## 统计代写|抽样调查作业代写sampling theory of survey代考|STAT506

statistics-lab™ 为您的留学生涯保驾护航 在代写抽样调查sampling theory of survey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。我们的专家在代写抽样调查sampling theory of survey方面经验极为丰富，各种代写抽样调查sampling theory of survey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|抽样调查作业代写sampling theory of survey代考|EXAMPLES OF REPRESENTATIVE STRATEGIES

The ratio estimator
$$t_{1}=X \frac{\sum_{i \in s} Y_{i}}{\sum_{i \in s} X_{i}}$$
is of special importance because of its traditional use in practice. Here, $\left(p, t_{1}\right)$ is obviously representative with respect to a size measure $x$, more precisely to $\left(X_{1}, \ldots, X_{N}\right)$, whatever the sampling design $p$.

Note, however, that $t_{1}$ is usually combined with SRSWOR or SRSWR. The sampling scheme of LAHIRI-MIDZUNO-SEN (LAHIRI, 1951; MIDZUNO, 1952; SEN, 1953) (LMS) yields a design of interest to be employed in conjunction with $t_{1}$ by rendering it design unbiased.
The Hansen-Hurwitz (HH, 1943) estimator (HHE)
$$t_{2}=\frac{1}{n} \sum_{i=1}^{N} f_{s i} \frac{Y_{i}}{P_{i}}$$ with $f_{s i}$ as the frequency of $i$ in $s, i \in \mathcal{U}$, combined with any design $p$, gives rise to a strategy representative with respect to $\left(P_{1}, \ldots, P_{N}\right)^{\prime}$. For the sake of design unbiasedness, $t_{2}$ is usually based on probability proportional to size (PPS) with replacement (PPSWR) sampling, that is, a scheme that consists of $n$ independent draws, each draw selecting unit $i$ with probability $P_{i}$.

Another representative strategy is due to RAO, HARTLEY and COCHRAN (RHC, 1962). We first describe the sampling scheme as follows: On choosing a sample size $n$, the population $\mathcal{U}$ is split at random into $n$ mutually exclusive groups of sizes suitably chosen $N_{i}\left(i=1, \ldots, n ; \sum_{1}^{n} N_{i}=N\right)$ coextensive with $\mathcal{U}$, the units bearing values $P_{i}$, the normed sizes $\left(0<P_{i}<1, \sum P_{i}=1\right)$. From each of the $n$ groups so formed independently one unit is selected with a probability proportional to its size given the units falling in the respective groups.

## 统计代写|抽样调查作业代写sampling theory of survey代考|Raj’s Estimator t5

Another popular strategy is due to RAJ $(1956,1968)$. The sampling scheme is called probability proportional to size without replacement (PPSWOR) with $P_{i}$ ‘s $\left(02)$ draw a unit $i_{n}\left(\neq i_{1}, \ldots, i_{n-1}\right)$ is chosen with probability
$$\frac{P_{i_{n}}}{1-P_{i_{1}}-P_{i_{2}}-\ldots,-P_{i_{n-1}}}$$ out of the units of $U$ minus $i_{1}, i_{2}, \ldots, i_{n-1}$. Then,
\begin{aligned} e_{1} &=\frac{Y_{i_{1}}}{P_{i_{1}}} \ e_{2} &=Y_{i_{1}}+\frac{Y_{i_{2}}}{P_{i_{2}}}\left(1-P_{i_{1}}\right) \ e_{j} &=Y_{i_{1}}+\ldots+Y_{i_{j-1}}+\frac{Y_{i_{j}}}{P_{i_{j}}}\left(1-P_{i_{1}}-\ldots-P_{i_{j-1}}\right) \end{aligned}
$j=3, \ldots, n$ are all unbiased for $Y$ because the conditional expectation
\begin{aligned} E_{c} & {\left[e_{j} \mid\left(i_{1}, Y_{i_{1}}\right), \ldots,\left(i_{j-1}, Y_{i_{j-1}}\right)\right] } \ &=\left(Y_{i_{1}}+\ldots,+Y_{i_{j-1}}\right)+\sum_{\substack{k=1 \ \left(\neq i_{1}, \ldots, i_{j-1}\right)}}^{N} Y_{k}=Y . \end{aligned}
So, unconditionally, $E_{p}\left(e_{j}\right)=Y$ for every $j=1, \ldots, n$, and
$$t_{5}=\frac{1}{n} \sum_{j=1}^{n} e_{j},$$
called Raj’s (1956) estimator, is unbiased for $Y$.

## 统计代写|抽样调查作业代写sampling theory of survey代考|EXAMPLES OF REPRESENTATIVE STRATEGIES

$$t_{1}=X \frac{\sum_{i \in s} Y_{i}}{\sum_{i \in s} X_{i}}$$

$$t_{2}=\frac{1}{n} \sum_{i=1}^{N} f_{s i} \frac{Y_{i}}{P_{i}}$$

## 统计代写|抽样调查作业代写sampling theory of survey代考|Raj’s Estimator t5

$$\frac{P_{i_{n}}}{1-P_{i_{1}}-P_{i_{2}}-\ldots,-P_{i_{n-1}}}$$

$$e_{1}=\frac{Y_{i_{1}}}{P_{i_{1}}} e_{2}=Y_{i_{1}}+\frac{Y_{i_{2}}}{P_{i_{2}}}\left(1-P_{i_{1}}\right) e_{j}=Y_{i_{1}}+\ldots+Y_{i_{j-1}}+\frac{Y_{i_{j}}}{P_{i_{j}}}\left(1-P_{i_{1}}-\ldots-P_{i_{j-1}}\right)$$
$j=3, \ldots, n$ 都是公正的 $Y$ 因为条件期望
$$E_{c}\left[e_{j} \mid\left(i_{1}, Y_{i_{1}}\right), \ldots,\left(i_{j-1}, Y_{i_{j-1}}\right)\right]=\left(Y_{i_{1}}+\ldots,+Y_{i_{j-1}}\right)+\sum_{k=1} \sum_{\left(\neq i_{1}, \ldots, i_{j-1}\right)}^{N} Y_{k}=Y .$$

$$t_{5}=\frac{1}{n} \sum_{j=1}^{n} e_{j}$$

## 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|抽样调查作业代写sampling theory of survey代考|STAT7124

statistics-lab™ 为您的留学生涯保驾护航 在代写抽样调查sampling theory of survey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。我们的专家在代写抽样调查sampling theory of survey方面经验极为丰富，各种代写抽样调查sampling theory of survey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|抽样调查作业代写sampling theory of survey代考|SAMPLING SCHEMES

A unified theory is developed by noting that it is enough to establish results concerning $(p, t)$ without heeding how one may actually succeed in choosing samples with preassigned probabilities. A method of choosing a sample draw by draw, assigning selection probabilities with each draw, is called a sampling scheme. Following HANURAV (1966), we show below that starting with an arbitrary design we may construct a sampling scheme.

Suppose for each possible sample $s$ from $U$ the selection probability $p(s)$ is fixed. Let
$$\begin{array}{lll} \beta_{i 1}=p\left(i_{1}\right), & \beta_{i_{1}, i_{2}}=p\left(i_{1}, i_{2}\right), \ldots, & \beta_{i_{1}, \ldots, i_{n}}=p\left(i_{1}, \ldots, i_{n}\right) \ \alpha_{i 1}=\Sigma_{1} p(s), & \alpha_{i_{1}, i_{2}}=\Sigma_{2} p(s), \ldots, & \alpha_{i_{1}, \ldots, i_{n}}=\Sigma_{n} p(s) \end{array}$$
where $\Sigma_{1}$ is the sum over all samples $s$ with $i_{1}$ as the first entry; $\Sigma_{2}$ is the sum over all samples with $i_{1}, i_{2}$, respectively, as the first and second entries in $s, \ldots$, and $\Sigma_{n}$ is the sum over all samples of which the first, second, $\ldots, n$th entries are, respectively, $i_{1}, i_{2}, \ldots, i_{n}$.

