# 统计代写|假设检验代写hypothesis testing代考|STA2023

#### Doug I. Jones

Lorem ipsum dolor sit amet, cons the all tetur adiscing elit

couryes-lab™ 为您的留学生涯保驾护航 在假设检验hypothesis testing作业代写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。我们的专家在假设检验hypothesis testing代写方面经验极为丰富，各种假设检验hypothesis testing相关的作业也就用不着 说。

• 时间序列分析Time-Series Analysis
• 马尔科夫过程 Markov process
• 随机最优控制stochastic optimal control
• 粒子滤波 Particle Filter
• 采样理论 sampling theory
couryes™为您提供可以保分的包课服务

## 统计代写|假设检验代写hypothesis testing代考|Applications of Hypothesis Testing for Environmental Science

The general procedure for conducting hypothesis testing can be used to make the decision regarding the proportion of students who have low lab skills.
Step 1: Specify the null and alternative hypotheses
The proportion of students who have low lab skills $(p)$ is less than or equal to $0.03$ as announced by the department which represents the population proportion. This claim should be under the null hypothesis because the claim includes equality $(=)$. If the proportion of student with low skills is not less than or equal to $0.03$, then the proportion is greater than $0.03$, this direction (greater than) can be represented mathematically as $>$ and placed under the alternative hypothesis. Thus, we can write the two hypotheses (null and alternative) as presented in Eq. (4.6).
$$H_0: p \leq 0.03 \text { vs } H_1: p>0.03$$
We should make a decision regarding the null hypothesis as to whether the proportion of students who have low lab skills is less than or equal to $0.03$, or the proportion is greater than $0.03$.
Step 2: Select the significance level $(\alpha)$ for the study
The significance level of $0.01 \quad(\alpha=0.01)$ is selected to test the hypothesis. Because the test is a one-tailed test as presented by the alternative hypothesis (greater than), we should represent the rejection region on the right tail of the standard normal curve. The $\mathrm{Z}$ critical value for a one-tailed test with $\alpha=0.01$ is $2.326$, as appeared in the standard normal table (Table A in the Appendix). Thus, the $\mathrm{Z}$ critical value for the right-tailed test is $2.326$, and we use the $\mathrm{Z}$ critical value to decide whether to reject or fail to reject the null hypothesis.
Step 3: Use the sample information to calculate the test statistic value
The number of students that have low skills is seven, and the students that have high skills is $200-7=193$. Thus, the proportion of students with low lab skills is:
$$\hat{p}=\frac{Y}{n}=\frac{7}{200}=0.035=0.04$$
$4 \%$ of the students have low lab skills and $0.97$ have high lab skills.
The proportion claimed by the department regarding the students with low lab skills is $p=0.03$ and the proportion of students with high lab skills is:
$$q=\frac{n-Y}{n}=1-p=1-0.03=0.97$$
The entries for the Z-test statistic formula as presented in Eq. (4.4) are already provided, The announced proportion of students with low lab skills $(p)$ is $0.03$, the proportion of students with high lab skills is $(q) 0.97$, the proportion of students with low lab skills calculated from the sample data is $0.02$, and the sample size $(n)$ is 200 .

## 统计代写|假设检验代写hypothesis testing代考|What is chi-square distribution

A chi-square distribution $\left(\chi^2\right)$ (pronounced “chi” as “kai”) is a statistical distribution that is used in many situations to test various hypotheses such as goodness-offit test, independence test, and hypothesis testing for population variance or standard deviation. This distribution belongs to the same family as $t$ distribution which depends on the concept of degrees of freedom.

Suppose $Y>0$ is a random variable that follows a chi-square distribution with $n$ degrees of freedom $(d . f) ; Y \sim \chi^2(n)$. A chi-square curve for various degrees of freedom $(1,4,7,10,17)$ is presented in Fig. $5.1$.
A chi-square distribution has the following properties:

1. A chi-square variable is always nonnegative, $Y>0$, and the shape of the curve tends to be skewed to the right.
2. The mean and variance for a chi-square distribution are $\mu=n$ and $\sigma^2=2 n$, respectively.
3. The number of degrees of freedom influences the shape of the distribution, the chi-square curve approaches a normal distribution with a large number of degrees of freedom.

