### 统计代写|回归分析作业代写Regression Analysis代考| Estimating an ESF as an OLS problem

statistics-lab™ 为您的留学生涯保驾护航 在代写回归分析Regression Analysis方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写回归分析Regression Analysis代写方面经验极为丰富，各种代写回归分析Regression Analysis相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 统计代写|回归分析作业代写Regression Analysis代考|An illustrative linear regression example

A pure SA analysis ignores covariates and estimates the SA latent in an attribute variable map pattern. This type of analysis is pertinent to, for example, the construction of histograms for georeferenced RVs or the calculation of Pearson product moment correlation coefficients for pairs of georeferenced RVs, among other things. The MESF linear regression equation, which assumes normally distributed residuals, is the following nonconstant mean-only specification:
$$\mathbf{Y}=\mathbf{1} \boldsymbol{\beta}{0}+\mathrm{E}{\mathrm{K}} \boldsymbol{\beta}_{\mathrm{E}}+\boldsymbol{\xi} .$$
One traditional specification error concern here pertains to how closely the response variable Y conforms to a normal distribution. Analysts frequently subject a nonnormal set of attribute values to a $B o x-\operatorname{Cox} /$ Manly transformation to normality (see Griffith, 2013).

Consider the 2010 population density (PD) across the 254 counties of Texas (see Fig. 3.1A); urban areas are conspicuous in this map pattern, revealing geographic heterogeneity. Raw PD values do not conform closely to a bell-shaped curve (Fig. 3.2A), whereas Box-Cox transformed values LN $(\mathrm{PD}-0.08)$ do (Fig. 3.2B), where LN denotes natural logarithm.

## 统计代写|回归分析作业代写Regression Analysis代考|The selection of eigenvectors to construct an ESF

The first step in constructing an ESF for the 2010 Texas PD by county is to extract the 254 eigenvectors from the modified SWM $\left(\mathbf{I}-11^{\mathrm{T}} / 254\right) \times$ $\mathbf{C}\left(\mathbf{I}-11^{\mathrm{T}} / 254\right)$, where $0-1$ matrix $\mathrm{C}$ denotes the Texas county SWM, based upon the rook definition of adjacency (see Preface, Fig. P1). Because this PD exhibits PSA, determining an appropriate candidate set of eigenvectors for stepwise regression can begin by setting aside the $149 \mathrm{NSA}$ eigenvectors plus the single eigenvector having a zero eigenvalue (corresponding to the eigenvector proportional to the vector 1 ), which a regression equation already includes for its intercept term. The next step is to determine how many of the 104 PSA eigenvectors to include, counting this number from the largest eigenvalue (i.e., the maximum possible PSA). Chun et al. (2016, p. 75) furnish the following equation to help with this decision:
$$1+\exp \left{2.1480-\frac{6.1808\left(\mathrm{z}{\mathrm{MC}}+0.6\right)^{0.1742}}{\mathrm{n}{\text {pos }}^{0.1298}}+\frac{3.3534}{\left(\mathrm{z}{\mathrm{MC}}+0.6\right)^{0.1742}}\right}$$ with $\mathrm{n}{\mathrm{Pos}}=104$ (the number of PSA eigenvectors), and $\mathrm{z}{\mathrm{MC}}=13.52$ (the linear regression residuals $z$-score measure of SA) here. This expression indicates that the candidate set should contain the 78 eigenvectors with the largest eigenvalues. Spatial regression analysis using eigenvector spatial filtering One useful criterion for eigenvector selection from the candidate set is the level of significance for each eigenvector’s regression coefficient, which essentially maximizes the linear regression $\mathrm{R}^{2}$ value; other selection criteria could be utilized (see Griffith, 2004). In addition, a stepwise procedure that combines both forward selection and backward elimination supports the construction of a parsimonious ESF. Because the eigenvectors are mutually orthogonal and uncorrelated, the primary factor in eigenvector selection during any given step is the marginal error sum of squares for that step. Of the 78 candidate eigenvectors, 26 were selected using a significance level criterion of $0.10$, accounting for roughly $62.5 \%$ of the variation in logtransformed PD across the counties of Texas (Fig. 3.1B), highlighting the Dallas, Houston, and Austin-San Antonio metropolitan regions and indicating that $\mathrm{SA}$ introduces variance inflation by more than doubling the underlying IID variance. Table $3.1$ summarizes the stepwise selection results, revealing that global (e.g., $\mathbf{E}{2}$ ), regional (e.g., $\mathbf{E}{19}$ ), and local (e.g., $\mathbf{E}{77}$ ) map pattern ${ }^{1}$ components account for the $\mathrm{SA}$ under study and that the Aegree of SA does not determine the selection sequence.

