15=30,100-30=70$ 和 $100+30=130$. 所有落在 70 和 130 之间的 观测值都在平均值的两个标准差范围内。
• $100+3(15)=145$. 高于 145 的观测值超过平均值三个标准差以上。
1. 根据分布的形状，可以应用以下两个规则。
• 如果分布是对称的，您可以使用经验法则来说明落在均值的指定标准差数内的数据值的比例。
• 对落在指定均值标准差数内的数据值比例的更一般解释源自切比雪夫定理，该定理适用于所有形状的分布。

## 有限元方法代写

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。

## 统计代写|统计计算代写Statistical calculation代考|Summarising data using numerical descriptors

statistics-lab™ 为您的留学生涯保驾护航 在代写统计计算Statistical calculation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。我们的专家在代写统计计算Statistical calculation代写方面经验极为丰富，各种代写统计计算Statistical calculation相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|统计计算代写Statistical calculation代考|Choose between the mean, median or mode

An average should convey an impression of a distribution in a single value. It is therefore important to use the right type of average. The different averages have different uses. The factors that play a role in choosing the right average are the following:

1. Is the nature of the data numerical or non-numerical?
• The mode, which is the value that occurs most often, is the only measure of central tendency useful for nominal scale data (qualitative data that you cannot rank in any way). You can also use the mode for all other qualitative or quantitative (numerical) data sets.
• If you can rank qualitative data sets (ordinal scale), you can use the median. The median is also valid for all quantitative data sets.
• The arithmetic mean can be calculated only for quantitative data sets.
1. What does each average tells us?
Depending on the situation and the problem under investigation, one measure may be superior to another, and in some other cases you can use all three in conjunction.
• The mode identifies the most common or ‘typical’ value, or the value that occurs more often than the others do. It may be a good choice if one value occurs much more often than others do. At the same time, the mode conveys the least amount of information about the data set as a whole. In some samples the mode may be in the middle of the distribution, but in others it may be a value at one end of the distribution. It is also possible to have more than one mode, which will eliminate the mode as an option. Outliers do not influence the mode at all and the mode stays at the peak of the distribution.
• The median indicates the centre of the distribution. The same number of observations lie above and below the median. Outliers occur at the beginning or end of a distribution; this means that it is unlikely that outliers will affect the median very much.
• The mean is the most frequently used average because it includes all the values in the data set. This feature makes it the most sensitive to extreme values.
• What is the shape of the distribution?
• In a symmetrical distribution, the mean, median and mode will be the same or very close together. Whichever one you choose will give you the same answer.
• If there are extreme values present on one side of the data set, the distribution is skewed. If the mean is very different from the median, the median will be a better option to use. Skewness will be discussed later in the unit.

## 统计代写|统计计算代写Statistical calculation代考|The range

The range is the difference between the largest and smallest values in a data set. Although it measures the distance across the entire set of data, its usefulness as a measure of dispersion is limited. It does not tell us how much the other values in the data set vary from one another or from the mean. The largest or smallest value (or both) may also be an outlier, which can cause a distorted picture of the data.
range $=$ largest value – smallest value
A midrange can be calculated by dividing the range by 2 .
For grouped data the range is the difference between the upper boundary of the last interval and the lower boundary of the first interval.

A bakery regularly orders punnets of blueberries for its famous blueberry cheesecake. The average weight of the punnets is supposed to be $600 \mathrm{~g}$. The baker uses one punnet of blueberries in each cake. It is important that the punnets are of consistent weight so that the cake turns out right. Random samples of punnets from two suppliers were weighed. The weights in grams of the punnets were:
Supplier 1:480 $\quad 600 \quad 600 \quad 600 \quad 760$
Supplier 2: $480 \quad 540 \quad 570 \quad 760 \quad 760$
Calculate the range of punnet weights for each supplier and comment on your results.
Supplier 1: range $=760-480=280 \mathrm{~g}$
Supplier 2: range $=760-480=280 \mathrm{~g}$
The ranges are the same, but it is obvious that the variations within the samples are different. So the range will not solve the bakery’s problem if they want to choose a supplier that will provide punnets with consistent weights.

# 统计计算代考

## 统计代写|统计计算代写Statistical calculation代考|Choose between the mean, median or mode

1. 数据的性质是数值的还是非数值的？
• 众数是最常出现的值，是对标称尺度数据（无法以任何方式排序的定性数据）有用的集中趋势的唯一度量。您还可以将该模式用于所有其他定性或定量（数字）数据集。
• 如果您可以对定性数据集（有序量表）进行排名，则可以使用中位数。中位数也适用于所有定量数据集。
• 只能对定量数据集计算算术平均值。
1. 每个平均值告诉我们什么？
根据具体情况和所调查的问题，一种措施可能优于另一种措施，在其他一些情况下，您可以结合使用所有三种措施。
• 该模式标识最常见或“典型”值，或者比其他值更频繁出现的值。如果一个值比其他值出现的次数多得多，这可能是一个不错的选择。同时，该模式传达了关于整个数据集的最少信息量。在某些样本中，模式可能位于分布的中间，但在其他样本中，它可能是分布一端的值。也可以有不止一种模式，这将消除模式作为一个选项。离群值根本不影响模式，模式停留在分布的峰值。
• 中位数表示分布的中心。相同数量的观察值位于中值之上和之下。异常值出现在分布的开始或结束时；这意味着异常值不太可能对中位数产生太大影响。
• 平均值是最常用的平均值，因为它包括数据集中的所有值。此功能使其对极值最敏感。
• 分布的形状是什么？
• 在对称分布中，均值、中位数和众数将相同或非常接近。无论你选择哪一个都会给你相同的答案。
• 如果数据集的一侧存在极值，则分布是偏斜的。如果均值与中位数相差很大，则中位数将是更好的选择。偏度将在本单元后面讨论。

## 有限元方法代写

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。

## MATH110 Statistical calculation课程简介

This course introduces the logic and methods of statistics. We begin with a discussion of the role of statistics, introducing the concepts of internal and external validity. Common methods for describing the characteristics of individuals and educational outcomes are presented, including the use of graphs and summary measures such as the mean, median, standard deviation, and correlation coefficient. Because of the large natural variability among individuals, one must be able to determine whether or not an apparent difference or patterns present in the data seems to be merely a chance occurrence.
Probability concepts are introduced to help us in this effort. Probability then forms the basis of all of the inferential statistical procedures subsequently presented. At the end of the course, students will:

• identify basic statistical applications for educational research.
• explain how to implement quantitative approaches to educational research.
• identify types of statistical methods and strategies, and select data collection and analysis approaches for different research interests.
• integrate fundamental statistical theories and concepts with functions of SPSS programs in the context of an analysis project’s overall design.

