### 统计代写|应用统计代写applied statistics代考|More Linear Models in R

statistics-lab™ 为您的留学生涯保驾护航 在代写应用统计applied statistics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。我们的专家在代写应用统计applied statistics代写方面经验极为丰富，各种代写应用统计applied statistics相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (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|应用统计代写applied statistics代考|More Linear Models

The purpose of this chapter is to expand on what you learned in Chapter 5 , introducing you to the multitude of linear models you can do in $R$. This chapter assumes you are using the RxP.byTank dataset that was created in Chapter $5 .$
This chapter will cover the following topics:

1. Analysis of Variance (ANOVA) with more than one predictor
2. Linear regression
3. Analysis of Covariance (ANCOVA)
4. Plotting regressions
5. Predicting values from a model
As a reminder, we are only concerned only with “normal” data for the moment. By normal data, we are referring to data that fit a normal distribution, with an approximately even distribution of values around the mean. Table $6.1$ shows the variety of models that we can conduct under the $\operatorname{lm}()$ framework. Note that all we do is change the number and type of predictor variables to do different types of models.

## 统计代写|应用统计代写applied statistics代考|GETTING STARTED

The model commonly referred to as a linear model, or $\operatorname{lm}()$, is one of the most flexible and useful in all of statistics. We will discuss generalized linear models (GLMs) in Chapter 7 , which allow us to analyze non-normally distributed data. Before we get started, let’s load all the packages you will need here. This is something I like to do at the head of any script file. Remember that if you don’t have one of these packages already, you can download it by using the Package Installer menu, or simply by typing install.packages() at the prompt and put the name of the package in quotes inside the parentheses. If you use the menu, make sure you click the button to “include dependencies.” This is important since most packages rely on other packages to run.

This shows that we have very strong effects of both resource and predator treatments and a significant interaction between the two (albeit not a particularly strong one). In other words, predators alter the age at metamorphosis and so do the different resource levels. However, more importantly our model says that the effect of resources on age at metamorphosis depends on the presence of predators. We could also reframe this in the other direction; the effect of predators on age at metamorphosis depends on the amount of food tadpoles are fed.

## 统计代写|应用统计代写applied statistics代考|MULTI-WAY ANALYSIS OF VARIANCE-ANOVA

The linear model we ran Chapter 5 (remember, it was called “lm1”) had a single categorical predictor. This type of model is called a one-way Analysis of Variance (ANOVA). But what about when we have multiple categorical predictors that may interact with one another? This is generally referred to as a multi-way ANOVA-a model with two predictors would be a two-way ANOVA, three predictors would be a three-way ANOVA, and so on. Okay, but what does it mean to “interact?”

For the RxP data, it is very reasonable to expect that the effects of predators on age at metamorphosis or mass at metamorphosis (or any other response variable) might differ under different resource conditions. To code an interaction between two effects in R you use a colon (:). However, $R$ also provides us with a shortcut for writing both individual effects (e.g., Res or Pred) and the interaction between them (e.g., Res:Pred). Using the asterisk ( $\left.{ }^{}\right)$ tells $\mathrm{R}$ to look at both individual and interaction effects between whatever predictors you have provided. Thus, “Res Pred” is the same as writing “Res+Pred+Res:Pred.” This model is therefore looking at the effects of just resources, of just predators, and then of a possible interaction between the two. Here, we will switch from using Age.FromEmergence

(which is what we examined in Chapter 5) to Age.DPO, which is more intuitive since it tells us the age when froglets metamorphosed in terms of when they were laid as eggs.

Let’s begin with a two-way ANOVA examining the interacting effects of resources and predators on the log-transformed age at metamorphosis.

## 统计代写|应用统计代写applied statistics代考|More Linear Models

1. 具有多个预测变量的方差分析 (ANOVA)
2. 线性回归
3. 协方差分析 (ANCOVA)
4. 绘制回归
5. 从模型中预测值
提醒一下，我们目前只关心“正常”数据。通过正态数据，我们指的是符合正态分布的数据，其值在均值附近分布大致均匀。桌子6.1显示了我们可以在流明⁡()框架。请注意，我们所做的只是更改预测变量的数量和类型以执行不同类型的模型。

## 统计代写|应用统计代写applied statistics代考|MULTI-WAY ANALYSIS OF VARIANCE-ANOVA

（这是我们在第 5 章中研究过的）到 Age.DPO，它更直观，因为它告诉我们小蛙从产卵时变态的年龄。

## 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。