Then, let us consider the scheme of selection such that on the first draw from $U, i_{1}$ is chosen with probability $\alpha_{i 1}$, a second draw from $U$ is made with probability
$$\left(1-\frac{\beta_{i 1}}{\alpha_{i 1}}\right) \text {. }$$
On the second draw from $U$ the unit $i_{2}$ is chosen with probability
$$\begin{gathered} \alpha_{i_{1}, i_{2}} \ \alpha_{i 1}-\beta_{i 1} \end{gathered}$$
A third draw is made from $U$ with probability
$$\left(1-\frac{\beta_{i_{1}, i_{2}}}{\alpha_{i_{1}, i_{2}}}\right)$$

## 统计代写|抽样调查作业代写sampling theory of survey代考|CONTROLLED SAMPLING

Now, consider an arbitrary design $p$ of fixed size $n$ and a linear estimator $t$; suppose a subset $S_{0}$ of all samples is less desirable from practical considerations like geographical location, inaccessibility, or, more generally, costliness. Then, it is advantageous to replace design $p$ by a modified one, for example, $q$, which attaches minimal values $q(s)$ to the samples $s$ in $S_{0}$ keeping
$$\begin{gathered} E_{p}(t)=E_{q}(t) \ E_{p}(t-Y)^{2}-E_{q}(t-Y)^{2} \end{gathered}$$
and even maintaining other desirable properties of $p$, if any. A resulting $q$ is called a controlled design and a corresponding scheme of selection is called a controlled sampling scheme. Quite a sizeable literature has grown around this problem of finding appropriate controlled designs. The methods of implementing such a scheme utilize theories of incomplete block designs and predominantly involve ingeneous devices of reducing the size of support of possible samples demanding trials and errors. But RAO and NIGAM (1990) have recently presented a simple solution by posing it as a linear programming problem and applying the well-known simplex algorithm to demonstrate their ability to work out suitable controlled schemes.
Taking $t$ as the HOR VIT7-THOMPSON estimator $\bar{t}=\sum_{i \in S}$ $Y_{i} / \pi_{i}$, they minimize the objective function $F=\sum_{s \in S_{0}} q(s)$ subject to the linear constraints
\begin{aligned} \sum_{s \ni i, j} q(s) &=\sum_{s \ni i, j} p(s)=\pi_{i j} \ q(s) & \geq 0 \text { for all } s \end{aligned}
where $\pi_{i j}{ }^{\prime} s$ are known quantities in terms of the original uncontrolled design $p$.

## 统计代写|抽样调查作业代写sampling theory of survey代考|SAMPLING SCHEMES

$$\beta_{i 1}=p\left(i_{1}\right), \quad \beta_{i_{1}, i_{2}}=p\left(i_{1}, i_{2}\right), \ldots, \quad \beta_{i_{1}, \ldots, i_{n}}=p\left(i_{1}, \ldots, i_{n}\right) \alpha_{i 1}=\Sigma_{1} p(s), \quad \alpha_{i_{1}, i_{2}}=\Sigma_{2} p(s), \ldots,$$

$$\left(1-\frac{\beta_{i 1}}{\alpha_{i 1}}\right) .$$

$$\alpha_{i_{1}, i_{2}} \alpha_{i 1}-\beta_{i 1}$$

$$\left(1-\frac{\beta_{i_{1}, i_{2}}}{\alpha_{i_{1}, i_{2}}}\right)$$

## 统计代写|抽样调查作业代写sampling theory of survey代考|CONTROLLED SAMPLING

$$E_{p}(t)=E_{q}(t) E_{p}(t-Y)^{2}-E_{q}(t-Y)^{2}$$

$$\sum_{s \ni i, j} q(s)=\sum_{s \ni i, j} p(s)=\pi_{i j} q(s) \quad \geq 0 \text { for all } s$$

## 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|抽样调查作业代写sampling theory of survey代考|MATH525

statistics-lab™ 为您的留学生涯保驾护航 在代写抽样调查sampling theory of survey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。我们的专家在代写抽样调查sampling theory of survey方面经验极为丰富，各种代写抽样调查sampling theory of survey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|抽样调查作业代写sampling theory of survey代考|ELEMENTARY DEFINITIONS

Let $N$ be a known number of units, e.g., godowns, hospitals, or income earners, each assignable identifying labels $1,2, \ldots, N$ and bearing values, respectively, $Y_{1}, Y_{2}, \ldots, Y_{N}$ of a realvalued variable $y$, which are initially unknown to an investigator who intends to estimate the total
$$Y=\sum_{1}^{N} Y_{i}$$
or the mean $\bar{Y}=Y / N$.
We call the sequence $U=(1, \ldots, N)$ of labels a population. Selecting units leads to a sequence $s=\left(i_{1}, \ldots, i_{n}\right)$, which is called a sample. Here $i_{1}, \ldots, i_{n}$ are elements of $U$, not necessarily distinct from one another but the order of its appearance is maintained. We refer to $n=n(s)$ as the size of $s$, while the effective sample size $v(s)=|s|$ is the cardinality of $s$, i.e., the number of distinct units in $s$. Once a specific sample $s$ is chosen we suppose it is possible to ascertain the values $Y_{i_{1}}, \ldots, Y_{i_{n}}$ of $y$ associated with the respective units of $s$. Then $d=\left[\left(i_{1}, Y_{i_{1}}\right), \ldots,\left(i_{n}, Y_{i_{n}}\right)\right] \quad$ or briefly $d=\left[\left(i, Y_{i}\right) \mid i \in s\right]$
constitutes the survey data.
An estimator $t$ is a real-valued function $t(d)$, which is free of $Y_{i}$ for $i \notin s$ but may involve $Y_{i}$ for $i \in s$. Sometimes we will express $t(d)$ alternatively by $t(s, Y)$, where $Y=\left(Y_{1}, \ldots\right.$, $\left.Y_{N}\right)^{\prime} .$

## 统计代写|抽样调查作业代写sampling theory of survey代考|DESIGN-BASED INFERENCE

Let $\Sigma_{1}$ be the sum over samples for which $|t(s, Y)-Y| \geq k>0$ and let $\Sigma_{2}$ be the sum over samples for which $|t(s, Y)-Y|<k$ for a fixed $Y$. Then from
\begin{aligned} M_{p}(t) &=\Sigma_{1} p(s)(t-Y)^{2}+\Sigma_{2} p(s)(t-Y)^{2} \ & \geq k^{2} \operatorname{Prob}[|t(s, Y)-Y| \geq k] \end{aligned}
one derives the Chebyshev inequality:
$$\operatorname{Prob}[|t(s, Y)-Y| \geq k] \leq \frac{M_{p}(t)}{k^{2}} .$$
Hence
$\operatorname{Prob}[t-k \leq Y \leq t+k] \geq 1-\frac{M_{p}(t)}{k^{2}}=1-\frac{1}{k^{2}}\left[V_{p}(t)+B_{p}^{2}(t)\right]$ where $B_{p}(t)=E_{p}(t)-Y$ is the bias of $t$. Writing $\sigma_{p}(t)=$ $\sqrt{V_{p}(t)}$ for the standard error of $t$ and taking $k=3 \sigma_{p}(t)$, it follows that, whatever $Y$ may be, the random interval $t \pm 3 \sigma_{p}(t)$ covers the unknown $Y$ with a probability not less than
$$\frac{8}{9}-\frac{1}{9} \frac{B_{p}^{2}(t)}{V_{p}(t)} .$$
So, to keep this probability high and the length of this covering interval small it is desirable that both $\left|B_{p}(t)\right|$ and $\sigma_{p}(t)$ be small, leading to a small $M_{p}(t)$ as well.

