[SOLVED] 代写 R html statistic Module: Business Statistics

Module: Business Statistics
Instructions
Please complete the following questions. Your answer to each question should have two separate sections. In Section 1 section, write out your answers using complete sentences. Include descriptive statistics in the text, or in tables or figures as appropriate. Tables and figures should be of publication quality (i.e., fully labelled, etc.). Integrate inferential statistics into your description of the results. Your answer should be short and concise. There should be no R code in Section 1.
Section 2 should include the complete R code that you used. Add comments to explain what the code does. The code should show all of the commands that you used, enough for me to replicate exactly what you did. Check that the code runs in one smooth go when you knit the R Markdown together in a fresh R session. You can include figures here that you used to explore the data that you don’ t wish to include in the first section. I will use the second section to help identify the sources of any mistakes. The first section should stand alone without the second section.
Use both null hypothesis significance testing and the estimation approach.
While there is a word limit of 3,500 words, your answers should be much shorter than this, perhaps 100-200 words per question. You get credit for a clear and concise report, and writing more words than necessary is not required.
In creating the code for Section 2, you may work together in small groups as we have been doing for the workshops. But for Section 1 you should work on your own. That is, your answer for Section 1 should be solely and entirely your own work.
Use the template “9999999.zip” which contains “9999999.Rmd” for your assessment. Replace everywhere 9999999 with your own student number. Knit the file to make the html file, which is what we will mark. Zip just the Rmd and html files by right clicking on the folder to zip it. Submit this zip file. Do not include data files or project files.
If you have questions, please be sure to post them to the module forum (see the “Forums” tab on the module page on my.wbs). If you are stuck coding, post a minimal working example.

Question 1
(50% of the marks)
You work for the financial conduct regulator and are completing work on the effect of payday loans. You have a survey of 5,000 customers where they reported their well-being and a measure of their socio-economic status. The regulator linked the survey responses to information from their credit file. Everyone applied for a payday loan, and those with credit scores of 500 or over received the loan. Does receiving a payday loan change well-being? If so, how much? Use a linear regression with credit score and loan status as independent variables to answer this question. Think carefully about whether you need to include socio-economic status.
In the file “payday.csv”, id is the customer ID. credit.score is their credit score. loan is a dummy variable indicating whether or not people were given the payday loan. SES indicates people’s socio-economic status, with higher scores indicating higer status. well.being is their self-reported well-being on a 1-7 scale, with 7 being the highest well-being. (Leave adverse.credit.event for Question 2.)

Question 2
(50% of the marks)
Using the payday loans data again, find out whether taking a payday loan makes people more or less likely to experience an adverse credit event (e.g., defaulting on another loan, making late payments on a credit card, etc.). Why doesn’t it matter whether or not you include socio-economic status?
The variable adverse.credit.event is a dummy indicating whether there was an adverse credit event in the next year.