## PREREQUISITES

Students have 3 to 9 months to complete 18 lessons, including a final research project. Each lesson contains a variety of items which include required chapter reading, a quiz on the reading assignment, at least one instructor video and supporting material for the lesson, and a lesson activity assignment. Lesson topics include:

1. Introduction to statistics
2. Frequency Distributions
3. Central Tendency
4. Variability
5. Z scores: Standardized distributions
6. Probability
7. Probability Sampling
8. Hypothesis Testing
9. $t$ Statistic
10. Independent Sample $t$ Test
11. Related Samples $t$ Test
12. Intro to Analysis of Variance
13. Repeated Measure Analysis of Variance
14. Two-factor Analysis of Variance
15. Correlations
16. Regression
17. Chi-square
18. Research Project

## MATH110 Statistical calculation HELP（EXAM HELP， ONLINE TUTOR）

For a Bose gas with fixed number of particles and at a given temperature, calculate the critical volume at which the Bose-Einstein condensation takes place. Repeat your analysis in $2 D$.

In three-dimensional space, the critical volume $\$ V_{-} c \$$for Bose-Einstein condensation (BEC) can be calculated using the formula:$$
V_c=\frac{N}{\zeta(3 / 2)}\left(\frac{h^2}{2 \pi m k_B T}\right)^{3 / 2}
$$where \ N \$$ is the number of particles, $\$ \backslash z e t a(3 / 2) \backslash$approx$2.612 \$$is the Riemann zeta function, \ \mathrm{~h} is Planck’s constant, \ \mathrm{~m} \$$ is the mass of the bosons, $\$ k_{-} B \$$is the Boltzmann constant, and \ T \$$ is the temperature.

In two-dimensional space, the critical area $\$ A_{-} c \$$can be calculated using a similar formula:$$
A_c=\frac{N}{\zeta(2)}\left(\frac{h^2}{2 \pi m k_B T}\right)
$$where \ \backslash z e t a(2)=\backslash f r a c{p i \wedge 2}{6} \$$.
Note that the critical volume/area is the minimum volume/area required for a Bose gas to undergo $\mathrm{BEC}$ at a given temperature.
Let’s assume a Bose gas with $\$ \mathrm{~N}=10^{\wedge} 6 \$$particles, \ \mathrm{~m}= 1.44 \backslash times 10^{\wedge}{-25} \ \mathrm{~kg} (mass of helium-4 atom), and \ T= 2.17 \ \mathrm{~K} (critical temperature for helium-4). We can then calculate the critical volume/area as follows: For a 3D Bose gas:$$
V_c=\frac{10^6}{2.612}\left(\frac{\left(6.626 \times 10^{-34}\right)^2}{2 \pi\left(1.44 \times 10^{-25}\right)\left(1.38 \times 10^{-23}\right)(2.17)}\right)^{3 / 2} \approx 2.16 \times 10^{-5} \mathrm{~m}^3
$$For a 2D Bose gas:$$
A_c=\frac{10^6}{\pi^2 / 6}\left(\frac{\left(6.626 \times 10^{-34}\right)^2}{2 \pi\left(1.44 \times 10^{-25}\right)\left(1.38 \times 10^{-23}\right)(2.17)}\right) \approx 1.22 \times 10^{-8} \mathrm{~m}^2
$$Therefore, for a Bose gas with \ N=10^{\wedge} 6 \$$ particles and $\$ T=2.17 \$\mathrm{~K}$, the critical volume for $\mathrm{BEC}$ is approximately $\$ 2.16\backslash$times$10^{\wedge}{-5} \$m \$ \wedge 3 \$$in 3 \mathrm{D} and the critical area for BEC is approximately \ 1.22 \backslash times 10^{\wedge}{-8} \ \mathrm{~m} \^{\wedge} 2 \$$ in $2 \mathrm{D}$.

For a dispersion relation $\varepsilon \propto|p|^\sigma$, what is the constraint on dimensionality of space $D$ for Bose-Einstein condensation to take place?

The condition for Bose-Einstein condensation (BEC) to occur is that the chemical potential $\$ \backslash m u \$$becomes equal to the ground-state energy of the system. In the low-temperature limit, this ground-state energy is proportional to \ N^{\wedge}{1 / D} \$$, where $\$ N \$$is the number of particles and \ D \$$ is the dimensionality of space.

For a dispersion relation \$|varepsilon \propto$|p|^{\wedge} \backslash$sigma\$, the number of particles in momentum space is given by:
$$N=\int \frac{d^D p}{(2 \pi)^D} \frac{1}{e^{(\varepsilon(p)-\mu) / k_B T}-1}$$
where $\$ T \$$is the temperature and \ \mathrm{k} B \$$ is the Boltzmann constant. In the low-temperature limit, we can approximate the Bose distribution function as:
$$\frac{1}{e^{(\varepsilon(p)-\mu) / k_B T}-1} \approx e^{-(\varepsilon(p)-\mu) / k_B T}$$
Using this approximation, we can rewrite the number of particles as:
$$N=\int \frac{d^D p}{(2 \pi)^D} e^{-(\varepsilon(p)-\mu) / k_B T}$$
Substituting \$|varepsilon \propto$|\mathrm{p}|^{\wedge} \backslash$sigma\$ and changing to spherical coordinates, we get:
$$N=\frac{V_D}{(2 \pi)^D} \int_0^{\infty} p^{D-1} e^{-\left(|p|^\sigma-\mu\right) / k_B T} d p$$
where $\$ V_{-} D \$$is the volume of a \ D \$$-dimensional sphere of radius $\$ R \$$:$$
V_D=\frac{\pi^{D / 2}}{\Gamma(D / 2+1)} R^D
$$For a fixed \ \backslash m u \$$, the integral can be evaluated by a saddle-point approximation. In the limit of low temperatures, the saddle-point is dominated by small values of $\$ \mathrm{p} \$$, so we can expand \|p|^{\wedge} \backslash sigma \$$ around $\$ p=0 \$$and keep only the leading term:$$
|p|^\sigma \approx p^\sigma \quad \text { for } p \rightarrow 0
$$Using this approximation, the integral becomes:$$
N \approx \frac{V_D}{(2 \pi)^D} \int_0^{\infty} p^{D-1} e^{-\left(p^\sigma-\mu\right) / k_B T} d p
$$Changing variables to \ \mathrm{x}=\mathrm{p}^{\wedge} \backslash sigma \$$ and using the gamma function, we get:
$$N \approx \frac{V_D}{\sigma(2 \pi)^D}\left(\frac{k_B T}{\mu}\right)^{D / \sigma} \Gamma\left(\frac{D}{\sigma}+1\right)$$
The ground-state energy in the low-temperature limit is then:
$$E_0=\frac{\mu}{\sigma}\left(\frac{N}{V_D}\right)^{1 / D}=\frac{\mu}{\sigma}\left(\frac{\sigma(2 \pi)^D}{V_D}\right)^{1 / D}\left(\frac{k_B T}{\mu}\right)^{1 / \sigma} \Gamma\left(\frac{D}{\sigma}+1\right)^{1 / D}$$
BEC occurs when the chemical potential $\$ \backslash$mu$\$$becomes equal to the ground-state energy \ E _0 \$$.

