本次美国作业代写主要内容是人机交互相关的R语言数据分析
EN.601.491/691 Human-Robot Interaction
Assignment 5: Inferential Statistics Basic Information
这是个人任务；您必须自己完成并提交此作业。您的上交文件应该是一个遵循命名约定“ HRI-Assignment5-FirstNameLastName”的zip文件。压缩文件应包含您的R代码和数据文件。
截止日期：2021年4月15日晚上11:59
将您的作业上传到Slack上的＃hw5频道。
描述
推论统计使用统计模型来帮助您将样本数据与其他样本或以前的研究进行比较。大多数研究使用称为通用线性模型的统计模型，包括学生的t检验，方差分析（ANOVA），回归分析和其他各种得出直线（“线性”）概率和结果的模型。
我希望您可以从Kaggle（https://www.kaggle.com/datasets）或您感兴趣的任何其他地方找到自己的数据。请获取具有大量样本数量（至少100个）的数据。提交作品时，将数据与R代码一起附加。确保在脚本上留下注释，以方便阅读和评估。
任务1：假设形成
假设用于解释传播研究中的现象或预测关系。假设必须满足四个评估标准。首先，它必须声明变量之间的预期关系。其次，它必须是可测试的和可证伪的；研究人员必须能够检验假设是对还是错。第三，它应该与现有知识体系保持一致。最后，应尽可能简明扼要地说明这一点。
提出假设需要在理论指导和/或先前证据的驱动下做出具体，可检验和可预测的陈述。可以在各种研究设计中提出假设。在实验环境中，研究人员比较两组或更多组研究参与者以调查研究结果的差异。
在研究环境中，假设问题是在收集数据之前形成的。但是，出于此任务的目的，我希望您看一下您的数据集并为任务2和任务3提出两个假设。
任务2：参数统计分析
执行参数统计分析的方法有很多。对于此分配，您被分配从以下列表中选择一种方法：重复测量ANVOA，阶乘ANOVA，混合模型ANOVA，ANCOVA和MANOVA。
请执行以下任务以检验您的假设：
1）数据准备（如有必要）
2）假设检查
3）计算
4）事后测试
5）结果
如果您的数据不符合您选择测试的关键假设，请讨论采取了哪些补救措施。
任务3：非参数统计分析
非参数测试包括许多方法和模型。在我们在课堂上学到的最常见的测试中，Mann-Whitney U检验，Wilcoxon签署秩检验，Kruskal-Wallis检验和卡方检验–选择一种方法来检验您的假设。
请执行以下任务以检验您的假设：
6）数据准备（如有必要）
7）假设检查
8）计算
9）事后测试
10）结果
评分标准（30分）
任务1 = 5分任务2 = 10分任务3 = 10分
提交格式= 5分
This is an individual assignment; you have to complete and turn in this assignment by yourself. Your turn-in should be one zip file following the naming convention “HRI- Assignment5-FirstNameLastName.” The zip file should contain your R code and the data file.
Deadline: 11:59pm, April 15st, 2021
Upload your assignment to #hw5 channel on Slack.
Description
Inferential statistics use statistical models to help you compare your sample data to other samples or to previous research. Most research uses statistical models called the Generalized Linear model and include Student’s t-tests, ANOVA (Analysis of Variance), regression analysis and various other models that result in straight-line (“linear“) probabilities and results.
I would like you to find your own data from Kaggle (https://www.kaggle.com/datasets) or any other places of your interest. Please get a data that has large number of sample size (at least 100). Attach your data along with your R code when submitting the work. Make sure to leave comments on your script for easy read and evaluation.
Task 1: Hypothesis Formation
A hypothesis is used to explain a phenomenon or predict a relationship in communication research. There are four evaluation criteria that a hypothesis must meet. First, it must state an expected relationship between variables. Second, it must be testable and falsifiable; researchers must be able to test whether a hypothesis is truth or false. Third, it should be consistent with the existing body of knowledge. Finally, it should be stated as simply and concisely as possible.
Formulating a hypothesis requires a specific, testable, and predictable statement driven by theoretical guidance and/or prior evidence. A hypothesis can be formulated in various research designs. In experimental settings, researchers compare two or more groups of research participants to investigate the differences of the research outcomes.
In the research setting, hypothesis questions are formed before collecting data. However, for the purpose of this assignment, I want you to take a look at your dataset and formulate two hypotheses for Task 2 and Task 3.