QBUS2810作业代写、代写Python编程设计作业、Canv

QBUS2810Statistical Modelling for BusinessIndividual Assignment 1This individual assignment will contribute 5% towards your final result inthe unit. The deadline is Friday 29th March by 5pm. Submission is viaTurnitin on Canvas.Key requirements:It is encouraged that you create your entire assignment in a Jupyter notebook, includingyour Python code and with Markdown sections for your tables and written answers,and to submit the resulting downloaded html file as your assignment. Care must betaken with presentation for this option, however unavoidable error messages or pageformatting issues will be ignored in marking, as discussed in class. Alternatively, youcan write/type your answers and copy and paste relevant outputs into a text editor andprepare a pdf file for submission; if you take this latter option then you must includethe Python code you developed, as an appendix in your report. Failure to provideyour Python code will result in penalty and significant loss of marks. In both cases,only relevant analysis outputs (graphs, tables, etc) should appear in the assignmentfile, while all output should appear together with, or very close to, the discussion ofthat output, in the file. Less relevant outputs may be placed in an optional (extra)appendix.Business problem:This assignment is a continuation of the analysis conducted in lecture regarding therelationship between earnings and asset returns for companies listed on the NYSE. Thatanalysis was done in a contemporaneous framework. This cannot lead to an investmentstrategy, since to invest in year t we need to buy stock at end of year t ? 1, but at endof year t 1 we do not which companies will have positive or negative earnings in year2t. In this assignment, you will work in a predictive framework, allowing an investmentstrategy to be formed if warranted, assessing whether (the sign of) earnings in oneyear (say t 1) affects (the sign of) asset returns in the subsequent year (say t),and in particular whether returns are typically positive, or negative, following positiveearnings years, compared to negative earnings years.Data:The data file is ”US 90 08 wk3.csv”. Use the Python commands in ”Assignment 1.py”to prepare the data for analysis.Tasks:1. Conduct an appropriate exploratory data analysis (EDA) on the two importantcategorical variables, individually and in terms of the primary question being consideredin this assignment: is there a relationship between lagged (sign of) earnings per share(year t 1) and (sign of) asset return in the subsequent year t? (4 marks)2. Did you do any cleaning of the data prior to the EDA in part 1? Why or why notDiscuss in detail. (2 marks)3. Conduct the Pearson test to formally assess the primary question here. List allassumptions and assess/discuss whether they could be satisfied or not. (5 marks)4. Did the data thinning step in ”Assignment 1.py” have any impact on the assumptionsof the Pearson test? Discuss. (2 marks)5. Conduct Fisher’s exact test to formally assess the primary question here. List allassumptions and assess/discuss whether they could be satisfied or not. (3 marks)6. Write a brief (e.g. 0.5 page) report summarising and discussing your findings andconclusions. Include a discussion of whether you would recommend an investmentstrategy based on your findings. (4 marks)本团队核心人员组成主要包括硅谷工程师、BAT一线工程师，精通德英语！我们主要业务范围是代做编程大作业、课程设计等等。我们的方向领域：window编程 数值算法 AI人工智能 金融统计 计量分析 大数据 网络编程 WEB编程 通讯编程 游戏编程多媒体linux 外挂编程 程序API图像处理 嵌入式/单片机 数据库编程 控制台 进程与线程 网络安全 汇编语言 硬件编程 软件设计 工程标准规等。其中代写编程、代写程序、代写留学生程序作业语言或工具包括但不限于以下范围:C/C++/C#代写Java代写IT代写Python代写辅导编程作业Matlab代写Haskell代写Processing代写Linux环境搭建Rust代写Data Structure Assginment 数据结构代写MIPS代写Machine Learning 作业 代写Oracle/SQL/PostgreSQL/Pig 数据库代写/代做/辅导Web开发、网站开发、网站作业ASP.NET网站开发Finance Insurace Statistics统计、回归、迭代Prolog代写Computer Computational method代做因为专业，所以值得信赖。如有需要，请加QQ：99515681 或邮箱：99515681@qq.com 微信：codehelp