## Textbooks

• An Introduction to Stochastic Modeling, Fourth Edition by Pinsky and Karlin (freely
available through the university library here)
• Essentials of Stochastic Processes, Third Edition by Durrett (freely available through
the university library here)
To reiterate, the textbooks are freely available through the university library. Note that
you must be connected to the university Wi-Fi or VPN to access the ebooks from the library
links. Furthermore, the library links take some time to populate, so do not be alarmed if
the webpage looks bare for a few seconds.

Statistics-lab™可以为您提供und.edu MATH110 Statistical calculation统计计算课程的代写代考辅导服务！ 请认准Statistics-lab™. Statistics-lab™为您的留学生涯保驾护航。

## 统计代写|统计计算代写Statistical calculation代考|STA317

statistics-lab™ 为您的留学生涯保驾护航 在代写统计计算Statistical calculation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。我们的专家在代写统计计算Statistical calculation代写方面经验极为丰富，各种代写统计计算Statistical calculation相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|统计计算代写Statistical calculation代考|Role of the computer in statistics

In all aspects of business life we are likely to encounter increasing quantities of data. Computers and new information technologies literally put data at our fingertips; for example, stock levels in a warehouse some distance away or share prices in Japan can be established in minutes.

The Internet can provide access to data across continents at low cost. The challenge is to organise and analyse this information in such a way that managers can make sense of it by utilising statistical and quantitative techniques. Facilities such as spreadsheets or statistical and mathematical software packages make analysis techniques readily available to everyone. The effective use of such computer software requires that you are able to interpret the output that can be generated, not only in a strictly quantitative way but also in assessing its potential to help in business decision-making.

Computers also provide the opportunity to experiment with and explore data in ways that would not otherwise be possible.

A computer may be efficiently used in any processing operation that has one or more of the following characteristics:

• large volume of input
• repetition of projects
• greater speed desired in processing
• greater accuracy
• processing complexities that require electronic help.
It can help you develop your ideas about how to organise the information by using a ‘try and refine’ approach, which can take too long to carry out manually.

## 统计代写|统计计算代写Statistical calculation代考|Sources of data: where to get the data

A statistical study may require the collection of new data from scratch, referred to as primary data, or be able to use already existing data, known as secondary data. It is also possible to use a combination of both sources.

Secondary data is already available in processed form, such as a database, the Internet, libraries or records kept within your company, and has been collected for some purpose other than you intend to use it for. Data is often collected through the use of secondary sources because it is available at low cost, but you need to be sure that you are not using unsuitable data just because it is easily available. Secondary data can be obtained internally or externally.

Internal data comes from within the organisation for its own use, for example from accounting records, payrolls, inventories, sales records, etc.

External data is collected from sources outside the organisation, such as trade publications, consumer price indexes, newspapers, libraries, universities, official statistics supplied by the Department of Statistics and other government departments, a Nielsen report on shopping behaviour, stock exchange reports. databases of the Department of Statistics, data on the unemployment rate supplied by the Department of Labour, or data on HIV/Aids provided by the Department of Health or websites on the Internet.

Primary data is information collected by those wishing to collect their own data. The distinguishing feature of this data is that it will be both reliable and relevant to your purpose. As a result, primary data can take a long time to collect and may be expensive. Sources of primary data include experiments, observation, group discussions and the use of questionnaires under controlled conditions.

There are multiple methods and tools that can be used to collect data, but you must decide which method(s) will best answer your research questions.
The four main methods of collecting data are:

• face to face
• by phone
• by post
• via the Internet.

# 统计计算代考

## 统计代写|统计计算代写Statistical calculation代考|Role of the computer in statistics

• 输入量大
• 重复项目
• 加工需要更快的速度
• 更高的准确性
• 处理需要电子帮助的复杂性。
它可以帮助您通过使用“尝试和完善”的方法来发展关于如何组织信息的想法，这可能需要很长时间才能手动执行。

• 面对面
• 用电话
• 通过邮寄
• 通过互联网。

## 有限元方法代写

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。

## 统计代写|统计计算代写Statistical calculation代考|STAT407

statistics-lab™ 为您的留学生涯保驾护航 在代写统计计算Statistical calculation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。我们的专家在代写统计计算Statistical calculation代写方面经验极为丰富，各种代写统计计算Statistical calculation相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|统计计算代写Statistical calculation代考|Problem-solving steps

Solving a statistical problem typically comprises the following steps:

1. Identify the problem and ask the question you hope to answer.
2. Collect the information (or data) needed to answer the problem: Identify an appropriate data source and decide how to measure it. Decide whether an existing data source is adequate or whether new data must be collected. Determine if you will use an entire population or a representative sample. If using a sample, decide on a viable sampling method.
3. Analyse the data: Organise and summarise the data into tables and graphs. which are effective ways to present data. Numerical summaries allow increased understanding by making use of single values to represent the data. This initial analysis provides insight into important characteristics of the data and gives guidance in selecting appropriate methods for further analysis.
4. Interpret the results in order to draw conclusions, make recommendations and assess the risk of an incorrect decision about the original problem under investigation. With sampling, the process usually involves generalising from a small group – or sample – of individuals or objects that were studied to a much larger group or population.

