统计代写|Generalized linear model代考广义线性模型代写|Statistics and Models

statistics-lab™ 为您的留学生涯保驾护航 在代写Generalized linear model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Generalized linear model代写方面经验极为丰富，各种代写Generalized linear model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|Generalized linear model代考广义线性模型代写|Why Statistics Matters

Although many students would not choose to take a statistics course, nearly every social science department requires its students to take a statistics course (e.g., Norcross et al., 2016; Stoloff et al., 2010). Why? Apparently, the professors in these departments think that statistics is essential to their students’ education, despite what their students may think.

The main reason that many students must take statistics is that research in the social sciences is dominated by methodologies that are statistics-based; this family of methods is called quantitative research. Researchers who use quantitative research convert their data into numbers for the purpose of analysis, and the numbers are then analyzed by statistical methods. Numerical

data are so important that one social scientist even argued that “progress in science is impossible without numbers and measurement as words and rhetoric are not enough” (Bouchard, 2014, p. 569).
Quantitative methods – and therefore statistics – dominate most of the behavioral sciences: psychology, sociology, education, criminal justice, economics, political science, and more. Most researchers working in these fields use statistics to test new theories, evaluate the effectiveness of therapies, and learn about the concepts they study. Even workers who do not conduct research must understand statistics in order to understand how (and whether) to apply scientific knowledge in their daily work. Without statistics a practitioner risks wasting time and money by using ineffective products, therapies, or procedures. In some cases this could lead to violations of ethics codes, accusations of malpractice, lawsuits, and harm to clients or customers. Even students who do not become scientists may need statistics to verify whether an anecdotal observation (e.g., that their company sells more products after a local sports team wins a game than after a losing one) is true. Thus, a mastery of statistics is important to many people, not just researchers and social scientists.
There are four main ways that practitioners use statistics in their work in the social sciences:

1. Separating good research from bad
2. Evaluating the conclusions of researchers
3. Communicating findings to others
4. Interpreting research to create practical, real-world results.
There is some overlap among these four points, so some job tasks will fall into more than one category. Nevertheless, this is still a useful list of ways that professionals use statistics.

Separating good research from bad is important for any practitioner. The quality of the research published in scientific journals varies greatly. Some articles become classics and spark new avenues of research; others report shoddy research. Thus, the fact that a study was published in a scientific journal is not, by itself, evidence of good-quality scientific work. A knowledge of statistics is one of the most important tools that a person can have in distinguishing good research from bad. Having the ability to independently judge research prevents practitioners from being susceptible to fads in their field or from wasting resources on practices that provide few benefits.

The benefits of separating good research from bad research are important for the general public, too (not just practitioners). Most people rely on reports from the news media and the Internet to learn about scientific findings. However, most journalists are not trained scientists and do not have the skills needed to distinguish between a high-quality study and a low-quality one (Yettick, 2015). Readers with statistical training will be able to make these judgments themselves, instead of relying on the judgment of a journalist or social media contacts.

Statistical savviness can also help people in evaluating researchers’ conclusions. Ideally, the conclusions in a scientific article are supported by the data that the researchers collected. However, this is not always the case. Sometimes researchers misinterpret their data because they either used the wrong statistical procedures or did not understand their results. Having statistical competence can prevent research consumers from being at the mercy of the authors and serve as an independent check on researchers.

统计代写|Generalized linear model代考广义线性模型代写|Two Branches of Statistics

As the science of quantitative data analysis, statistics is a broad field, and it would be impossible for any textbook to cover every branch of statistics while still being of manageable length. In this book we will discuss two branches of statistics: descriptive statistics and inferential statistics. Descriptive statistics is concerned with merely describing the data that a researcher has on hand. Table $1.1$ shows an excerpt from a real collection of data from a study (Waite, Cardon, \& Warne, 2015) about the sibling relationships in families where a child has an autism spectrum disorder. (We will discuss this study and its data in much more detail in Chapters 3 and 10.) Each row in the dataset represents a person and each column in the dataset represents a variable. Therefore, Table $1.1$ has 13 people and 6 variables in it. Each piece of information is a datum (plural: data), and because every person in the table has a value for every variable, there are 84 data in the table (13 people multiplied by 6 variables $=78$ data). A compilation of data is called a dataset.

Even though the dataset in Table $1.1$ is small, it is still difficult to interpret. It takes a moment to ascertain, for example, that there are more females than males in the dataset, or that most people are satisfied with their relationship with their sibling with autism. Table $1.1$ shows just an excerpt of the data. In the study as a whole, there were 45 variables for 13 subjects, which totals to 585 data. No person – no matter how persistent and motivated they are – could understand the entire dataset without some simplification. This is actually a rather small dataset. Most studies in the social sciences have much larger sample sizes. The purpose of descriptive statistics is to describe the

dataset so that it is easier to understand. For example, we could use descriptive statistics to say that in the range of scores on the variable that measures people’s satisfaction with their sibling relationship, the average score is $4.1$, while the average score on the variable measuring whether the sibling with autism understands the respondent’s interests is $2.9$. Chapters 2 – 5 are concerned with descriptive statistics.

