### 电子工程代写|软件项目作业代写Software Project代考|ENSF409

statistics-lab™ 为您的留学生涯保驾护航 在代写软件项目Software Project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。我们的专家在代写软件项目Software Project方面经验极为丰富，各种代写软件项目Software Project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

## 电子工程代写|软件项目作业代写Software Project代考|Numerical Outcome of Reusability Estimation Model

The numerical outcome of the proposed system is shown in Tables $1.1,1.2,1.3$ and $1.4 .$

Figure $1.5$ shows the outcome of the reusability factor $\Delta D_{A}$ considering five design attributes. The parameter $X_{i}$ and $Y_{i}$ is used for computing reusability factor $\Delta D_{A}$. The numerical outcome of the study shows accomplishment of superior value of reusability factor of approximately $73 \%$. In order to estimate the effectiveness of the proposed outcome, we also compare the individual values of the CK metrics with that of Zhou and Leung [30]. Although there are various forms of thresholdbased studies formulated by various researchers, the recent study conducted by Antony [31] has shown that the study done by Zhou and Leung [30] has direct interpreted values of threshold and was also found to be referred by almost 169 researchers till date and is one of the latest thresholds formulated till date.
We use statistical analysis to explore the cost of the individual $\mathrm{CK}$ metrics. For better comparative analysis, we evaluate the accomplished values of proposed model with respect to the standard threshold values fixed by Zhou and Leung [30]. Our outcome shows that almost all the CK metrics considered for the study accomplished lower value, which is quite good for reusability factor. Lower value of WMC (2.30) will also mean lower number of classes leading to fewer complexes in accessing the program. Similar pattern of $\mathrm{RFC}(2.44)$ is also found in the comparative analysis. Smaller values of DIT (2.77) accomplished also show extremely lesser deeper class in hierarchy rendering it less complex. Even the $\mathrm{CBO}$ value (2.57) is also less than the maximum limit of 8 thereby reducing the dependencies of the inter-classes. Finally, moderate value of NOC (3.78) has kept a good balance between reuse and less probability of testing the code. It is because if NOC value is high, it ensures reusability, but at the same time, it minimizes reliability and requires more number of testing of classes. Hence, the proposed outcome shows a very good balance between the reusability and other performance of classes.

## 电子工程代写|软件项目作业代写Software Project代考|Numerical Outcome of Optimized Model

The numerical outcome of optimization model is highlighted in Table 1.4. The outcome shows the experimental and actually predicted value of consistency in adoptability and complexity factor. Out of total set of 324 rows of observation, we show only the significant reading in Table $1.4$ with different value of costs quantity, work schedule, and error. The calculation of cost, quantity, and work schedule is carried out using statistical analysis of previous model (t-test, analysis of variance, regression test, etc.). The proposed model considers $1-4 \%$ of errors in numerical analysis of optimized model. In multilayered perceptron, error computation is carried out by using subtracting desired result with actual result. We use curve fitting toolbox of Matlab to obtain this objective of implementing optimization model. Our experiment uses Levenberg-Marquard backpropagation algorithm using the toolbox. Usually the error value will lie between 0 and $0.05$ using the toolbox. Hence, we consider practical error values, e.g., $0-0.05$, as according to probability theory if $\mathrm{p}$ value lies between 0 and $0.05$, it is considered as ideal value for observation. Hence, we choose the error inclusion to be $0-0.04$, which after percentage conversion looks like 1-4\%. A closer look into the tabulated data also shows higher accomplishment of software consistency. The overall runtime of the application takes around $2.3 \mathrm{~s}$ for a five-project data totaling to $975 \mathrm{MB}$ of file size. Therefore, depending on higher degree of consistency value and lower values of complexities, it can be concluded that proposed model score is well in terms of software reusability with better consistency factor.

The key process information for the proposed design are basically (i) evaluating mean value for feature attribute CK and (ii) applying backpropagation and Levenberg-Marquardt optimization algorithm. As the proposed model performs reusability of design considering constraint formulations of number of developers and number of clients, cost, and errors, the accuracy depends on assumption of formulation of such constraints. However, even presences of minor errors are rectified in the optimization process using multilayered perceptron as core key process of enhancing the design reusability. Another key process is the adherence to the threshold value of design reusability considering only five $\mathrm{CK}$ metrics (i.e., CBO, RFC, WMC, DIT, and NOC). A significant process of the proposed model is basically the consistency measurement with reduced computational complexity. The entire process takes only $1.27-4.76 \mathrm{~s}$ to execute for normal operating system without storing any transactional computational data. Hence, the proposed model is highly computationally efficient model.

## 广义线性模型代考

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。