As part of a weekly quality check to access the calibration of a filling machine, the quality control manager randomly selects 50 bottles of beer that were filled on a specific day.

1. Ask a question: Is the calibration of the filling machine still within acceptable standards?
2. Collect the appropriate data: Randomly select 50 bottles on a specified day and measure the contents of each bottle. Record the measurements to the nearest millilitre.
3. Analyse the data: Summarise the data in a table and draw a graph, such as a scatter plot, to show the sample data as well as a line graph on the same plot to indicate the desired fill. The average fill of the sample bottles can also be calculated together with the standard deviation and other descriptive summary statistics.
4. Interpret the results and draw conclusions. For example: Compare the scatter plot with the required standard line graph to get a visual impression of any deviations. The sample average can also be compared with the required average to access the calibration of the filling machine. You can extend the results from the sample of 50 bottles to all bottles filled during that week.

## 统计代写|统计计算代写Statistical calculation代考|THE LANGUAGE OF STATISTICS

• An investigation or experiment is any process of observation or measurement.
• Elements are the people or objects about which information is collected.
• A population is the entire group about which you want information. If the population contains a countable number of items, it is said to be finite, and when the number of items is unlimited, it is said to be infinite. A study of the entire population is known as a census. A parameter is a numerical measure that describes the population. It is calculated using all the data of the population, such as an average. It is usually indicated by a letter from the Greek alphabet (e.g. $\mu, \sigma, \pi)$.
• To gain information about the population, a portion of the population data can be examined. This portion of data is called a sample. The sample must be representative of the population. A representative sample is one in which the relevant characteristics of the sample elements are generally the same as the characteristics of the population elements. A statistic is a numerical measure that describes a sample. It is usually indicated by a letter from the Roman alphabet (e.g. $x, s, n, p$ ).
• A variable is a characteristic of interest about each element of a population or sample. It is the topic about which data is collected, such as the age of first-year students at a university or the mass of each first-year student. Not all students are the same age or weigh the same; this will vary from student to student. That means there is a variation in the weights and ages. If there were no variability in the weights or ages, statistical inference would not be necessary. The observed values of the variable are the data you will use in a statistical investigation.
• Variables can be classified as quantitative or qualitative.
• Qualitative or categorical variables provide information that is nonnumerical, such as marital status, type of job, gender, etc. Qualitative information can sometimes be coded to make it appear quantitative, but will have no meaning on a number line.
• Quantitative variables provide numerical measurements of the elements of a study. Arithmetic operations such as addition and subtraction can be performed on the values of a quantitative variable.

# 统计计算代考

## 统计代写|统计计算代写Statistical calculation代考|Problem-solving steps

1. 确定问题并提出您希望回答的问题。
2. 收集回答问题所需的信息（或数据）：确定合适的数据源并决定如何衡量它。确定现有数据源是否足够或是否必须收集新数据。确定您将使用整个总体还是代表性样本。如果使用样本，请确定可行的抽样方法。
3. 分析数据：将数据组织和汇总为表格和图表。这是呈现数据的有效方式。数字摘要通过使用单个值来表示数据来增加理解。这种初步分析提供了对数据重要特征的深入了解，并为选择合适的方法进行进一步分析提供了指导。
4. 解释结果以得出结论、提出建议并评估对正在调查的原始问题做出错误决定的风险。通过抽样，这个过程通常涉及从一小群人或样本中将被研究的个体或对象推广到更大的群体或人口。

1. 问一个问题：灌装机的校准是否还在可接受的标准之内？
2. 收集适当的数据：在指定日期随机选择 50 个瓶子并测量每个瓶子的内容。记录测量值，精确到毫升。
3. 分析数据：汇总表格中的数据并绘制图表（例如散点图）以显示示例数据，并在同一图表上绘制折线图以指示所需的填充。样品瓶的平均填充量也可以与标准偏差和其他描述性汇总统计一起计算。
4. 解释结果并得出结论。例如：将散点图与所需的标准折线图进行比较，以获得任何偏差的视觉印象。样本平均值也可以与所需的平均值进行比较，以访问灌装机的校准。您可以将结果从 50 个瓶子的样本扩展到该周灌装的所有瓶子。

## 统计代写|统计计算代写Statistical calculation代考|THE LANGUAGE OF STATISTICS

• 调查或实验是任何观察或测量的过程。
• 元素是收集信息的人或物。
• 人口是您想要了解其信息的整个群体。如果总体包含可数的项目，则称它是有限的，而当项目的数量是无限的时，则称它是无限的。对整个人口的研究称为人口普查。参数是描述总体的数值度量。它是使用人口的所有数据（例如平均值）计算得出的。它通常由希腊字母表中的字母表示（例如米,p,π).
• 要获得有关人口的信息，可以检查人口数据的一部分。这部分数据称为样本。样本必须代表总体。代表性样本是样本要素的相关特征与总体要素的特征大致相同的样本。统计量是描述样本的数值度量。它通常由罗马字母表中的一个字母表示（例如X,秒,n,p ).
• 变量是关于总体或样本的每个元素的感兴趣特征。这是关于收集数据的主题，例如大学一年级学生的年龄或每个一年级学生的质量。并非所有学生的年龄或体重都相同；这将因学生而异。这意味着体重和年龄存在差异。如果体重或年龄没有变化，就没有必要进行统计推断。变量的观察值是您将在统计调查中使用的数据。
• 变量可以分为定量的或定性的。
• 定性或分类变量提供非数值信息，例如婚姻状况、工作类型、性别等。有时可以对定性信息进行编码以使其看起来是定量的，但在数轴上没有意义。
• 定量变量提供研究要素的数值测量。可以对定量变量的值执行加法和减法等算术运算。

