统计代写|工程统计作业代写Engineering Statistics代考|Fundamentals of Probability and Statistics

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• 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|工程统计作业代写Engineering Statistics代考|Introduction

In this book, the word statistics is used in two ways. First, it refers to the techniques involved in collecting, analyzing, and drawing conclusions from data – a procedure, or a recipe. The second, more frequently inferred meaning, is that of an estimated value, a number, calculated from either the data or a proposed theory, that is used for comparative purposes in testing a hypothesis (guess, supposition, etc.) about a parameter of a population – a numerical value. The topics presented in this book have been selected from our experience (and others’) to provide you with a set of procedures which are relevant to application work (such as data analysis in engineering, science, and business). Although fundamental concepts are explained and some equations are derived, the focus of this book is on the how-to of statistical applications.

There is a tension between perfection and sufficiency. Perfection seeks the truth, which follows the mathematical science viewpoint. Although perfection provides grounding in statistical analysis methods, it is usually a mathematical analysis that is predicated on many idealizations, making it imperfect. By contrast, sufficiency seeks utility and functional adequacy, a balance of expediency which is also grounded in mathematical fundamentals. Sufficiency is not sloppiness or inaccuracy. It is appropriate liberty with the idealization, grounded in an understanding of the limitations of the idealization and uncertainty in the “givens”. Both perfection and sufficiency are important, and perspectives of both are presented in this text. In this “Applied Engineering” text the balance tends toward sufficiency, rather than unrealistic perfection.

统计代写|工程统计作业代写Engineering Statistics代考|Deterministic and Stochastic

The term deterministic means that there is no uncertainty, there is perfect certainty about a value. Here are some simple examples: What is 3 times 4 ? Given that the side of a cube is $2.1 \mathrm{~cm}$, what is the surface area? What angle (rounded to three digits) has a tangent value of $0.75$ ? These were very simple calculations, but it is the same with something more complicated, such as: Given a particular heat exchanger and fluid flow rates and associated properties, use the equations in your heat transfer book to calculate the exit temperatures of the fluids. Regardless of the time of day, or location, or the computer type being used, every time the calculation is performed, we get exactly the same answer.

The term stochastic means that we get a different answer each time the calculation is performed, or each time the measurement is obtained. Here are some examples: What is the height of the next person you pass on the street? How many grains of sand are in a handful? If the product label indicates that the package contains $40 \mathrm{lbs}$, what might be the actual weight? If there are the same number of red and green marbles in a box and you draw three, blindfolded, how many green marbles will you have? If you want to compare fertilizer treatments, you will find that the year-to-year variation in weather and insect population, and the location-to-location variation in properties of the earth will cause significant variation in results.

Despite the use of deterministic calculations in teaching concepts and in estimating values, the reality about measurements and samples and predictions is that they have variation. Statistics provides techniques for analyzing and making decisions within the uncertainty.
Sources of variation include the vagaries of weather, the probability of selecting a particular sample, variation in raw material, mechanical vibration, incomplete fluid mixing, prior stress on a device, new laws and regulations, future prices, and many other aspects.

统计代写|工程统计作业代写Engineering Statistics代考|Treatments, Process, and Outcomes

The term treatment refers to the influence on a process. The influence might be how safety training is delivered (video, reading materials, in-person, comically, or seriously). A treatment could be a recipe or procedure to be followed. A treatment could be the type of equipment used (batch or continuous, toaster or microwave). A treatment could be the raw material supplier or the service provider. The treatment might be the operating conditions in manufacturing (flow rates, temperatures, mixing time, etc.).

The process is whatever responds to the treatment. It may be a human response to an office lighting treatment. It may be a mechanical spring-and-weight response to treatment by the ambient temperature. It may be a biological process response to a $\mathrm{pH}$ (acidity) treatment.

Outcome refers to the response of the process. It may be the time to recover physical health after an infection in response to the medicine dose. It may be the economic response of the nation due to changes in the prime lending rate. It may be the variation in a quality metric due to a particular treatment. It may be the probability of automobile accidents if the speed limit is changed.

Treatments and outcomes are variously termed influences and responses, causes and effects, inputs and outputs, independent and dependent variables, etc.

有限元方法代写

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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。