Business forecasting

refer to attached

ANL317
Business Forecasting
Tutor-Marked Assignment
January 2024 Presentation
ANL317
Tutor-Marked Assignment
TUTOR-MARKED ASSIGNMENT (TMA)
This assignment is worth 24% of the final mark for ANL317 Business Forecasting.
The cut-off date for this assignment is 21 February 2024, 2355hrs.
Note to Students:
Compose your report using Microsoft Office Word, and save either as .doc or .docx
(preferred).
You are to include the following particulars in your submission: Course Code, Title of the
TMA, SUSS PI No., Your Name, and Submission Date.
Up to 25 marks of penalties will be imposed for inappropriate or poor paraphrasing. For serious
cases, they will be investigated by the examination department. More information on effective
paraphrasing
strategies
can
be
found
on
https://academicguides.waldenu.edu/writingcenter/evidence/paraphrase/effective.
If your course involves programming, you are urged to read the following articles as well:
https://wiki.cs.astate.edu/index.php/Plagiarism_in_a_Programming_Context
https://www.turnitin.com/blog/plagiarism-and-programming-how-to-code-withoutplagiarizing-2
Use of Generative AI Tools (Allowed)
The use of generative AI tools is allowed for this assignment.
•
You are expected to provide proper attribution if you use generative AI tools while
completing the assignment, including appropriate and discipline-specific citation, a
table detailing the name of the AI tool used, the approach to using the tool (e.g. what
prompts were used), the full output provided by the tool, and which part of the output
was adapted for the assignment;
•
To take note of section 3, paragraph 3.2 and section 5.2, paragraph 2A.1 (Viva Voce)
of the Student Handbook;
•
The University has the right to exercise the viva voce option to determine the authorship
of a student’s submission should there be reasonable grounds to suspect that the
submission may not be fully the student’s own work.
•
For more details on academic integrity and guidance on responsible use of generative
AI tools in assignments, please refer to the TLC website for more details;
SINGAPORE UNIVERSITY OF SOCIAL SCIENCES (SUSS)
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ANL317
•
Tutor-Marked Assignment
The University will continue to review the use of generative AI tools based on feedback
and in light of developments in AI and related technologies.
SINGAPORE UNIVERSITY OF SOCIAL SCIENCES (SUSS)
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ANL317
Tutor-Marked Assignment
Question
In the text file “uscrime.csv”, a total of 347,892 criminal cases occurred in the United States
between 1994 and 2014 are recorded. The dataset contains the following variables:
Variable
Possible Values
Description
Agency Type
County Police, Municipal Police,
Regional Police, Sheriff, Special
Police, State Police, Tribal Police
Agency to which the crime
case was first reported
State
All 51 US States
State in which the crime case
took place
Year
1994 – 2014
Year in which the crime case
took place
Month
January – December
Month in which the crime case
took place
Crime Type
Manslaughter by Negligence,
Murder or Manslaughter
Type of the reported crime
case
Crime Solved
Yes, No
Indicator whether the crime
case was solved or not
Victim Sex
Female, Male
Gender of the main victim
involved in the crime case
Victim Age
0 – 99
Age of the main victim
involved in the crime case
Victim Race
Asian/Pacific Islander, Black,
Native American/Alaska Native,
Unknown, White
Race of the main victim
involved in the crime case
Victim Ethnicity
Hispanic, Not Hispanic, Unknown Ethnicity of the main victim
involved in the crime case
Perpetrator Sex
Female, Male
Gender of the main perpetrator
involved in the crime case
Perpetrator Age
0 – 99
Age of the main perpetrator
involved in the crime case
Perpetrator Race
Asian/Pacific Islander, Black,
Native American/Alaska Native,
Unknown, White
Race of the main perpetrator
involved in the crime case
Perpetrator Ethnicity Hispanic, Not Hispanic, Unknown Ethnicity of the main
perpetrator involved in the
crime case
Relationship
Acquaintance, Boyfriend/
Girlfriend, Brother, CommonLaw Husband, Common-Law
Wife, Daughter,
Employer/Employee, Ex-
SINGAPORE UNIVERSITY OF SOCIAL SCIENCES (SUSS)
How the victim is related to the
perpetrator
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ANL317
Tutor-Marked Assignment
Husband, Ex-Wife, Family,
Father, Friend, Husband, In-Law,
Mother, Neighbour, Sister, Son,
Stepdaughter, Stepfather,
Stepmother, Stepson, Stranger,
Unknown, Wife
Weapon
Blunt Object, Drowning, Drugs,
Explosives, Fall, Fire, Firearm,
Gun, Handgun, Knife, Poison,
Rifle, Shotgun, Strangulation,
Suffocation, Unknown
Main weapon used in the crime
case
Victim Count
0 – 10
Number of victims involved in
the crime case
Perpetrator Count
0 – 10
Number of perpetrators
involved in the crime case
Record Source
FBI, FOIA
Source of the crime
(a)
Discuss whether the given data is a univariate time series or not (max. 200 words).
(10 marks)
(b)
If the researcher wanted to study the general yearly development of the crime rate
(occurrence of crime cases) in the United States between 1994 and 2014 and forecast
future criminal rate based on the given dataset, what would be your suggestion on how
to reshape this dataset to meet his study requirements? Write down your data
preparation plan step by step in point form without going into detail or including any
screenshots (max. 300 words).
Note: It is not an Excel exercise. Therefore, you should not describe the steps you would
conduct in Excel for this task. Instead, you must describe how you can construct such
a time series in general data preparation terms.
(20 marks)
(c)
The researcher has then decided to use the time series of the nationwide (entire USA)
monthly average victim age for his study, regardless the type of crime that was
committed. Prepare the data accordingly and report your steps in point form with all
necessary screenshots (max 400 words, each screenshot should not have a height of
more than 7cm).
(40 marks)
(d)
Construct useful charts and calculate relevant statistics in SAS for the time series.
Explain in less than 200 words (excluding the SAS code) why these charts or statistics
are significant in analysing and forecasting future values of the time series (each
screenshot should not have a height of more than 7cm).
(20 marks)
(e)
From the statistics and charts created in part (d), evaluate how the average age of the
victims had been developing between 1994 and 2014. Present your argument in less
than 200 words.
SINGAPORE UNIVERSITY OF SOCIAL SCIENCES (SUSS)
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ANL317
Tutor-Marked Assignment
(10 marks)
—- END OF ASSIGNMENT —-
SINGAPORE UNIVERSITY OF SOCIAL SCIENCES (SUSS)
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