## 有限元方法代写

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。

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|R Markdown and Rhtml

R 统计计算和统计计算是采用计算、图形和数字方法解决统计问题的两个领域，这使得多功能的R语言成为这些领域的理想计算环境。

statistics-lab™ 为您的留学生涯保驾护航 在代写R 统计计算Introduction to Statistical Computing with R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R 统计计算方面经验极为丰富，各种代写R 统计计算Introduction to Statistical Computing with R相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等楖率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|R 统计计算作业代写Introduction to Statistical Computing with R代考|Publishing a notebook

Markdown (created by John Gruber and Aaron Swartz) is an easy-to-read and easy-to-write markup language that is deșigned to make preparing HTML documents (web pages) easier. The Markdown syntax is inspired by how people write plain text e-mails. For example, to emphasize a word in an e-mail, constructs like * emphasized word* or_emphasized word_are frequently used. Also, people tend to use asterisks or dashes to represent hullet lists in plain text The idea of Markdown is to treat such constructions as actual markup commands by translating them to equivalent HTML syntax (web page). With Markdown, you can alter the appearance of text by altering its size, typeface, and more. What you cannot do with Markdown, is to alter document properties such as page size, margin sizes, and so on. If you need to control such features, you can consider switching to LaTeX (described in the following section). Alternatively, one can use Max Kuhn’s odfWeave package (not supported by RStudio).
With RStudio, you can generate reports with . Rmd or . Rhtm1 files – in these files you combine R output with Markdown or HTML. Note that RStudio also supports editing plain Markdown (.md) and HTML (. html) files.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Workflow for R Markdown

To create a report with R Markdown, open or start a new . Rmd file (File $\mid$ New $\mid \mathbf{R}$ Markdown). Note that the . Rmd tab has special menu items.

Click on the Knit HTML button (Ctrl + Shift $+$ H or Command $+$ Shift $+H$ ) to create and open the report. If a report is already open, it will be updated.
As a first example, let us create a new . Rmd file, empty it, and type:
Adding_one and one_gives ‘1 $+1$ ‘
Now click on the Knit HTML button. RStudio generates an HTML file and opens it in a viewer. It is important to realize that this HTML file is self-contained. That is, all text and figures are contained in a single file, whereas web pages usually rely on many external references to include pictures, for example. The main advantage is that you can store the HTML file and send it as a single unit by an e-mail.
When a new . Rmd file is created, RStudio opens an example file with a starter guide. If you click on the MD button on the left of the Knit HTML button, a help file will open showing some of Markdown’s syntax. On the right-hand side, there are the Run, Rerun, and Chunks buttons. Since these are present for Rnw/Sweave as well as for Rmd and Rhtml files, they will be discussed separately in the section on code chunks and chunk options.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|An extended example

To demonstrate some of the most important capabilities of R Markdown, we will walk step by step through an extensive example. In this example, we’ll see how to create a document and section titles, equations, how to include code chunks inline as well as in separate blocks, and how to add links to other documents. We’ll also see the first example of code chunk options. You can either type the example in an empty file or pull the example from github by clicking on Project | New | Version control | Git and entering https://github.com/ratudiobook/abalone.git.
Also see Chapter 4, Managing R Projects on version control. Alternatively, you can copy the preceding URL to your browser and read through the code online.
In this example, we are going to create a report of a simple analysis on the Abalone dataset that we’ve used throughout the book. We assume that by now you have an RStudio project directory with a subdirectory data that holds the abalone. cav file. See Chapter 1, Getting Started, to see how to obtain the file (it is also included in the github repository mentioned in the preceding section).

To start, create a new directory named Rmd and a file called density. Rmd. In the example, we are going to estimate the “density” (weight per volume) of abalones, by modeling them as rectangular boxes. We start with a title, author name, and date as follows:

Here, the double-underlining tells Markdown that the text above it should be treated as the document title (in HTML it will be put between the $<\mathrm{H} 1>$ tag as well as between $<$ titles</titles). Under the title, we add the author names, and between brackets, the current date as returned by $R$. This is the first example of inline code. In Markdown, text between single backticks is interpreted and printed as code. By adding an $r$ behind the first backtick, we tell knitr to replace the R code between backticks with its result.
Next, an introducing section is added.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Publishing a notebook

Markdown（由 John Gruber 和 Aaron Swartz 创建）是一种易于阅读和易于编写的标记语言，旨在简化 HTML 文档（网页）的准备工作。Markdown 语法的灵感来自人们编写纯文本电子邮件的方式。例如，为了强调电子邮件中的一个词，经常使用*强调词*或_强调词_之类的结构。此外，人们倾向于使用星号或破折号以纯文本形式表示 hullet 列表。 Markdown 的想法是通过将这些结构转换为等效的 HTML 语法（网页），将它们视为实际的标记命令。使用 Markdown，您可以通过更改文本的大小、字体等来更改文本的外观。使用 Markdown 不能做的是更改文档属性，例如页面大小、边距大小等。如果您需要控制这些功能，您可以考虑切换到 LaTeX（在下一节中描述）。或者，可以使用 Max Kuhn 的 odfWeave 包（RStudio 不支持）。

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Workflow for R Markdown

Adding_one and one_gives ‘1+1’

## 广义线性模型代考

statistics-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。

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Generating Reports

R 统计计算和统计计算是采用计算、图形和数字方法解决统计问题的两个领域，这使得多功能的R语言成为这些领域的理想计算环境。

statistics-lab™ 为您的留学生涯保驾护航 在代写R 统计计算Introduction to Statistical Computing with R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R 统计计算方面经验极为丰富，各种代写R 统计计算Introduction to Statistical Computing with R相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等楖率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|R 统计计算作业代写Introduction to Statistical Computing with R代考|Generating Reports

In this chapter, we treat three different ways to produce reports that automatically include the results of an analysis.

A very important feature of reproducible science is generating reports. The main idea of automatic report generation is that the results of analyses are not manually copied to the report. Instead, both the $R$ code and the report’s text are combined in one or more plain text files. The report is generated by a tool that executes the chunks of code, captures the results (including figures), and generates the report by weaving the report’s text and results together. To achieve this, you need to learn a few special commands, called markup specifiers, that tell the report generator which part of your text is $\mathrm{R}$ code, and which parts you want in special typesetting such as boldface or italic. There are several markup languages to do this, but the following is a minimal example using the Markdown language:

The left panel shows the plain text file in RStudio’s editor and the right panel shows the web page that is generated by clicking on the Knit HTML button. The markup specifiers used here are the double asterisks for boldface, single underscores for slanted font, and the backticks for code. By adding an $x$ to the first backtick, the report generator executes the code following it.