## 统计代写|抽样调查作业代写sampling theory of survey代考|ELEMENTARY DEFINITIONS

$$Y=\sum_{1}^{N} Y_{i}$$

Ibegin{aligned}
$\mathrm{M}{-}{\mathrm{p}}(\mathrm{t}) \&=\mid$ sigma ${1} \mathrm{p}(\mathrm{s})(\mathrm{t} \mathbf{\mathrm { Y }}) \wedge{2}+\backslash \operatorname{sigma}{2} \mathrm{p}(\mathrm{s})(\mathrm{tY}) \wedge{2} \backslash$
\& Igeq $k \wedge{2}$ loperatorname{概率 $}[|t(s, Y)-Y| \backslash g e q ~ k]$
lend{对齐 $}$
onederivestheChebyshevinequality:
loperatorname{概率 $}[|\mathrm{t}(\mathrm{s}, Y)-\mathrm{Y}| \operatorname{lgeq} \mathrm{k}] \backslash \operatorname{leq} \backslash f$ frac $\left{\mathrm{M}{-}{\mathrm{p}}(\mathrm{t})\right}{\mathrm{k} \wedge{2}}$ $\$ \$$因此 \operatorname{Prob}[t-k \leq Y \leq t+k] \geq 1-\frac{M{p}(t)}{k^{2}}=1-\frac{1}{k^{2}}\left[V_{p}(t)+B_{p}^{2}(t)\right] 在哪里 B_{p}(t)=E_{p}(t)-Y 是偏差 t. 写作 \sigma_{p}(t)=\sqrt{V_{p}(t)} 对于标准误 t 并采取 k=3 \sigma_{p}(t), 由此可知，无论 \ צmaybe, therandomintervalt Ipm 3 ปsigma_{p}(t)coverstheunknown 是withaprobabilitynotlessthan \frac{8}{9}-\frac{1}{9} \frac{B_{p}^{2}(t)}{V_{p}(t)}. So, tokeepthisprobabilityhighandthelengthofthiscoveringintervalsmallitisdesirablethatboth Veft \mid \mathrm{B}{-}{\mathrm{p}}(\mathrm{t}) \backslash right \mid and \backslash sigma{p}(t)besmall, leadingtoasmall \mathrm{M}_{-}{\mathrm{p}}(\mathrm{t}) \$$ 也是如此。

## 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|抽样调查作业代写sampling theory of survey代考|STAT 7124

statistics-lab™ 为您的留学生涯保驾护航 在代写抽样调查sampling theory of survey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。我们的专家在代写抽样调查sampling theory of survey方面经验极为丰富，各种代写抽样调查sampling theory of survey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|抽样调查作业代写sampling theory of survey代考|Probability Proportional to Size Without Replacement Sampling

In probability proportional to size WOR (PPSWOR) sampling scheme, probability of selection of $i_{1}$ at the first draw is $p_{i_{1}}(1)=p_{i_{1}}$. Probability of selecting $i_{2}$ at the second draw is $p_{i_{2}}(2)=\frac{p_{i_{2}}}{1-p_{i_{1}}}$ if the unit $i_{1}\left(i_{2} \neq i_{1}\right)$ is selected at the first draw and $p_{i_{2}}(2)=0$ when the unit $i_{2}$ is selected at the first draw, i.e., $i_{2}=i_{1}$. In general, the probability of selection of $i_{k}$ at the $k$ th draw is $p_{i_{k}}(k)=p_{1-p_{i_{1}}-p_{i_{2}}-\cdots-p_{i_{k-1}}}$, if the units $i_{1}, i_{2}, \ldots, i_{k-1}$ are selected in any of the first $k-1$ draws and $p_{i_{k}}(k)=0$ if the unit $i_{k}$ is selected in any of the first $k-1$ draws for $k=2, \ldots, n ; i=1, \ldots, N$. So, for a PPSWOR sampling scheme, the probability of selecting $i_{1}$ at the first draw, $i_{2}$ at the second draw, and $i_{n}$ at the $n$th draw is
\begin{aligned} p\left(i_{1}, \ldots, i_{n}\right)=& p_{i_{1}} \frac{p_{i_{2}}}{1-p_{i_{1}}} \cdots \frac{p_{i_{k}}}{1-p_{i_{1}}-\cdots-p_{i_{k-1}}} \cdots \frac{p_{i_{n}}}{1-p_{i_{1}}-\cdots-p_{i_{n-1}}} \text { for } \ 1 \leq i_{1} \neq i_{2} \neq \cdots \neq i_{n} \leq N \end{aligned}
It should be noted that PPSWOR reduces to SRSWOR sampling scheme if $p_{i}=1 / N$ for $i=1, \ldots, N$.

## 统计代写|抽样调查作业代写sampling theory of survey代考|HANURAV’S ALGORITHM

Hanurav (1966) established a correspondence between a sampling design and a sampling scheme. He proved that any sampling scheme results in a sampling design. Similarly, for a given sampling design, one can construct at least one sampling scheme, which can implement the sampling design. In fact, Hanurav proposed the most general sampling scheme, known as Hanurav’s algorithm, using which one can derive various types of sampling schemes or sampling designs. Henceforth, we will not differentiate between the terms “sampling design” and “sampling scheme”.

Let $n_{0}$ denote the maximum sample size that might be required from a sampling scheme. Then, Hanurav’s (1966) algorithm is defined as follows:
$$\mathscr{A}=\mathscr{A}\left{q_{1}(i) ; q_{2}(s) ; q_{3}(s, i)\right}$$
where
(i) $0 \leq q_{1}(i) \leq 1, \quad \sum_{i=1}^{N} q_{1}(i)=1$ for $i=1, \ldots, N$
(ii) $0 \leq q_{2}(s) \leq 1$ for any sample $s \in \mathscr{S}$, where $\mathscr{\mathcal { S }}$ be the set of all possible samples.
(iii) $q_{3}(s, i)$ is defined when $q_{2}(s)>0$ and subject to $0 \leq q_{3}(s, i) \leq 1$,
$$\sum_{i=1}^{N} q_{3}(s, i)=1 \text { for } i=1, \ldots, N$$
Samples are selected using the following steps:
Step 1: At the first draw a unit $i_{1}$ is selected with probability $q_{1}\left(i_{1}\right)$; $i_{1}=1, \ldots, N$

Step 2: In this step, we decide whether the sampling procedure will be terminated or continued. Let $s_{(1)}=i_{1}$ be the unit selected in the first draw. A Bernoulli trial is performed with success probability $q_{2}\left(s_{(1)}\right)$. If the trial results in a failure, the sampling procedure is terminated and the selected sample is $s_{(1)}=i_{1}$. On the other hand, if the trial results in a success, we go to step 3 .

