Statistics for Business Decisions – Hire Academic Expert

HOLMES INSTITUTE
FACULTY OF
HIGHER EDUCATION

 

Assessment Details and Submission Guidelines
Trimester T2 2022
Unit Code HI6007
Unit Title Statistics for Business Decisions
Assessment Type Assessment 2
Assessment Title Group Assignment
Purpose of the
assessment (with ULO
Mapping)
Students are required to show an understanding of the principles and techniques of
business research and statistical analysis as taught in the course.
Weight 40% of the total assessments
Total Marks 40
Word limit N/A
Due Date Week 10 (23rd of September 2022)
Submission
Guidelines
Students must form groups (Min of 3 and Max of 4 members).
All work must be submitted on Blackboard by the due date along with a completed
Assignment Cover Page.
The assignment must be in MS Word format only, single spacing, 12-pt Arial font and
2 cm margins on all four sides of your page with appropriate section headings and
page numbers.
Reference sources must be cited in the text of the report and listed appropriately at
the end in a reference list using Holmes Adapted Harvard referencing style.

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HI6007 STATISTICS FOR BUSINESS DECISIONS
HI6007 STATISTICS FOR BUSINESS DECISIONS GROUP ASSIGNMENT
Assignment Specifications
Purpose:
The principal purpose of this assignment is to assess students’ understanding of different qualitative and
quantitative research methodologies and techniques. Other purposes are:
1. Explain how statistical techniques can solve business problems
2. Identify and evaluate a business situation in which statistical techniques have been used to solve a
business problem
3. Explain and justify the results of a statistical analysis in the context of critical reasoning for a business
problem solving
4. Apply statistical knowledge to summarize data graphically and statistically, either manually or via a
computer package
5. Justify and interpret statistical/analytical scenarios that best fit business solutions
Assignment Structure should be as the following:
This is an applied assignment. Students have to show that they understand the principles and techniques
taught in this course. Therefore, students are expected to show all their workings. All problems must be
completed in the format taught in class, the lecture notes or prescribed text book.
Any problems not done in the prescribed format will not be marked, regardless of the ultimate correctness
of the answer.
(Note: The questions and the necessary data are provided under “Assignment and Due date” in the
Blackboard.)
Instructions:
Your assignment must be submitted in WORD format only. Otherwise, your submission will
not be graded.
The assignment should be supported with the excel file which includes the data set and
outputs. Inability to meet this requirement will lead to a 10% reduction of total marks.
When answering questions, wherever required, you should copy/cut and paste the Excel output
(e.g., plots, regression output etc.) into your word doc so as to show your working/output.
Otherwise, you will not receive the allocated marks.
You are required to keep an electronic copy of your submitted assignment to re-submit, in case
the original submission fails and you are asked to resubmit.

Page 3 of 10
HI6007 STATISTICS FOR BUSINESS DECISIONS
Important Notice:
All assignments submitted undergo plagiarism checking; if found to have cheated, all students
involved in the submissions will be subject to penalty.
Group Assignment Questions
Part A
(10 marks)
The majority of people around the world are profoundly impacted by inflation since it reduces the purchasing
power of currency. Hence, researchers try to investigate the link between inflation and factors such as interest
rate, GDP growth rate, unemployment rate and GDP per capita.
Sheet 1 in the Data set (Group Assignment T2 2022) shows data related to the Australian inflation rate and
GDP per capita from 1975 – 2021.
You are required to:
a. Draw a suitable graph to show the relationship between inflation and per capita GDP and justify the
usage of the selected chart type.
(5 marks)
b. Critically review the observed relationship between those two variables and comment on key
fluctuations or trends. Also comment on their links with the major economic milestones in the Australian
economy over the 1975- 2021 period.
(5 marks)
Part B (30 marks)
Insurance is an integral part of peoples’ life. It is a flourishing industry from a business point of view. This
industry is mainly divided into two sub sectors namely life insurance and general insurance.
Life insurance companies are keenly interested in predicting how long their customers will live, because their
premium and profitability depends on such numbers. An actuary for one insurance company gathered data
from 100 recently deceased clients. He recorded the age at death, ages at death of their mothers and fathers,
number of years of employment and gender.
You are required to gather 80 observations from the given data set (data for part B), following the steps below.
Steps
1. Write down the student ID of all the members in your group. Ex: EPS3006, NXP5005
DY30120, ABN 8009
2. Add the last digit of each student IDs until a single digit result: 6 +5+0+9 =20 = 2+0 = 2
3.
If it is, 1 or 2 – select observations from: 1- 80
If it is, 3 or 4 – select observations from: – 6 – 85
If it is, 5 or 6 – select observations from: 11 – 90
If it is, 7 or 8 – select observations from: 16 – 95
If it is, 9 or 0 – select observations from: 21 – 100
Note: You will be graded zero for Part B of the assignment unless you satisfied the above
sample selection criteria.

