QBUS2810代写、代写Python编程设计、Canvas留学生代做、代做Python语言

日期：2019-03-26 10:05

QBUS2810

Statistical Modelling for Business

Individual Assignment 1

This individual assignment will contribute 5% towards your final result in

the unit. The deadline is Friday 29th March by 5pm. Submission is via

Turnitin on Canvas.

Key requirements:

It is encouraged that you create your entire assignment in a Jupyter notebook, including

your 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 be

taken with presentation for this option, however unavoidable error messages or page

formatting issues will be ignored in marking, as discussed in class. Alternatively, you

can write/type your answers and copy and paste relevant outputs into a text editor and

prepare a pdf file for submission; if you take this latter option then you must include

the Python code you developed, as an appendix in your report. Failure to provide

your 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 assignment

file, while all output should appear together with, or very close to, the discussion of

that 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 the

relationship between earnings and asset returns for companies listed on the NYSE. That

analysis was done in a contemporaneous framework. This cannot lead to an investment

strategy, since to invest in year t we need to buy stock at end of year t ? 1, but at end

of year t 1 we do not which companies will have positive or negative earnings in year

2

t. In this assignment, you will work in a predictive framework, allowing an investment

strategy to be formed if warranted, assessing whether (the sign of) earnings in one

year (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 positive

earnings 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 important

categorical variables, individually and in terms of the primary question being considered

in 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 not

Discuss in detail. (2 marks)

3. Conduct the Pearson test to formally assess the primary question here. List all

assumptions 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 assumptions

of the Pearson test? Discuss. (2 marks)

5. Conduct Fisher’s exact test to formally assess the primary question here. List all

assumptions 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 and

conclusions. Include a discussion of whether you would recommend an investment

strategy based on your findings. (4 marks)