The Markdown language is one of many markup languages in existence and RStudio supports several of them. RStudio has excellent support for interweaving code with Markdown, HTML, LaTeX, or even in plain comments. We’ve encountered the latter option already in Chapter 1, Getting Started, when we created a notebook straight from R script.

Notebooks are useful to quickly share annotated lines of code or results. There are a few ways to control the layout of a notebook. The Markdown language is easy to learn and has a fair amount of layout options. It also allows you to include equations in the LaTeX format. The HTML option is really only useful if you aim to create a web page. You should know, or be willing to learn HTML to use it. The result of these three methods is always a web page (that is, an HTML file) although this can be exported to PDF.
If you need ultimate control over your document’s layout, and if you need features like automated bibliographies and equation numbering, LaTeX is the way to go. With this last option, it is possible to create papers for scientific journals straight from your analysis.

Depending on the chosen system, a text file with a different extension is used as the source file. The following table gives an overview.

Finally, we note that the interweaving of code and text (often referred to as literate programming) may serve two purposes. The first, described in this chapter, is to gencrate a data analysis report by cxccuting code to produce the result. The second is to document the code itself, for example, by describing the purpose of a function and all its arguments. The latter purpose will be discussed in the next chapter, where we will discuss the Roxygen2 package for code documentation.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Prerequisites for report generation

For notebooks, R Markdown, and Rhtml, RStudio relies on Yihui Xie’s knitr package for executing code chunks and merging the results. The knitr package can be installed via RStudio’s Packages tab or with the command install. packages (“knitr”).

For LaTeX/Sweave files, the default is to use R’s native Sweave driver. The knitr package is easier to use and has more options for fine-tuning, so in the rest of this chapter we assume that knitr is always used. To make sure that knitr is also used for Sweave files, go to Tools | Options | Sweave and choose knitr as Weave Rnw files. If you’re working in an RStudio project, you can set this as a project option as well by navigating to Project | Project Options | Sweave. When you work with LaTeX/Sweave, you need to have a working LaTeX distribution installed. Popular distributions are TeXLive for Linux, MikTeX for Windows, and MacTeX for Mac OS X.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Notebook options

RStudio offers three ways to generate a notebook from an Rscript – the simplest are Default and knitr:istitch. These only differ a little in layout. The knitr:spin mode allows you to use the Markdown markup language to specify text layout. The markup options are presented after navigating to File | Notebook or after clicking on the Notebook button. Under the hood, the Default and knitr::stitch options use knitr to generate a Markdown file which is then directly converted to a web page (HTML file). The knitr:spin mode allows for using Markdown commands in your comments and will convert your . R file to a .Rmd (R Markdown) file before further processing.

In Default mode, $R$ code and printed results are rendered to code blocks in a fixedwidth font with a different background color. Figures are included in the output and the document is prepended with a title, an optional author name, and the date. The only option to include text in your output is to add it as an R comment (behind the # sign) and it will be rendered as such.
In knitr:stitch mode, instead of prepending the report with an author name and date, the report is appended with a call to Sys . time () and R’s sessionInfo(). The latter is useful since it shows the context in which the code was executed including R’s version, locale settings, and loaded packages. The result of the knitr::stitch mode depends on a template file called knitr-template. Rnw, included with the knitr package. It is stored in a directory that you can find by typing system.
file (‘misc’, package=’ knitr’).

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Generating Reports

Markdown 语言是现有的许多标记语言之一，RStudio 支持其中的几种。RStudio 非常支持将代码与 Markdown、HTML、LaTeX 甚至是纯注释交织在一起。我们已经在第 1 章“入门”中遇到过后一种选择，当时我们直接从 R 脚本创建了一个笔记本。

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Notebook options

RStudio 提供了三种从 Rscript 生成笔记本的方法——最简单的是 Default 和 knitr:istitch。这些只是布局略有不同。knitr:spin 模式允许您使用 Markdown 标记语言来指定文本布局。导航到 File | 后会显示标记选项。笔记本或单击笔记本按钮后。在后台， Default 和 knitr::stitch 选项使用 knitr 生成 Markdown 文件，然后直接将其转换为网页（HTML 文件）。knitr:spin 模式允许在您的评论中使用 Markdown 命令，并将转换您的 . 在进一步处理之前将 R 文件转换为 .Rmd (R Markdown) 文件。

## 广义线性模型代考

statistics-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。

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Subversion

R 统计计算和统计计算是采用计算、图形和数字方法解决统计问题的两个领域，这使得多功能的R语言成为这些领域的理想计算环境。

statistics-lab™ 为您的留学生涯保驾护航 在代写R 统计计算Introduction to Statistical Computing with R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R 统计计算方面经验极为丰富，各种代写R 统计计算Introduction to Statistical Computing with R相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等楖率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|R 统计计算作业代写Introduction to Statistical Computing with R代考|Create a new

In Subversion, the location of the repository that stores increments of your project is different from the directory where you actually do your work. To create a project under Subversion version control, perform the following steps:

1. Create a new svn repository.
In your operating system’s command-line interface, you can do this by typing svnadmin create epath to projectnames. A new directory will be created with some svn-specific files. You should never alter this directory yourself. It is where Subversion will store incremental versions of your project.
2. In some directory, for example, in /projects/, do an svn checkout. In your operating system’s command-line interface, you can do this by typing svn checkout file:/// (notice the triple slash after file:).
3. Open RStudio. Go to Project | Create project… | Existing directory. Choose the directory that you just checked out from the empty Subversion repository.
4. Or, instead of the last two steps, you can go to Project | Create project | Version Control | Subversion. Type file: $/ / /<$ path to projectname> in Repository Url and RStudio will do the rest for you.
5. We made a fresh empty repository named abalone, checked it out with Subversion, and created an RStudio project in the checked-out directory. The RStudio panel now contains an extra tab SVN, shown in the following screenshot. We will replay some of the steps of the previous section, but now with Subversion.