## 统计代写|回归分析作业代写Regression Analysis代考|Selected criteria for assessing regression models

Once an ESF is constructed, model dingnostics should be performed. The predicted residual error sum of squares (PRESS) statistic is a useful global diagnostic to calculate because it relates to a cross-validation assessment, with the set of covariates being held constant. Values of the ratio PRESS/ESS close to 1, where ESS denotes error sum of squares, indicate good model performance in this context because the corresponding estimated model fitting and prediction error essentially are the same (i.e., the estimated trend line also describes new observations well). Here this values is $376.737 / 355.645=1.059$, implying a very respectable model performance with regard to the cross-validation criterion.

Three features of the linear regression residuals merit assessment. The first concerns normality (Fig. 3.3A); here the Shapiro-Wilk statistic for the linear regrension residuals is $0.98030(p=0.0014)$; the frequency distribution for these residuals differs statistically, but not substantively, from a bell-shaped curve. The second concerns residual SA. The expected value
‘The grouping into global, regional, and local map patterns is subjective. These terms, respectively, refer to $\mathrm{MC} / \mathrm{MC}_{\max }$ (i-e., the maximum $\mathrm{MC}$ ) values in the ranges $0.9-1,0.7-0.9$, and $0.25-0.7$. The maximum MC value here is $1.09798$, which should be used to standardize $\mathrm{MC}$ values to make them comparable across geoggraphic handscapes.

## 统计代写|回归分析作业代写Regression Analysis代考|An illustrative linear regression example

$$\mathbf{Y}=\mathbf{1} \boldsymbol{\beta} {0}+\mathrm{E} { \mathrm{K}} \boldsymbol{\beta}_{\mathrm{E}}+\boldsymbol{\xi} 。$$

## 统计代写|回归分析作业代写Regression Analysis代考|The selection of eigenvectors to construct an ESF

1+\exp \left{2.1480-\frac{6.1808\left(\mathrm{z}{\mathrm{MC}}+0.6\right)^{0.1742}}{\mathrm{n}{\text {pos } }^{0.1298}}+\frac{3.3534}{\left(\mathrm{z}{\mathrm{MC}}+0.6\right)^{0.1742}}\right}1+\exp \left{2.1480-\frac{6.1808\left(\mathrm{z}{\mathrm{MC}}+0.6\right)^{0.1742}}{\mathrm{n}{\text {pos } }^{0.1298}}+\frac{3.3534}{\left(\mathrm{z}{\mathrm{MC}}+0.6\right)^{0.1742}}\right}和n磷这s=104（PSA特征向量的数量），和和米C=13.52（线性回归残差和-SA 的得分度量）在这里。这个表达式表明候选集应该包含 78 个特征向量的最大特征值。使用特征向量空间滤波的空间回归分析 从候选集中选择特征向量的一个有用标准是每个特征向量的回归系数的显着性水平，它基本上使线性回归最大化R2价值; 可以使用其他选择标准（参见 Griffith，2004 年）。此外，结合前向选择和后向消除的逐步过程支持简约 ESF 的构建。因为特征向量是相互正交且不相关的，所以在任何给定步骤中选择特征向量的主要因素是该步骤的边际误差平方和。在 78 个候选特征向量中，使用显着性水平标准选择了 26 个0.10, 大致占62.5%得克萨斯州各县的对数转换 PD 的变化（图 3.1B），突出显示达拉斯、休斯顿和奥斯汀-圣安东尼奥大都市区，并表明小号一种通过将基础 IID 方差增加一倍以上来引入方差膨胀。桌子3.1总结了逐步选择的结果，揭示了全局（例如，和2), 地区性的 (例如,和19）和本地（例如，和77) 地图图案1组件占小号一种正在研究中，并且 SA 的 Aegree 不能确定选择顺序。

## 统计代写|回归分析作业代写Regression Analysis代考|Selected criteria for assessing regression models

‘对全球、区域和本地地图模式的分组是主观的。这些术语分别指米C/米C最大限度（即，最大米C) 范围内的值0.9−1,0.7−0.9， 和0.25−0.7. 这里的最大 MC 值为1.09798, 这应该用于标准化米C值以使它们在地理景观中具有可比性。

## 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。