## 统计代写|抽样调查作业代写sampling theory of survey代考|Probability Proportional to Size Without Replacement Sampling

$$p\left(i_{1}, \ldots, i_{n}\right)=p_{i_{1}} \frac{p_{i_{2}}}{1-p_{i_{1}}} \cdots \frac{p_{i_{k}}}{1-p_{i_{1}}-\cdots-p_{i_{k-1}}} \cdots \frac{p_{i_{n}}}{1-p_{i_{1}}-\cdots-p_{i_{n-1}}} \text { for } 1 \leq i_{1} \neq i_{2} \neq \cdots$$

## 统计代写|抽样调查作业代写sampling theory of survey代考|HANURAV’S ALGORITHM

Hanurav (1966) 建立了抽样设计和抽样方案之间的对应关系。他证明了任何抽样方案都会导致抽样设计。类似 地，对于给定的抽样设计，可以构建至少一种抽样方案，该方案可以实现抽样设计。事实上，Hanurav 提出了最 通用的抽样方案，称为 Hanurav 算法，利用该算法可以推导出各种类型的抽样方案或抽样设计。此后，我们将 不再区分”抽样设计”和”抽样方案”这两个术语。

$\backslash$ mathscr ${\mathrm{A}}=\backslash$ mathscr ${\mathrm{A}} \backslash \operatorname{left}\left{\mathrm{q}{-}{1}(\mathrm{i}) ; \mathrm{q}{-}{2}(\mathrm{s}) ; \mathrm{q}{-}{3}(\mathrm{s}, \mathrm{i}) \backslash\right.$ right $}$ 其中 (i) $0 \leq q{1}(i) \leq 1, \quad \sum_{i=1}^{N} q_{1}(i)=1$ 为了 $i=1, \ldots, N$
(二) $0 \leq q_{2}(s) \leq 1$ 对于任何样品 $s \in \mathscr{S}$ ，在哪里 $\mathcal{S}$ 是所有可能样本的集合。
$\Leftrightarrow q_{3}(s, i)$ 定义为 $q_{2}(s)>0$ 并受 $0 \leq q_{3}(s, i) \leq 1$ ，
$$\sum_{i=1}^{N} q_{3}(s, i)=1 \text { for } i=1, \ldots, N$$

## 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。

## 英国补考|抽样调查作业代写sampling theory of survey代考|MATH 525

statistics-lab™ 为您的留学生涯保驾护航 在代写抽样调查sampling theory of survey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。我们的专家在代写抽样调查sampling theory of survey方面经验极为丰富，各种代写抽样调查sampling theory of survey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

## 英国补考|抽样调查作业代写sampling theory of survey代考|Sampling and Nonsampling Errors

Obviously, using the complete enumeration method, we get the correct value of the parameter, provided all the $\gamma$-values of the population obtained are correct. This would mean that there is no nonresponse, i.e., a response from each unit is obtained, and there is no measurement error in measuring $\gamma$-values. However, in practice, at least for a large-scale survey, nonresponse is unavoidable, and $\gamma$-values are also subject to error because the respondents report untrue values, especially when $\gamma$-values relate to confidential characteristics such as income and age. The error in a survey, which is originated from nonresponse or incorrect measurement of $y$-values, is termed as the nonsampling error. The nonsampling errors increase with the sample size.
From a sample survey, we cannot get the true value of the parameter because we surveyed only a sample, which is just a part of the population. The error committed by making inference by surveying a part of the population is known as the sampling error. In complete enumeration,sampling error is absent, but it is subjected more to nonsampling error than sample surveys. When the population is large, complete enumeration is not possible as it is very expensive, time-consuming, and requires many trained investigators. The advantages of sample surveys over complete enumeration were advocated by Mahalanobis (1946), Cochran (1977), and Murthy (1977), to name a few.

## 英国补考|抽样调查作业代写sampling theory of survey代考|Cumulative Total Method

Here we label all possible samples of $\mathcal{S}$ as $s_{1}, \ldots, s_{i}, \ldots, s_{M}$, where $M=$ total number of samples in $\mathscr{e}$. Then we calculate the cumulative total $T_{i}=p\left(s_{1}\right)+\cdots+p\left(s_{i}\right)$ for $i=1, \ldots, M$ and select a random sample $R$ (say) from a uniform population with range $(0,1)$. This can be done by choosing a five-digit random number and placing a decimal preceding it. The sample $s_{k}$ is selected if $T_{k-1}<R \leq T_{k}$, for $k=1, \ldots, M$ with $T_{0}=0$.

Example $1.4 .1$
Let $U=(1,2,3,4) ; s_{1}=(1,1,2), s_{2}=(1,2,2), s_{3}=(3,2), s_{4}=(4)$; $p\left(s_{1}\right)=0.25, p\left(s_{2}\right)=0.30, p\left(s_{3}\right)=0.20$, and $p\left(s_{4}\right)=0.25$.
$\begin{array}{lllll}s & s_{1} & s_{2} & s_{3} & s_{4} \ p(s) & 0.25 & 0.30 & 0.20 & 0.25 \ T_{k} & 0.25 & 0.55 & 0.75 & 1\end{array}$
Let a random sample $R=0.34802$ be selected from a uniform population with range $(0,1)$. The sample $s_{2}$ is selected as $T_{1}=0.25<R=$ $0.34802 \leq T_{2}=0.55$.

The cumulative total method mentioned above, however, cannot be used in practice because here we have to list all the possible samples having positive probabilities. For example, suppose we need to select a sample of size 15 from a population size $R=30$ following a sampling design, where all possible samples of size $n=15$ have positive probabilities, we need to list $M=\left(\begin{array}{l}30 \ 15\end{array}\right)$ possible samples, which is obviously a huge number.

## 英国补考|抽样调查作业代写sampling theory of survey代考|Cumulative Total Method

$p\left(s_{1}\right)=0.25, p\left(s_{2}\right)=0.30, p\left(s_{3}\right)=0.20$ ，和 $p\left(s_{4}\right)=0.25$.

## 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|抽样调查作业代写sampling theory of survey代考|STAT 506

statistics-lab™ 为您的留学生涯保驾护航 在代写抽样调查sampling theory of survey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。我们的专家在代写抽样调查sampling theory of survey方面经验极为丰富，各种代写抽样调查sampling theory of survey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|抽样调查作业代写sampling theory of survey代考|Preliminaries and Basics of Probability Sampling

Various government organizations, researchers, sociologist, and businesses often conduct surveys to get answers to certain specific questions, which cannot be obtained merely through laboratory experiments or simply using economic, mathematical, or statistical formulation. For example, the knowledge of the proportion of unemployed people, those below poverty line, and the extent of child labor in a certain locality is very important for the formulation of a proper economic planning. To get the answers to such questions, we conduct surveys on sections of people of the locality very often. Surveys should be conducted in such a way that the results of the surveys can be interpreted objectively in terms of probability. Drawing inference about aggregate (population) on the basis of a sample, a part of the populations, is a natural instinct of human beings. Surveys should be conducted in such a way that the inference relating to the population should have some valid statistical background. To achieve valid statistical inferences, one needs to select samples using some suitable sampling procedure. The collected data should be analyzed appropriately. In this book, we have discussed various methods of sample selection procedures, data collection, and methods of data analysis and their applications under various circumstances. The statistical theories behind such procedures have also been studied in great detail.

In this chapter we introduce some of the basic definitions and terminologies in survey sampling such as population, unit, sample, sampling designs, and sampling schemes. Various methods of sample selection as well as Hanurav’s algorithm which gives the correspondence between a sampling design and a sampling scheme have also been discussed.