The yellow question mark shows that abalone. Rproj is not (yet) in the central repository.
The SVN tab of RStudio has a Status column containing icons that indicate the status of files with respect to their versions in the central repository. At the moment, there is only the abalone. Rproj file, which has not been added to the repository yet, so it is marked with a question mark icon. An overview of SVN status icons is given in the following table.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Working with a team

Working with a team on a project is almost unthinkable without a version control system. In principle, with GIT it is possible to work without a central repository. However, it is very common to still work with a central repository where collaborators can pull changes from and push their own. There are several online resources where you can host your open source projects free of charge. Popular ones include github (obviously supporting GTT only), code. google. com, and bitbucket. The latter two support GIT as well as Subversion. At the time of writing, bitbucket is the only of these three hosting non-open repositories for free as well.

To start on a project with an online repository, you need to create an account and create a new project at the hosting site. When you create a project, you usually have to choose the version control system you want to use. Once the online repository is created, start RStudio and click on Project | New project. Choose Check out a project from a version control repository. After choosing the version control system, you will be asked for the URL of your repository and where to store the files on your own computer.

Now, for GIT repositories, the workflow is as follows. To get the updates from your collaborators, pull the latest changes via the Git tab menu More | Pull Branches. Next, you can do the work, stage files, and commit them with a comment. After the commit, the local copy of the GIT repository is updated. However, to send the same changes to your coworkers, you need to push the latest commits to the central repository via More | Push Branch.
For subversion repositories, you need to update your working copy, using More | Update. After the work is done, when you commit the changes, they will be immediately uploaded to the central repository.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Further reading

There is much more to be said about version control and we have only covered enough here to get you started with the most common operations. As you grow accustomed with version control, you probably want to start using more features than are currently interfaced through RStudio. The first features to look into are probably branching and merging of development lines and reverting commits. A good online resource for using GIT on the command line is the GIT book (http://git-scm. com/book). For Subversion, the SVN book (http: // avnbook . red-bean . com), which is partly written by some of Subversion’s developers comes highly recommended. Both books can be read for free online or ordered as a hard copy.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Create a new

1. 创建一个新的 svn 存储库。
在您的操作系统的命令行界面中，您可以通过键入 svnadmin create epath to projectnames 来执行此操作。将使用一些特定于 svn 的文件创建一个新目录。你不应该自己改变这个目录。Subversion 将在这里存储项目的增量版本。
2. 在某个目录中，例如，在 /projects/ 中，执行 svn checkout。在您的操作系统的命令行界面中，您可以通过键入 svn checkout file:/// 来执行此操作（注意 file: 后面的三个斜杠）。
3. 打开 RStudio。前往项目 | 创建项目… | 现有目录。从空的 Subversion 存储库中选择您刚刚签出的目录。
4. 或者，代替最后两个步骤，您可以转到项目 | 创建项目 | 版本控制 | 颠覆。类型文件：///<存储库 URL 中 projectname> 的路径，RStudio 将为您完成剩下的工作。
5. 我们新建了一个名为 abalone 的空存储库，使用 Subversion 将其签出，并在签出目录中创建了一个 RStudio 项目。RStudio 面板现在包含一个额外的选项卡 SVN，如以下屏幕截图所示。我们将重播上一节的一些步骤，但现在使用 Subversion。

RStudio 的 SVN 选项卡有一个 Status 列，其中包含指示文件相对于中央存储库中的版本的状态的图标。此刻，只有鲍鱼。rproj 文件，该文件尚未添加到存储库中，因此标有问号图标。下表给出了 SVN 状态图标的概述。

## 广义线性模型代考

statistics-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。

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Version control

R 统计计算和统计计算是采用计算、图形和数字方法解决统计问题的两个领域，这使得多功能的R语言成为这些领域的理想计算环境。

statistics-lab™ 为您的留学生涯保驾护航 在代写R 统计计算Introduction to Statistical Computing with R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R 统计计算方面经验极为丰富，各种代写R 统计计算Introduction to Statistical Computing with R相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等楖率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|R 统计计算作业代写Introduction to Statistical Computing with R代考|Installing GIT or Subversion

You need to have GIT and/or Subversion installed to be able to use them from RStudio. Both are frec and open source tools. Most Linux distributions include a version of GIT and Subversion in their application repositories. For example, under Debian-based distributions such as Ubuntu, open a terminal and type the following statements to install GIT or Subversion:
sudo apt-get install git-core
sudo apt-get install subversion
Alternatively, use Synaptic or another graphical package manager to install it. For Windows, the authors of RStudio recommend msysGit (ht tp://msysgit. github. com/) as the GIT client and SlikSVN for Subversion. The popular TortoiseSVN (tortoisesvn. net) is not supported by RStudio since it does not offer a command-line interface that RStudio uses to control the version control system. You can use TortoiseSVN alongside RStudio with no problems, however. For OS X, you can use GIT-osx-installer available at http://code . google. $\mathrm{com} / \mathrm{p} / \mathrm{git}$-osx-installer. For OS X version $10.7$ and lower, a Subversion client is already installed. For $10.8$ and higher, you need to install Xcode and download the command-line tools via Xcode Preferences.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Version control for single-person projects

Although it may at first not be obvious, using a version control system for your own work has its merits. Once you grow accustomed to managing $R$ projects with source control, you’ll find it hard to believe how you managed without it. In the following sections, we will demonstrate a simple example, first using GIT and next using Subversion as version control system.

To demonstrate how to work with a local version control repository, we will work through some examples of our Abalone project. If you don’t have those files (anymore), you can download or view them at ht tps://github.com/ rstudiobook/abalone. When we left the project in Chapter 1, Getting Started, we had the following files:

If you set the project up with the Create a git repository for the project option checked, there should be a Git tab near your workspace browser. If not, you can still create one now by going to Project | Project options | Version control and choosing Git as the version control system from the drop-down ment

Once a repository has been created, working with GIT has the following basic workflow:

1. If necessary, get the latest version of the project from the repository (pull). This is only necessary when collaborating with multiple developers.
2. Do the work – create, delete, move, or alter files.
3. Stage changes you want to commit to the repository. That is, you need to tell GIT which of the alterations should make it to the repository.
4. Commit the staged changes to the repository.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Existing directory

The staging part of the workflow is an important feature that sets GIT apart from Subversion. Staging gives you the freedom to try quick and dirty stuff that you may not want to end up in the repository. It saves you making the famous <filenames. 1 copy, since none of the changes will be submitted as long as you don’t stage them. Reverting work that has been staged, but not committed, can be done with the click of a button in RStudio and will be discussed next.