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HI6007 STATISTICS FOR BUSINESS DECISIONS
Questions
1. Perform descriptive statistical analysis in excel and prepare a table with following descriptive
measures for quantitative variables in your data set.
Mean, median, mode, variance, standard deviation, skewness, kurtosis, coefficient of variation.
(4 marks)
2.
Briefly comment on the descriptive statistics in the question 1 and explain the nature of the
distribution (whether the variables are normally distributed or not) of those variables. Use graphs
where necessary.
(4 marks)
3. C
onstruct suitable graphs to represent the relationship between dependent variables and each
quantitative independent variable in your data set.
(3 marks)

4. Construct a suitable graph to describe the gender composition of the clients in your sample.
(2 marks)

5. Based on the data set you extracted, perform a regression analysis and correlation analysis, and
answer the questions given below.
a. Interpret the meaning of all the coefficients in the regression equation.
(2 marks)
b. Interpret the calculated coefficient of determination and the standard error of the

estimate
c. At 5% significance level, test the overall model significance.
(4 marks)
(2 marks)

d. At 5% significance level, assess the significance of independent variables in the
model.
(3 marks)
e. Based on the correlation coefficients in the correlation output, assess the correlation
between explanatory variables and check the possibility of multicollinearity.
(2 marks)
6. Assuming that you are the actuary, help this insurance company to make policy decisions on
insurance premiums. Your suggestions should be based on the above findings. You can propose to
them to gather more evidence if there are important variables omitted in the above
model. (
4 marks)
Page 5 of 10
HI6007 STATISTICS FOR BUSINESS DECISIONS
Marking criteria

Marking criteria Weighting
Part A topic: Understanding suitable chart type based on the nature of the data. Also,
presentation and interpretation of data.
10 marks
Part B topic: Selecting data based on the given selection criteria.
Inability to meet this will result in reduction of 5 marks from the final grade
Performing descriptive statistical analysis and review of the calculated values (Q 1 and Q 2) 8 marks
Constructing suitable graphs to represent the relationship between dependent variable and each
independent variable in your data set.
3 marks
Constructing a suitable graph to describe the gender composition of the clients in your sample 2 marks
Interpreting slope, intercepts, coefficient of determination and standard error of the regression
model
6 marks
Assessing the overall model significance and the significance of independent variables in the
model.
5 marks
Examining the correlation between explanatory variables and checking the possibility of
multicollinearity.
2 marks
Use of statistical calculations to make decisions 4 marks
TOTAL Weight 40 Marks
Assessment Feedback to the Student:

Marking Rubric

Excellent Very Good Good Satisfactory Unsatisfactory
Part A:
Understanding suitable
chart type based on the
nature of the data,
presentation of data and
interpretation
Demonstration of
outstanding knowledge
on representation of
data, based on the type
and scales of
measurements.
Demonstration of
very good knowledge
on representation of
data, based on the
type and scales of
measurements.
Demonstration of
good knowledge on
representation of
data based on the
type and scales of
measurements.
Demonstration of
basic knowledge on
representation of
data based on the
type and scales of
measurements.
Demonstration of poor
knowledge on
representation of data
based on the type and
scales of measurements.
Part B
Performing descriptive
statistical analysis and
review of the calculated
values
Demonstration of
outstanding knowledge
on descriptive
measures
Demonstration of
very good
knowledge on
descriptive
measures
Demonstration of
good knowledge on
descriptive
measures
Demonstration of
basic knowledge on
descriptive
measures
Demonstration of poor
knowledge on descriptive
measures
Deriving suitable graph to
represent the relationship
between variables
Demonstration of
outstanding knowledge
on presentation of data
using suitable chart
types.
Demonstration of
very good
knowledge on
presentation of data
using presentation
of data using
suitable chart types.
Demonstration of
good knowledge on
presentation of
data using suitable
chart types.
Demonstration of
basic knowledge on
presentation of data
using suitable chart
types.
Demonstration of poor
knowledge on
presentation of data using
suitable chart types.