## 统计代写|抽样调查作业代写sampling theory of survey代考|Parameter and Parameter Space

For a given population $U$, we may be interested in studying certain characteristics of it. Such characteristics are known as study variables. When considering a population of students in a certain class, we may be interested to know the age, height, racial group, economic condition, marks on different subjects, and so forth. Each of the variables under study is called a study variable, and it will be denoted by $\gamma$. Let $\gamma_{i}$ be the value of a study variable $y$ for the $i$ th unit of the population $U$, which is generally not known before the survey. The $N$-dimension vector $\mathbf{y}=\left(\gamma_{1}, \ldots, \gamma_{i}, \ldots, \gamma_{\mathrm{N}}\right)$ is known as a parameter of the population $U$ with respect to the characteristic $\gamma$. The set of all-possible values of the vector $\mathbf{y}$ is the $N$-dimensional Euclidean space $R^{N}=\left(-\infty<y_{1}<\infty, \ldots,-\infty<\gamma_{i}<\infty, \ldots,-\infty<\gamma_{N}<\infty\right)$ and it is known as a parameter space. In most of the cases we are not interested in knowing the parameter $\mathbf{y}$ but in a certain parametric function of $\mathbf{y}$ such as, $Y=\sum_{i=1}^{N} \gamma_{i}=$ population total, $\bar{Y}=\frac{1}{N} \sum_{i=1}^{N} \gamma_{i}=$ population mean, $S_{Y}^{2}=\frac{1}{N-1} \sum_{i=1}^{N}\left(y_{i}-\bar{Y}\right)^{2}=$ population variance, $C_{\gamma}=S_{Y} / \bar{Y}$ $=$ population coefficient of variation, and so forth.

## 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|python代考|Functions

Python是一种高级的、解释性的、通用的编程语言。它的设计理念强调代码的可读性，使用大量的缩进。

Python是动态类型的，并且是垃圾收集的。它支持多种编程范式，包括结构化（特别是程序化）、面向对象和函数式编程。由于其全面的标准库，它经常被描述为一种 “包含电池 “的语言。

statistics-lab™ 为您的留学生涯保驾护航 在代写python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python代写方面经验极为丰富，各种代写python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|python代考|Putting Your Program into Its Own File

Up until this point, any time you wanted to accomplish a task, you have needed to type out entire programs to do the job. If you needed to do the same work again, you could type the entire program again or place it in a loop. However, loops are most useful when you are repeating the same thing, but writing the same loop repeatedly in different parts of your program with slightly modified values in each one is not a sane way to live your life.

Python has functions that enable you to gather sections of code into more convenient groupings that can be called on when you have a need for them.

In this chapter, you will learn how to create and use your own functions. You will be given guidelines to help facilitate your thinking about how to create and structure your programs to use functions. You will also learn to write your functions so that you can later interrogate them for information about how they behave and what you intend for them to do.

As the examples in this book get longer, typing the entire code block begins to be a burden. A single mistake causes you to retype in the entire block of code you are working on. Long before you’ve gotten to the point where you’ve got more than, say, 40 lines of code to type, you are unlikely to want to have to do it more than once.
You are probably already aware that programmers write programs that are saved as source code into files that can be opened, edited, and run without a great deal of work.

To reach this far more convenient state of affairs, from here on out you should type the programs you are using into the main codeEditor window, and save the examples from the book into a single folder from which you can reference them and run them. One suggestion for naming the folder could be “Learning Python, ” and then you could name the programs according to the chapters in which they appear.

## 统计代写|python代考|Grouping Code under a Name

When you invoke ch5. py with just the in_fridge function defined, you won’t see any output. However, the function will be defined, and it can be invoked from the interactive Python session that you’ve created.

To take advantage of the in_fridge function, though, you have to ensure that there is a dictionary called fridge with food names in it. In addition, you have to have a string in the name wanted_food. This string is how you can ask, using in_fridge, whether that food is available. Therefore, from the interactive session, you can do this to use the function:
$>>>$ fridge $=\left{\right.$ ‘apples’ $: 10$, ‘oranges’ $: 3$, ‘milk’ $\left.1 k^{\prime}\right}$
$>>$ wanted_food = ‘apples’
$>>$ in_fridge(l)
10
$>>$ wanted_food = ‘oranges’
$>>>$ in_fridge()
3
$>>>$ wanted_food = ‘milk’
$>>$ in_fridge(1)
2
This is more than just useful – it makes sense and it saves you work. This grouping of blocks of code under the cover of a single name means that you can now simplify your code, which in turn enables you to get more done more quickly. You can type less and worry less about making a mistake as well.

Functions are a core part of any modern programming language, and they are a key part of getting problems solved using Python.
Functions can be thought of as a question and answer process when you write them. When they are invoked, a question is often being asked of them: “how many,” “what time,” “does this exist?” “can this be changed?” and more. In response, functions will often return an answer – a value that will contain an answer, such as True, a sequence, a dictionary, or another type of data. In the absence of any of these, the answer returned is the special value None.
Even when a function is mainly being asked to just get something simple done, there is usually an implied question that you should know to look for. When a function has completed its task, the questions “Did it work?” or “How did it work out?” are usually part of how you invoke the function.

## 统计代写|python代考|Describing a Function in the Function

After you’ve chosen a name for your function, you should also add a description of the function. Python enables you to do this in a way that is simple and makes sense.

If you place a string as the first thing in a function, without referencing a name to the string, Python will store it in the function so you can reference it later. This is commonly called a docstring, which is short for documentation string.
Documentation in the context of a function is anything written that describes the part of the program (the function, in this case) that you’re looking at. It’s famously rare to find computer software that is well documented. However, the simplicity of the docstring feature in Python makes it so that, generally, much more information is available inside Python programs than in programs written in other languages that lack this friendly and helpful convention.
The text inside the docstring doesn’t necessarily have to obey the indentation rules that the rest of the source code does, because it’s only a string. Even though it may visually interrupt the indentation, it’s important to remember that, when you’ve finished typing in your docstring, the remainder of your functions must still be correctly indented.
def in_fridge (\rangle :
*”This is a function to see if the fridge has a food.
fridge has to be a dictionary defined outside of the function.
the food to be searched for is in the string wanted_food” *
try:
count = fridge[wanted_food]
def in_fridge ():
” “This is a function to see if
the food to be searched for is in the
try:
count = fridge[wanted_food]
except KeyError:
count = 0
return count
except KeyError:
count $=0$
return count
The docstring is referenced through a name that is part of the function, almost as though the function were a dictionary. This name is doc and it’s found by following the function name with a period and the name _ doc..

## 统计代写|python代考|Putting Your Program into Its Own File

Python 具有使您能够将代码段收集到更方便的分组中的功能，这些分组可以在您需要时调用。

## 统计代写|python代考|Grouping Code under a Name

>>>冰箱=\left{\right.$‘苹果’$: 10$, ‘橙子’$: 3$, ‘牛奶’$\left.1 k^{\prime}\right}=\left{\right.$‘苹果’$: 10$, ‘橙子’$: 3$, ‘牛奶’$\left.1 k^{\prime}\right}
>>Wanted_food = ‘苹果’
>>in_fridge(l)
10
>>Wanted_food = ‘橘子’
>>>in_fridge()
3
>>>Wanted_food = ‘牛奶’
>>in_fridge(1)
2

## 统计代写|python代考|Describing a Function in the Function

def in_fridge (\rangle :
*”这是一个查看冰箱是否有食物的函数。

try:
count =冰箱[wanted_food]
def in_fridge ():
” “这是一个查看

try中的函数：
count =冰箱[wanted_food]

count = 0

count=0
return count

## 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|python代考|Making Decisions

Python是一种高级的、解释性的、通用的编程语言。它的设计理念强调代码的可读性，使用大量的缩进。

Python是动态类型的，并且是垃圾收集的。它支持多种编程范式，包括结构化（特别是程序化）、面向对象和函数式编程。由于其全面的标准库，它经常被描述为一种 “包含电池 “的语言。

statistics-lab™ 为您的留学生涯保驾护航 在代写python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python代写方面经验极为丰富，各种代写python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|python代考|Making Decisions

So far, you have only seen how to manipulate data directly or through names to which the data is bound. Now that you have the basic understanding of how those data types can be manipulated manually, you can begin to exercise your knowledge of data types and use your data to make decisions.
In this chapter, you’ll learn about how Python makes decisions using True and False and how to make more complex decisions based on whether a condition is True or False.
You will learn how to create situations in which you can repeat the same actions using loops that give you the capability to automate stepping through lists, tuples, and dictionaries. You’ll also learn how to use lists or tuples with dictionaries cooperatively to explore the contents of a dictionary.