Thus far in our Abalone example, we have only created a repository for GIT. Nothing has been committed to that repository yet, and we first need to decide which files we want to bring under version control. The only files that are directly created by us are abalone. cav and abalone. R. The abalone. htmi file was generated automatically from our R script when we compiled the notebook. Since this is the output of our script, we do not need to put it under version control. It can be recreated any time we want. The files . gitignore and . Rhistory are for GIT and RStudio’s internal use and do not need to be put under version control right now. In some cases, for example, when working with multiple people on a project, it can still be convenient to bring the . gitignore file under version control.

To add files to the version control system, open the Git tab, near the Workspace panel, and mark abalone.R and abalone.csv as shown in the following screenshot:By marking these files, we tell GIT that the files are staged for submission to the repository. This is indicated by the status icons between the checkmarks and the filenames The Status column has two columns of icons The right column is used to indicate that GIT has noticed that a file has been changed since its last commit. A question mark means that the file has not been added to version control yet. When you stage a file, the left icon indicates what the committing action will be. In the preceding screenshot, the A stands for adding. The following table lists the icon combinations used in GIT’s Status column.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Installing GIT or Subversion

sudo apt-get install git-core
sudo apt-get install subversion

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Version control for single-person projects

1. 如有必要，从存储库中获取最新版本的项目（拉取）。仅在与多个开发人员协作时才需要这样做。
2. 做这项工作——创建、删除、移动或更改文件。
3. 您要提交到存储库的阶段更改。也就是说，您需要告诉 GIT 哪些更改应该进入存储库。
4. 将分阶段的更改提交到存储库。

## 广义线性模型代考

statistics-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。

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Managing R Projects

R 统计计算和统计计算是采用计算、图形和数字方法解决统计问题的两个领域，这使得多功能的R语言成为这些领域的理想计算环境。

statistics-lab™ 为您的留学生涯保驾护航 在代写R 统计计算Introduction to Statistical Computing with R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R 统计计算方面经验极为丰富，各种代写R 统计计算Introduction to Statistical Computing with R相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等楖率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|R 统计计算作业代写Introduction to Statistical Computing with R代考|R projects

In Chapter 1, Getting Started, we introduced the concept of a compendium – the set of scripts and data files that reproduce a statistical analyses as well as the report that is based on it. Managing growing sets of interdependent files, especially when multiple people are working on the same analyses, can be a hassle. RStudio’s project management features make things a lot more manageable.

Technically, an RStudio project is just a directory with the name of the project and a few files and folders created by RStudio for internal purposes. It typically holds your scripts, data, and reports, which you may manage through RStudio’s file manager tab or through your operating system’s file manager. The project directory can also contain subdirectories.

When a project is reopened, RStudio opens every file and data view that was open when the project was closed the last time. Moreover, a new R session is started in the project directory, its working directory is set to the project directory, and the history and workspace data are reloaded (if they were saved the last time). This means that when you reopen a project, R will be in (nearly) the exact same state as when you closed it the last time, so you can continue where you left off. A possible exception is when you’re using a package that creates objects outside of R’s memory space; such objects are obviously not stored in a . Rdata file when $R$ is closed. One example of such a package is 1 psolve, which creates a linear program definition for GNU Ipsolve outside of R’s memory space while the corresponding R object is just a reference to that external object.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Creating an R project

When creating a project, you have the option to start from scratch (New Directory), to turn an existing directory into a project managed with RStudio (Existing Directory), or to hook up to an existing project and download a project from a repository (version contrul). We will save the latler uption for the section un version contrul.

When a project is created, RStudio creates a text file called . Rproj, which is used to store the project-specific options such as which I aTeX compiler to use. Although it is a simple text file, you should neither alter its contents by direct editing nor remove it, or RStudio may not recognize the folder as a project anymore. Besides the <projectnames. Rproj file, RStudio creates a hidden directory called . Rproj . user. This folder is used to store some information between sessions, so your RStudio session looks exactly the way you left it when switching between projects or leaving and restarting RStudio. It is also used to make sure that two different users do not open the same $\mathrm{R}$ project at the same time. This wouldn’t make sense since each user may have personal pane layout options set and that are not to be shared between collaborators. To collaborate on a project, one usually sets up a (central) repository. That way, each user gets a copy with their own . Rproj – user directory. Using version control tools (to be discussed at the end of the chapter), contributions from collaborators can be merged.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Directory structure and file manipulations

For simple projects, a single script file and one data file can be sufficient. But as analyses grow and become more complex, organizing the work in a well-chosen directory structure becomes almost inevitable. A commonly-used subdivision is to put all files of a certain type in the same directory, for example:

• R: The directory that holds scripts or files with custom functions
• data: All data needed for the analysis
• doc: Articles or other documents related to the analyses
• reports/html/latex: A directory with generated reports from the analysisNavigating directories is done by clicking on a directory name in the file list or on the path shown at the top of the list. The green, angled arrow takes you one step up in the directory tree. To alter a file’s name, or to move or delete it, you need to select it first using the checkbox in front of it, before choosing one of the menu items:

To import files into your project, just copy the file to the project directory or a subdirectory thereof, using your operating system’s file browser. RStudio’s file browser tab does not support dragging-and-dropping files into its file browser. Instead, the button with three dots at the right of the menu opens a file or folder browser of your operating system.
Data does not necessarily have to be stored in the project directory since $R$ can read data from almost anywhere, including the databases and the web. If your data is not stored under the project directory, it is a good idea to save the references to where the data is stored (paths, filenames, database connection strings) in a single $R$ file that is to be sourced before running the actual analysis.

## 统计代写|R 统计计算作业代写Introduction to Statistical Computing with R代考|Directory structure and file manipulations

• R：包含自定义函数的脚本或文件的目录
• 数据：分析所需的所有数据
• doc：与分析相关的文章或其他文件
• 报告/html/latex：通过单击文件列表中的目录名称或列表顶部显示的路径来完成从分析中生成报告的目录导航目录。绿色的有角度的箭头会带您在目录树中上一层楼。要更改文件名，或者移动或删除它，您需要先使用它前面的复选框来选择它，然后再选择其中一个菜单项：

## 广义线性模型代考

statistics-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。