On the other hand, if a researcher only has sample data on hand, descriptive statistics tell the researcher little about the population. A separate branch of statistics, termed inferential statistics, was created to help researchers use their sample data to draw conclusions (i.e., inferences) about the population. Inferential statistics is a more complicated field than descriptive statistics, but it is also far more useful. Few social scientists are interested just in the members of their sample. Instead, most are interested in their entire population, and so many social scientists use inferential statistics to learn more about their population – even though they don’t have data from every population member. In fact, they usually only have data from a tiny portion of population members. Inferential statistics spans Chapters $6-15$ of this book.

An example of a use of inferential statistics can be found in a study by Kornrich (2016). This researcher used survey data to examine the amount of money that parents spend on their children. He divided his sample into five groups, ranked from the highest income to the lowest income. He then found the average amount of money that the parents in each group spent on their children and used inferential statistics to estimate the amount of money each group in the population would spend on their children. Unsurprisingly, richer parents spent more money on their children, but Kornrich $(2016)$ also found that the gap in spending on children between the richest $20 \%$ and poorest $20 \%$ of families had widened between 1972 and 2010 . Because Kornrich used inferential statistics, he could draw conclusions about the general population of parents – not just the parents in his sample.

统计代写|Generalized linear model代考广义线性模型代写|Models

This book is not organized like most other textbooks. As the title states, it is built around a general linear model (GLM) approach. The GLM is a family of statistical procedures that help researchers ascertain the relationships between variables. Chapter 7 explains the GLM in depth. Until then, it is important to understand the concept of a model.

When you hear the word “model,” what do you think of? Some people imagine a fashion model. Others think of a miniature airplane model. Still others think of a prototype or a blueprint. These are all things that are called “models” in the English language. In science, models are “simplifications of a complex reality” (Rodgers, 2010, p. 1). Reality is messy and complicated. It is hard to understand. In fact, reality is so complex-especially in the social sciences – that in order for people to comprehend it, researchers create models.

An example from criminology can illustrate the complexity of reality and the need for models. One of the most pressing questions in criminology is understanding who will commit crimes and why. In reality, it is impossible to comprehend every influence that leads to a person’s decision to commit a crime (or not). This would mean understanding the person’s entire personal history, culture, thoughts, neighborhood, genetic makeup, and more. Andrews and Bonta (2010) have developed the risk-need-responsivity (RNR) model of criminal conduct. Although not its only purpose, the RNR model can help users establish the risk that someone will commit a crime. Andrews and Bonta do not do this by attempting to understand every aspect of a person. Rather, they have chosen a limited number of variables to measure and use those to predict criminal activity. Some of these variables include a history of drug abuse, previous criminal behavior, whether the person is employed, the behavior of their friends, and the presence of certain psychological diagnoses (all of which affect the probability that someone will commit a crime). By limiting the number of variables they measure and use, Andrews and Bonta have created a model of criminal behavior that has been successful in identifying risk of criminal behavior and reducing offenders’ risk of future reoffending after treatment (Andrews, Bonta, \& Wormith, 2011). This model because it does not contain every possible influence on a person’s criminal behavior – is simplified compared to reality.

This example illustrates an important consequence of creating a model. Because models are simplified, every model is – in some way – wrong. Andrews and Bonta (2010) recognize that

their model does not make perfect predictions of criminal behavior every time. Moreover, there are likely some influences not included in the RNR model that may affect the risk of criminal behavior, such as a cultural influence to prevent family shame or the dying request of a beloved relative. Therefore, one can think of a trade-off between model simplicity and model accuracy: simpler models are easier to understand than reality, but this simplification comes at a cost because simplicity makes the model wrong. In a sense, this is true of the types of models most people usually think about. A miniature airplane model is “wrong” because it often does not include many of the parts that a real airplane has. In fact, many model airplanes don’t have any engines – a characteristic that definitely is not true of real airplanes!

Because every model is wrong, it is not realistic to expect models to be perfectly accurate. Instead, models are judged on the basis of how useful they are. A miniature model airplane may be useless in understanding how a full-sized airplane works, but it may be very helpful in understanding the aerodynamic properties of the plane’s body. However, a different model – a blueprint of the engine – may be helpful in understanding how the airplane obtains enough thrust and lift to leave the ground. As this example shows, the usefulness of the model may depend on the goals of the researcher. The engineer interested in aerodynamics may have little use for the engine blueprint, even though a different engineer would argue that the engine blueprint is a vital aspect of understanding the airplane’s function.

This example also shows one last important characteristic of models: often multiple models can fit reality equally well. In other words, it is possible for different models to fit the same reality, such as the miniature airplane model and the plane engine blueprint (Meehl, 1990). As a result, even if a model explains a phenomenon under investigation very well, it may not be the only model that could fit reality well. In fact, there is no guarantee that the model is even the best possible model. Indeed, many researchers in the social sciences are interested in improving their models because that would lead to an improved understanding of the things they investigate. This improvement can happen by combining two models together, finding improved operationalizations of variables, or eliminating unnecessary parts from a model.

统计代写|Generalized linear model代考广义线性模型代写|Why Statistics Matters

1. 区分好的研究和坏的研究
2. 评估研究人员的结论
3. 与他人交流调查结果
4. 解释研究以创造实用的、真实的结果。
这四点之间有一些重叠，因此一些工作任务将属于不止一个类别。尽管如此，这仍然是专业人士使用统计数据的有用列表。

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。