You will also be introduced to exception handling, which enables you to write your programs to cope with problematic situations that you can handle within the program.

## 统计代写|python代考|Comparing Values — Are They the Same

When you use the equality comparison, Python will compare the values on both sides. If the numbers are different, False will be the result. If the numbers are the same, then True will be the result.
If you have different types of numbers, Python will still be able to compare them and give you the correct answer:
$x>1.23==1$
False
$>>>1.0==1$
True
You can also use the double equals to test whether strings have the same contents, and you can even restrict this test to ranges within the strings (remember from the last chapter that slices create copies of the part of the strings they reference, so you’re really comparing two strings that represent just the range that a slice covers):
$>>>a=$ “Mackintosh apples”
$\rightarrow>>b=$ “Black Berries”
$>>c=$ “Golden Delicious apples”
$>>>a==b$
False
$x>b=a c$
False
$>>>$ a $[-1$ en $($ “apples “):-1] == c[-len $($ “apples ” $):-1]$
True
Sequences can be compared in Python with the double equals as well. Python considers two sequences to be equal when every element in the same position is the same in each list. Therefore, if you have three items each in two sequences and they contain the same data but in a different order, they are not equal:
$>>>$ apples $=[$ “Mackintosh”, “Golden Delicious”, “Fuji “, “Mitsu”]

apple_trees = [“Golden Delicious”, “Fuji”, “Mitsu”, “Mackintosh”]
$>>>$ apples = apple_trees
False
$>>>$ apple_trees = [“Mackintosh”, “Golden Delicious”, “Fuji ” “Mitsu*]
$>>$ apples = = apple_trees
True
In addition, dictionaries can be compared. Like lists, every key and value (paired, together) in one dictionary has to have a key and value in the other dictionary in which the key in the first is equal to the key in the second, and the value in the first is equal to the value in the second:
$>>>$ tuesday_breakfast_sold $=$ “pancakes” :10, ” french toast”: 4 , “bagels”:32,
“omelets”:12, “eggs and sausages” :131
$>>>$ wednesday_breakfast_sold $=$ “pancakes” $: 8$, “french toast” : 5 , “bagels” $: 22$,
“omelets”:16, “eggs and sausages” :22}
$>>$ tuesday_breakfast_sold $==$ wednesday_breakfast_sold
False
$>>>$ thursday_breakfast_sold = {“pancakes” :10, “french toast” $: 4$, “bagels” $: 32$,
“omelets” $: 12$, “eggs and sausages” :13)
tuesday_breakfast_sold == thursday_breakfast_sold
True

## 统计代写|python代考|Comparing Values — Which One Is More

The number on the left is compared to the number on the right. You can compare letters, too. There are a few conditions where this might not do what you expect, such as trying to compare letters to numbers. (The question just doesn’t come up in many cases, so what you expect and what Python expects is

probably not the same.) The values of the letters in the alphabet run roughly this way: A capital “A” is the lowest letter. “B” is the next, followed by ” $\mathrm{C}^{\prime \prime}$, and so on until ” $\mathrm{Z}$.” This is followed by the lowercase letters, with “a” being the lowest lowercase letter and ” $\mathrm{z}^{\prime \prime}$ the highest. However, “a” is higher than “Z”:
$>>>” a “>” b$ “
False
$>>$ “A” $^{\prime \prime} \mathrm{b}$ “
False
$>>>^{\prime A} |^{\prime \prime} a$ “
False
$>>>” b “>\mathrm{A}^{n}$
True
$>>$ ” $^{\prime \prime}>” a$ “
False
If you wanted to compare two strings that were longer than a single character, Python will look at each letter until it finds one that’s different. When that happens, the outcome will depend on that one difference. If the strings are completely different, the first letter will decide the outcome:
$>>>$ “Zebra” > “aardvark”
False
$>>>$ “Zebra” > “Zebrb”
False
$x>>$ “Zebra” $<$ “Zebrb* True You can avoid the problem of trying to compare two words that are similar but have differences in capitalization by using a special method of strings called lower, which acts on its string and return a new string with all lowercase letters. There is also a corresponding upper method. These are available for every string in Python: $\Rightarrow>>$ “Pumpkin” $==$ “pumpkin”
False
$>>>$ “Pumpkin”. lower( ()$==$ “pumpkin”. lower()
True
s $>$ “Pumpkin”. lower()
‘pumpkin’
$>>>$ “Pumpkin”. upper() == “pumpkin”. upper()
True
$>>>$ “pumpkin”. upper()
$>>>$ “Pumpkin” == “pumpkin”
False
$>>>$ “Pumpkin”. 1ower() == “pumpkin”. lower()
True
$>>>$ “Pumpkin”. 1ower()
‘pumpkin’
$>>>$ “Pumpkin” , upper() == “pumpkin” upper()
True
$>>>$ “pumpkin”. upper()
‘ PUMPKIN’
‘PUMPKIN’ ‘
When you have a string referenced by a name, you can still access all of the methods that strings normally have:
$>>>$ gourd $=$ “Calabash”
$>>>$ gourd
‘Calabash’
$>>>$ gourd = “Calabash”
$>>>$ gourd
‘Calabash’
$>>>$ gourd. lower()
‘calabash’
$>>$ gourd. upper()
‘CALABASH’
$>>>$ gourd. Lower()
‘calabash’
$>>>$ gourd. upper()
‘CALABASH’

## 统计代写|python代考|Comparing Values — Are They the Same

X>1.23==1

>>>1.0==1
True

>>>一种=“麦金托什苹果”
→>>b=“黑莓”
>>C=“金冠苹果”
>>>一种==b

X>b=一种C

>>>一种[−1在(“苹果”):-1] == c[-len(“苹果 ”):−1]

>>>苹果=[“麦金托什”、“金冠”、“富士”、“美津”]

apple_trees = [“金冠”、“富士”、“美津”、“麦金托什”]
>>>苹果 = apple_trees

>>>apple_trees = [“麦金托什”、“金冠”、“富士”“美津*]
>>apples = = apple_trees
True

>>>tuesday_breakfast_sold=“煎饼”：10，“法式吐司”：4，“百吉饼”：32，
“煎蛋卷”：12，“鸡蛋和香肠”：131
>>>星期三_早餐_已售出=“薄煎饼”:8, “法式吐司” : 5 , “百吉饼”:22，
“煎蛋卷”：16，“鸡蛋和香肠”：22}
>>tuesday_breakfast_sold==wednesday_breakfast_sold

>>>thursday_breakfast_sold = {“煎饼”：10，“法式吐司”:4， “贝果”:32,
“煎蛋卷”:12, “鸡蛋和香肠” :13)
tuesday_breakfast_sold == thursday_breakfast_sold
True