Critical values for chi-square distribution are usually used to help and guide researchers making a decision about a hypothesis of interest. We can obtain chisquare critical values for various degrees of freedom $(d . f)$ and the level of significance $(\alpha)$ from Table $\mathrm{C}$ in the Appendix. Thus we need to prepare two values to use a chi-square table and obtain the required critical value; the two values are the degrees of freedom and the significance level. The first column on the left of Table $\mathrm{C}$ represents the degrees of freedom $(d . f)$ from 1 to $\infty$, while the first upper row represents the level of significance $(\alpha)$.

Example 5.1: Finding the critical value for the left-tailed chi-square test: Use a significance level of $0.01(\alpha=0.01)$ and degrees of freedom of $11(d . f=11)$ to obtain the chi-square critical value for the left-tailed chi-square test.

We can obtain the chi-square critical value for the left-tailed test as long as two values are provided, the two values are the degrees of freedom ” $d . f=11$ ” and significance level ” $\alpha=0.01$.”

• The first step in extracting the chi-square critical value is to specify the position of $” d . f=11$ ” in the first column of T’able $\mathrm{C}$, labeled $d . f$. Table $5.1$ is a portion of Table $\mathrm{C}$ in the Appendix.
• The second step is to specify the position of $1-\alpha=0.99$ (because the area under the chi-square curve is to the right of $\chi^2$ and the required value is to the left side, thus the required area equals to $1-\alpha$ ) in the first upper row (highlighted column) and then move on the column to the row labeled $d . f=11$ (highlighted row), the value that represents the point of intersection between $d . f=11$ and $1-\alpha=0.99$ is the $\chi^2$ critical value. One can observe that the $\chi_L^2$ critical value is $3.053$ as shown in Table $5.1$ (bold value).

# 假设检验代写

## 统计代写|假设检验代写假设检验代考|假设检验在环境科学中的应用

$$H_0: p \leq 0.03 \text { vs } H_1: p>0.03$$关于实验技能低的学生的比例是否小于或等于，我们应该根据零假设做出决定 $0.03$，或比例大于 $0.03$.

$$\hat{p}=\frac{Y}{n}=\frac{7}{200}=0.035=0.04$$
$4 \%$ 实验技能较低的学生 $0.97$ 具有较高的实验室技能。

$$q=\frac{n-Y}{n}=1-p=1-0.03=0.97$$

## 统计代写|假设检验代写假设检验代考|什么是卡方分布

1. 卡方变量总是非负的$Y>0$，并且曲线的形状倾向于向右倾斜。卡方分布的均值和方差分别为$\mu=n$和$\sigma^2=2 n$。
2. 自由度的个数影响分布的形状，当自由度较多时，卡方曲线趋于正态分布 卡方分布的临界值通常用于帮助和指导研究人员对感兴趣的假设做出决策。我们可以从附录中的表$\mathrm{C}$中获得各种自由度的chisquare临界值$(d . f)$和显著性水平$(\alpha)$。因此，我们需要准备两个值来使用卡方表，并获得所需的临界值;这两个值分别是自由度和显著性水平。表$\mathrm{C}$左侧第一列表示从1到$\infty$的自由度$(d . f)$，上面第一行表示显著性水平$(\alpha)$。
例5.1:寻找左尾卡方检验的临界值:使用显著性水平$0.01(\alpha=0.01)$和自由度$11(d . f=11)$来获得左尾卡方检验的卡方临界值我们可以得到左尾检验的卡方临界值，只要提供两个值，这两个值是自由度。 $d . f=11$ “和显著性水平” $\alpha=0.01$提取卡方临界值的第一步是指定的位置 $” d . f=11$ 在《T’able》第一栏 $\mathrm{C}$, labeled $d . f$。表格 $5.1$ 是Table的一部分 $\mathrm{C}$ 在附录中。第二步是指定的位置 $1-\alpha=0.99$ (因为卡方曲线下的面积在的右边 $\chi^2$ 所需的值在左边，因此所需的面积等于 $1-\alpha$ )，然后在上一行(高亮显示的列)上移动到标记的行 $d . f=11$ (突出显示的行)，表示之间的交点的值 $d . f=11$ 和 $1-\alpha=0.99$ 是 $\chi^2$ 临界值。人们可以观察到 $\chi_L^2$ 临界值为 $3.053$ 如表所示 $5.1$

## 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。

Days
Hours
Minutes
Seconds

# 15% OFF

## On All Tickets

Don’t hesitate and buy tickets today – All tickets are at a special price until 15.08.2021. Hope to see you there :)