 

Interpreting slope,
intercepts, coefficient of
determination and
standard error of the
regression model
Demonstration of
outstanding knowledge
on interpretation of
regression model
Demonstration of
very good knowledge
on interpretation of
regression model
Demonstration of
good knowledge on
interpretation of
regression model
Demonstration of
basic knowledge on
interpretation of
regression model
Demonstration of poor
knowledge on
interpretation of
regression model
Assessing the overall
model significance and
the significance of
independent variables in
the model.
Demonstration of
outstanding knowledge
on use of statistical
tests for significance
assessment in
regression analysis
Demonstration of
very good
knowledge on use of
statistical tests for
significance
assessment in
regression analysis
Demonstration of
good knowledge on
use of statistical
tests for
significance
assessment in
regression analysis
Demonstration of
basic knowledge on
use of statistical
tests for significance
assessment in
regression analysis
Demonstration of poor
knowledge on use of
statistical tests for
significance assessment in
regression analysis
Examining the correlation
between explanatory
variables and check the
possibility of
multicollinearity.
Demonstration of
outstanding knowledge
on correlation
coefficient calculation,
interpretation and
assessing
multicollinearity.
Demonstration of
very good
knowledge on
correlation
coefficient
calculation,
interpretation and
assessing
multicollinearity.
Demonstration of
good knowledge on
correlation
coefficient
calculation,
interpretation and
assessing
multicollinearity.
Demonstration of
basic knowledge on
correlation
coefficient
calculation,
interpretation and
assessing
multicollinearity.
Demonstration of poor
knowledge on correlation
coefficient calculation,
interpretation and
assessing
multicollinearity.
Use of statistical
calculations to make
decisions
Demonstration of
outstanding knowledge
on critical review and
application of statistical
findings
Demonstration of
very good
knowledge on
critical review and
application of
statistical findings
Demonstration of
good knowledge on
on critical review
and application of
statistical findings
Demonstration of
basic knowledge on
critical review and
application of
statistical findings
Demonstration of poor
knowledge on critical
review and application of
statistical findings

8
Academic Integrity
Holmes Institute is committed to ensuring and upholding Academic Integrity, as Academic Integrity is integral
to maintaining academic quality and the reputation of Holmes’ graduates. Accordingly, all assessment tasks
need to comply with academic integrity guidelines. Table 1 identifies the six categories of Academic Integrity
breaches. If you have any questions about Academic Integrity issues related to your assessment tasks, please
consult your lecturer or tutor for relevant referencing guidelines and support resources. Many of these
resources can also be found through the Study Skills link on Blackboard.
Academic Integrity breaches are a serious offence punishable by penalties that may range from deduction of
marks,
failure of the assessment task or unit involved, suspension of course enrolment, or cancellation of
course enrolment
.
Table 1: Six categories of Academic Integrity breaches

Plagiarism Reproducing the work of someone else without attribution. When a
student submits their own work on multiple occasions this is known
as
self-plagiarism.
Collusion Working with one or more other individuals to complete an
assignment, in a way that is not authorised.
Copying Reproducing and submitting the work of another student, with or
without their knowledge. If a student fails to take reasonable
precautions to prevent their own original work from being copied,
this may also be considered an offence.
Impersonation Falsely presenting oneself, or engaging someone else to present as
oneself, in an in-person examination.
Contract cheating Contracting a third party to complete an assessment task, generally
in exchange for money or other manner of payment.
Data fabrication and
falsification
Manipulating or inventing data with the intent of supporting false
conclusions, including manipulating images.

Source: INQAAHE, 2020
9
Assessment Design – Adapted Harvard Referencing
Holmes will be implementing as a pilot program a revised Harvard approach to referencing. The following
guidelines apply:
1. Reference sources in assignments are limited to sources which provide full text access to the
source’s content for lecturers and markers.
2. The Reference list should be located on a separate page at the end of the essay and titled:
References.
3. It should include the details of all the in-text citations,
arranged alphabetically A-Z by author
surname
and then numbered sequentially. In addition, it MUST include a hyperlink to the full text
of the cited reference source.
For example;
7. Hawking, P., McCarthy, B., and Stein, A. 2004, Second Wave ERP Education,
Journal of Information
Systems Education
, Fall, http://jise.org/Volume15/n3/JISEv15n3p327.pdf
4. All assignments will require additional in-text reference details which will consist of the surname of
the author/authors or name of the authoring body, year of publication, page number of contents,
paragraph where the content can be found.
For example;
“The company decided to implement an enterprise wide data warehouse business intelligence
strategy (Hawking et al, 2004, p3(4)).”
Non-Adherence to Referencing Guidelines
Where students do not follow the above guidelines:
1. Students who submit assignments which do not comply with the guidelines will be penalised -20%.
2. Students who comply with guidelines but their citations are “fake” will be reported for academic
misconduct.