## 统计代写|python代考|Comparing Values — Which One Is More

>>>”一种“>”b“

>>“一种”′′b“

>>>′一种|′′一种“

>>>”b“>一种n

>> ” ′′>”一种“
False

>>>“斑马”>“土豚”

>>>“斑马”>“斑马”

X>>“斑马”<“Zebrb* True 您可以避免尝试比较两个相似但大小写不同的单词的问题，方法是使用一种称为 lower 的特殊字符串方法，该方法作用于其字符串并返回一个全小写字母的新字符串。还有对应的upper方法。这些可用于 Python 中的每个字符串：⇒>>“南瓜”==“南瓜”

>>>“南瓜”。降低（ （）==“南瓜”。lower( )

>“南瓜”。lower()
‘南瓜’
>>>“南瓜”。上（）==“南瓜”。上（）

>>>“南瓜”。上（）
>>>“南瓜” == “南瓜”

>>>“南瓜”。1ower() == “南瓜”。较低（）

>>>“南瓜”。1ower()
‘南瓜’
>>>“南瓜”，上（）==“南瓜”上（）

>>>“南瓜”。upper()
‘ PUMPKIN’
‘PUMPKIN’ ‘

>>>葫芦=“蠡”
>>>葫芦
‘葫芦’
>>>葫芦=“葫芦”
>>>葫芦
‘葫芦’
>>>葫芦。lower()
‘葫芦’
>>葫芦。上（）
‘葫芦’
>>>葫芦。Lower()
‘葫芦’
>>>葫芦。上（）
‘葫芦’

## 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|python代考|Growing Lists by Appending Sequences

Python是一种高级的、解释性的、通用的编程语言。它的设计理念强调代码的可读性，使用大量的缩进。

Python是动态类型的，并且是垃圾收集的。它支持多种编程范式，包括结构化（特别是程序化）、面向对象和函数式编程。由于其全面的标准库，它经常被描述为一种 “包含电池 “的语言。

statistics-lab™ 为您的留学生涯保驾护航 在代写python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python代写方面经验极为丰富，各种代写python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|python代考|Growing Lists by Appending Sequences

Suppose you have two lists that you want to join together. You haven’t been shown a purposely built way to do that yet. You can’t use append to take one sequence and add it to another. Instead, you will find that you have layered a sequence into your list:
$>>>$ living_room $=($ “rug”, “table”, “chair”, “TV”, “dustbin”, “shelf”)

apartment $=[1]$
$>>>$ apartment.append(living_room)
$>>$ apartment
[(‘rug’, ‘table’, ‘chair’, ‘TV’, ‘dustbin’, ‘shelf’)]
This is probably not what you want if you were intending to create a list from the contents of the tuple living_room that could be used to create a list of all the items in the apartment.

To copy all of the elements of a sequence, instead of using append, you can use the extend method of lists and tuples, which takes each element of the sequence you give it and inserts those elements into the list from which it is called:
$>>>$ apartment $=[1]$

apartment.extend(1iving_room)
$>>$ apartment = []
$>>$ apartment. extend(living_room)
$>>$ apartment
[‘rug’, ‘table’, ‘chair’, ‘TV’, ‘dustbin’, ‘shelf’]
$>>>$ apartment
[‘rug’, ‘table’, ‘chair’, ‘TV’, ‘dustbin’, ‘shelf’]

## 统计代写|python代考|Using Lists to Temporarily Store Data

You’ll often want to acquire data from another source, such as a user entering data or another computer whose information you need. To do that, it is best to put this data in a list so that it can be processed later in the same order in which it arrived.

However, after you’ve processed the data, you no longer need it to be in the list, because you won’t need it again. Temporal (time-specific) information such as stock tickers, weather reports, or news headlines would be in this category.

To keep your lists from becoming unwieldy, a method called pop enables you to remove a specific reference to data from the list after you’re done with it. When you’ve removed the reference, the position it occupied will be filled with whatever the next element was, and the list will be reduced by as many elements as you’ve popped.

## 统计代写|python代考|Popping Elements from a List

When a value is popped, if the action is on the right-hand side of an equals sign, you can assign the element that was removed to a value on the left-hand side, or just use that value in cases where it would be appropriate. If you don’t assign the popped value or otherwise use it, it will be discarded from the list.
You can also avoid the use of an intermediate name, by just using pop to populate, say, a string format, because pop will return the specified element in the list, which can be used just as though you’d specified a number or a name that referenced a number:

print “Afternoon temperature was 호. $02 \mathrm{f}$ ” 와 todays_temperatures.pop(0)
Afternoon temperature was $31.00$
print “Afternoon temperature was $8.02 \mathrm{f}$ ” of todays
Afternoon temperature was $31.00$
$>>>$ todays_temperatures
${[29] }$
$\rightarrow>$ todays_temperatures
[29]
If you don’t tell pop to use a specific element ( 0 in the examples) from the list it’s invoked from, it will remove the last element of the list, not the first as shown here.

In this chapter, you learned how to manipulate many core types that Python offers. These types are tuples, lists, dictionaries, and three special types: None, True, and False. You’ve also learned a special way that strings can be treated like a sequence. The other sequence types are tuples and lists.

A tuple is a sequence of data that’s indexed in a fixed numeric order, starting at zero. The references in the tuple can’t be changed after the tuple is created. Nor can it have elements added or deleted. However, if a tuple contains a data type that has changeable elements, such as a list, the elements of that data type are not prevented from changing. Tuples are useful when the data in the sequence is better off not changing, such as when you want to explicitly prevent data from being accidentally changed.

A list is another type of sequence, which is similar to a tuple except that its elements can be modified. The length of the list can be modified to accommodate elements being added using the append method, and the length can be reduced by using the pop method. If you have a sequence whose data you want to append to a list, you can append it all at once with the extend method of a list.

## 统计代写|python代考|Growing Lists by Appending Sequences

>>>客厅=(“地毯”、“桌子”、“椅子”、“电视”、“垃圾箱”、“架子”）

>>>公寓。附加（客厅）
>>apartment
[(‘rug’, ‘table’, ‘chair’, ‘TV’, ‘dustbin’, ‘shelf’)]

>>>公寓=[1]

>>公寓 = []
>>公寓。扩展（客厅）
>>公寓
[‘地毯’, ‘桌子’, ‘椅子’, ‘电视’, ‘垃圾箱’, ‘架子’]
>>>公寓
[‘地毯’, ‘桌子’, ‘椅子’, ‘电视’, ‘垃圾箱’, ‘架子’]

## 统计代写|python代考|Popping Elements from a List

>>>今天_温度
[29]
→>todays_temperatures
[29]

## 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|python代考|Treating a String Like a List

Python是一种高级的、解释性的、通用的编程语言。它的设计理念强调代码的可读性，使用大量的缩进。

Python是动态类型的，并且是垃圾收集的。它支持多种编程范式，包括结构化（特别是程序化）、面向对象和函数式编程。由于其全面的标准库，它经常被描述为一种 “包含电池 “的语言。

statistics-lab™ 为您的留学生涯保驾护航 在代写python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python代写方面经验极为丰富，各种代写python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|python代考|Treating a String Like a List

Python offers an interesting feature of strings. Sometimes, it is useful to be able to treat a string as though it were a list of individual characters. It’s not uncommon to have extraneous characters at the end of a string. People may not recognize these, but computers will get hung up on them. It’s also common to only need to look at the first character of a string to know what you want to do with it. For instance, if you had a list of last names and first names, you could view the first letter of each by using the same syntax that you would for a list. This method of looking at strings is called slicing and is one of the fun things about Python:

$\Rightarrow>$ last_names = [ “Douglass”, “Jefferson”, “Williams”, “Frank”, “Thomas” ]

print “ofs” of last_names[0]
Douglass
$>>$ print “ofs” 항 last_names[0][0]
D
print ” o \$s” of last_names [1] Jefferson$>>>$print “o्os” of last_names [1] [0]$\Rightarrow>$print “ơos” 항 last_names [2] Williams print “o्षs” of last_names[2][0]$\mathrm{W}$print “o्8s” of last_names [3] Frank$>>>$print “o्ㅎ” 뭉 last_names [3] [0]$\mathrm{F}$print “o 와” 왕 last_names [4] Thomas print “oेs” of last_names[4][0]$\mathrm{T}$For example, you can use the letter positioning of strings to arrange them into groups in a dictionary based on the first letter of the last name. You don’t need to do anything complicated; you can just check to see which letter the string containing the name starts with and file it under that:$>>$by_letter$={1>>$by_letter[last_names [0] [0]] = last_names [0]$>>$by_letter [last_names [1][0]] = last_names [1]$>>$by_letter[last_names [2][0]] = last_names [2]$>>>$by_letter[1ast_names [3] [0]] = last_names [3]$>>$by_letter [last_names [5] [0]$=$last_names [5] The by_letter dictionary will, thanks to string slicing, only contain the first letter from each of the last names. Therefore, by_letter is a dictionary indexed by the first letter of each last name. You could also make each key in by_letter reference a list instead and use the append method of that list to create a list of names beginning with that letter (if, of course, you wanted to have a dictionary that indexed a larger group of names, where each one did not begin with a different letter). Remember that, like tuples, strings are immutable. When you are slicing strings, you are actually creating new strings that are copies of sections of the original string. ## 统计代写|python代考|Special Types There are a handful of special types in Python. You’ve seen them all, but they bear mentioning on their own: None, True, and False are all special built-in values that are useful at different times. None is special because there is only one None. It’s a name that no matter how many times you use it, it doesn’t match any other object, just itself. When you use functions that don’t have anything to return to you – that is, when the function doesn’t have anything to respond with – it will return None. True and False are special representations of the numbers 1 and 0 . This prevents a lot of the confusion that is common in other programming languages where the truth value of a statement is arbitrary. For instance, in a Unix shell (shell is both how you interact with the system, as well as a programming language), 0 is true and anything else is false. With$\mathrm{C}$and Perl, 0 is false and anything else is true. However, in all of these cases, there are no built-in names to distinguish these values. Python makes this easier by explicitly naming the values. The names True and False can be used in elementary comparisons, which you’ll see a lot; and in Chapter 4, you will learn how these comparisons can dramatically affect your programs – in fact, they enable you to make decisions within your program.$>>>$True True$\rightarrow>$False False$\Rightarrow>$True$==1$True$>>>$True True$>>>$False False$>>>$True$==1$True$>>>$True$==0$False$>>>$False$==1$False$>>>$False$==0$True$\Rightarrow>$True$==0$False ) False$==1$False$>>\Rightarrow$False$==0$True ## 统计代写|python代考|Referencing the Last Elements All of the sequence types provide you with some shortcuts to make their use more convenient. You often need to know the contents of the final element of a sequence, and you can get that information in two ways. One way is to get the number of elements in the list and then use that number to directly access the value there:$>>$last_names = [ “Douglass”, “Jefferson”, “Williams”, “Frank”, “Thomas” ]$>>>$len (last_names) 5$>>$last_element = 1en (last_names) – 1$\rightarrow>$print “o्8s” 훟 last_names[last_element] Thomas However, that method takes two steps; and as a programmer, typing it repeatedly in a program can be time-consuming. Fortunately, Python provides a shortcut that enables you to access the last element of a sequence by using the number$-1$, and the next-to-last element with$-2$, letting you reverse the order of the list by using negative numbers from$-1$to the number that is the negative length of the list (-5 in the case of the last_names list).$>>$print “ofo” of last_names$[-1]$Thomas$>>>$print “ofos” 핳 last_names$[-2]$Frank$>>$print “o्षेs” 형 last_names$[-3]$Williams ## python代写 ## 统计代写|python代考|Treating a String Like a List Python 提供了一个有趣的字符串特性。有时，能够将字符串视为单个字符的列表很有用。在字符串的末尾有多余的字符并不少见。人们可能无法识别这些，但计算机会挂断它们。只需要查看字符串的第一个字符就知道你想用它做什么也是很常见的。例如，如果您有一个姓氏和名字的列表，您可以使用与列表相同的语法来查看每个名字的第一个字母。这种查看字符串的方法称为切片，是 Python 的有趣之处之一： ⇒>last_names = [“道格拉斯”、“杰斐逊”、“威廉姆斯”、“弗兰克”、“托马斯”] 打印 last_names[0] Douglass的“ofs” >>打印“ofs” 항 last_names[0][0] D 打印“o$ s” of last_names [1]
Jefferson
>>>打印姓氏 [1] [0] 的“o्os”
⇒>打印“ơos” 항 last_names [2]

Frank
>>>打印“o्ha” Mung last_names [3] [0]
F

Thomas

>>by_letter $={1>>b是l和吨吨和r[l一种s吨n一种米和s[0][0]]=l一种s吨n一种米和s[0]>>b是l和吨吨和r[l一种s吨n一种米和s[1][0]]=l一种s吨n一种米和s[1]>>b是l和吨吨和r[l一种s吨n一种米和s[2][0]]=l一种s吨n一种米和s[2]>>>b是l和吨吨和r[1一种s吨n一种米和s[3][0]]=l一种s吨n一种米和s[3]>>b是l和吨吨和r[l一种s吨n一种米和s[5][0]=$ last_names [5]

## 统计代写|python代考|Special Types

Python中有一些特殊类型。您已经看到了它们，但它们本身值得一提：None、True 和 False 都是在不同时间有用的特殊内置值。

None 是特殊的，因为只有一个 None。这是一个名称，无论你使用多少次，它都不匹配任何其他对象，只匹配它自己。当你使用没有任何东西可以返回给你的函数时——也就是说，当函数没有任何东西可以响应时——它会返回 None。

True 和 False 是数字 1 和 0 的特殊表示。这避免了其他编程语言中常见的许多混淆，其中语句的真值是任意的。例如，在 Unix shell 中（shell 既是你与系统交互的方式，也是一种编程语言），0 为真，其他任何东西都是假的。和C和 Perl，0 是假的，其他的都是真的。但是，在所有这些情况下，都没有内置名称来区分这些值。Python 通过显式命名值使这更容易。名称 True 和 False 可用于基本比较，您会看到很多；在第 4 章中，您将了解这些比较如何极大地影响您的程序——事实上，它们使您能够在程序中做出决定。
>>>真真
_
→>假

⇒>真的==1

>>>真真
_
>>>假

>>>真的==1

>>>真的==0

>>>错误的==1

>>>错误的==0

⇒>真的==0

） 错误的==1

>>⇒错误的==0

## 统计代写|python代考|Referencing the Last Elements

>>last_names = [“道格拉斯”、“杰斐逊”、“威廉姆斯”、“弗兰克”、“托马斯”]
>>>len (last_names)
5
>>last_element = 1en (last_names) – 1
→>print “o्8s” 훟 last_names[last_element]
Thomas

>>打印姓氏的“ofo”[−1]

>>>打印“ofos” 핳 last_names[−2]

>>打印“o्षेs”형 last_names[−3]

## 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。