本次北美统计代写主要是R语言数据分析

De描述您的研究问题/假设/研究目的

about关于该主题的简要背景

o可选:包括对该领域已经完成的研究的引用(APA格式)

scribe描述如何清除您的数据

of简要说明您将在本文中进行的分析

Exp解释为什么某些变量会被排除在模型之外(如果您决定不使用所有

列出的变量)

数据集说明

Include包括要分析的每个变量的描述性统计信息

Add添加图表以帮助说明数据分布

Check检查响应变量的分布并探索之间的关系

响应和解释变量(同样在解释变量之间)

建筑模型和模型验证

Build建立您可以找到的最佳多元回归模型
o注意:没有一个“正确”模型。我们把它留给你玩

处理数据,然后找到您认为最能描述数据的模型。

model模型的解释是本节最重要的部分。

Cl根据R的输出清楚地指出最终的(选定的)回归方程式。

o需要系数解释

Val验证您最终选择的模型。

模型诊断

using使用残差诊断检查回归假设是否有效

A再次,仅将绘图插入纸中是不够的。确保解释什么

它们是关于您的模型的意思。

Check检查是否有任何外围和有影响力的要点。

结论

findings总结发现

Address解决您的学习限制

S建议的未来方向/扩展

参考

PA APA引用了您用于该项目的参考文献(如果您使用了来自外部来源的信息

可选的

to在本文中添加本课程中没有讲授的内容,或者对本课的讲授内容进行扩展

为您的论文和演示文稿考虑的事项

假设标记您的项目的TA没有关于数据集的背景知识。您应该尽力传达足够详细的信息,以便我们能够理解您的项目所处的位置,但要找到一个平衡点(不要过分注重细节)!

注意:这并不是可以添加到报告中的所有内容的详尽列表。它应该用作指导写作的模板,但是如果您认为自己的报告有需要,可以随时添加章节!

尖端

如果您以前没有接触过原始数据分析,那么此案例研究乍一看可能会让人感到不知所措。这里有一些帮助:

1)进行小组通话,并弄清楚每个人接下来几周的时间表
一种。相应地划分工作–确保每个人都有事情要做

为项目做贡献
2)与您的小组共享一个Google文档,并提出每周计划

一种。每隔几天设置截止日期,并确保达到要求的期限

3)安排本周结束时的汇报会,以便每个人都能按时完成

项目进度

4)进行分析时,经常有人请您看一下您的工作通常会有所帮助

一种。进行分析时,请尝试进行缩放通话并共享屏幕,以便您可以实时讨论和查看彼此的工作

5)玩得开心!
一种。这可能是您第一次真正应用自己的统计技术

在您的统计信息类别中被教导为真实的数据集。您从此类项目中学习到的技能将在未来继续存在,并帮助您找到工作,获得研究职位,甚至获得期末考试!!

Introduction

STAC67 Case Study Guidelines

  •   Describe your research question/hypothesis/study aims
  •   Brief background about the topic

    o Optional: Include references to research already done in this area (APA format)

  •   Describe how your data was cleaned
  •   Brief description of what analyses you will conduct in the paper
  •   Explain why some variables will be left out of the model (if you decide not to use all the

    variables listed)

    Description of Dataset

  •   Include descriptive statistics for each one of the variables that you will be analyzing
  •   Add graphs to help illustrate distributions of data
  •   Check the distribution of the response variable and explore the relationships between

    response and explanatory variables (also between explanatory variables themselves)

    Building Model and Model Validation

  •   Build the best multiple regressions model that you can find
    o Note: There is no one “correct” model. We are leaving it to you to play around

    with the data and find a model that you believe best describes the data.

  •   Interpretation of the model is the most important part of this section.
  •   Clearly indicate your final (selected) regression equation based on your output from R.

    o Co-efficient interpretations are required

  •   Validate your final selected model.

    Model Diagnostics

  •   Check if regression assumptions are valid using residual diagnostics
  •   Again, it’s not enough to just insert the plots in the paper. Make sure to interpret what

    they mean in relation to your model.

  •   Check if there any outlying and influential points.

    Conclusion

  •   Summarize findings
  •   Address limitations of your study
  •   Suggest future directions/extensions

Reference

APA citation of references that you used for this project (if you used information from external sources

Optional

Add something to this paper that was not taught in the course, or is an extension to something that was taught

Something to think about for your paper and presentation

Assume that the TAs marking your projects have no background knowledge about the dataset. You should try to convey the information with sufficient detail, so we are able to understand what your project is on, but find a balance (don’t go overboard with the details)!

Note: This is not an exhaustive list of everything that can be added to your report. It should serve as a template to guide your writing, but feel free to add sections if you think it is necessary for your own report!

Tips

This case study may seem overwhelming at first if you have no prior exposure to raw data analysis. Here’s a few things that help:

1) Get on a group call and figure out everyone’s schedules for the next few weeks
a. Divide up the work accordingly ensure that everyone has something to do to

contribute to the project
2) Share a google doc with your group and come up with a weekly schedule

a. Set deadlines every few days and ensure that they are met

  1. 3)  Schedule de-briefing sessions at the end of the week so that everyone is on track with the

    project progression

  2. 4)  When doing the analyses, it’s often helpful to have someone look over your work

a. Try jumping on a zoom call and sharing your screen while you’re doing the analysis so you can discuss and look over each other’s work in real-time

5) Have fun!
a. This could be
the first time you are actually applying the statistical techniques you’ve

been taught in your statistics classes to a real-world dataset. It’s the skills you learn from projects like these that will stick with you in the future and help you land a job, get a research position or even ace your final exam!!


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