HI6007 Statistics for Business Decisions Assignment Help

Group Assignment

Assessment Details and Submission Guidelines
Trimester	T1 2025
Unit Code	HM6007 Block Mode 1
Unit Title	Statistics for Business Decisions
Assessment Type	Group Assignment
Due Date+ time:	25/04/2025 11.59pm(Melb/Sydtime)
Weight	40%
Submission Guidelines	All work must be submitted on Blackboard by the due date along with a completed Assignment Cover Page. The assignment must be in MSWord format unless otherwise specified.
Academic Integrity Information	Holmes Institute is committed to ensuring and upholding academic integrity. All assessments must comply with academic integrity guidelines. Please learn about academic integrity and consult your teachers with any questions. Violating academic integrityisseriousandpunishablebypenaltiesthatrangefromdeductionofmarks, failure of the assessment task or unit involved, suspension of course enrolment, or cancellation of course enrolment.
Penalties	All work must be submitted on Blackboard by the due date and time, along with a completed Assessment Cover Page. Late penalties apply. 20%penalty applicable for solo (single student)group submissions. Your answers must be based on Holmes Institute syllabus of this unit. Outside sources may not amount to more than 10% of any answer and must be correctly referenced in full. Over-reliance on outside sources will be penalised Reference sources must be cited in the text of the report and listed appropriately at the end in a reference list using Holmes Institute Adapted Harvard Referencing. Penalties are associated within correct citation and referencing.

Group Assignment Guidelines and Specifications

PART A (25 marks)

Assume your group is the data analytic steam in a renowned Australian company. The company offers its assistance to a distinct group of clients, including (but not limited to) public listed companies, small businesses, and educational institutions .The company has undertaken several data analysis projects ,all based on multiple regression analysis. One such project is related to the real estate market in Australia, and the team needs to answer the following research question based on their analysis.

Research question:

How do different factors, such as the size of the land, the number of bedrooms, the distance to the nearest secondary school, and the number of garage spaces, influence the selling price of residential properties?

Data Collection Task(5 marks)

Create a dataset (in Excel) that satisfies the following conditions.(You are required to upload the data file separately).

Minimum number of observations–100 observations.
The data set should be based on houses sold from 01/01/2025 onwards. (To verify the data set, you are required to add a hyper link to each property’s details from the real estate websites that you used. (5 marks)

Questions (20 marks)

Conduct a descriptive statistical analysis in Excelusing the data analysis tool. Create a table that includes the following descriptive statistics for each variable in your data set: mean, median, mode, variance, standard deviation, skewness, kurtosis, and coefficient of variation. (4 marks)
Provide a brief commentary on the descriptive statistics you calculated. Describe the characteristics of the distribution for each variable based on these statistics. (3 marks)
Create an appropriate graph to illustrate the distribution of the number of bedrooms in your dataset.(2)
Derive a suitable graph to represent the relationship between the dependent variable and the land size in your data set and comment on the identified relationship. (3 marks)
Based on the data set, perform multiple regression analysis and correlation analysis, and answer the questions given below.

At a 5% significance level, test the overall model significance.(3marks)
- Ata5% significance level, assess the significance of the independent variables in the model.(3marks)
- Perform correlation analysis and based on the correlation coefficients in the correlation output,assess the correlation between explanatory variables and check for the possibility of multicollinearity.(2mrk)

Part B (10marks)

In Australia many people are involved in multi-jobs to supplement their income. There is an on going discussion surrounding work force development, and the gender-specific challenges in Australia’s employment landscape. The phenomenon of multiple job holders and their supplementary income sources is of major interest to policy makers, employers, and the general public.

Using data obtained from the ABS. We will specifically investigate the various issues related to employment in Australia, focusing on employment income between first and second job in Financial and insurance services sector.

Research Question:

What the relationship between first and second job employment income in Australia.

Task

Note: Refer the data given the excel file“HIM6007 T1 2025 Group Assessment-Dataset”

Based on the dataset ,perform regression analysis, and answer the questions given below.

Derive the simple regression equation.(1mark)
Interpret the meaning of the coefficients in the regression equation.(2marks)
Interpret the calculated coefficient of determination. (2marks)
At a5% significance level, test the over all model significance.(3marks)
Select an appropriate analysis to compare Male and Female employment income(2 marks)

PART C(5 marks)

Based on the answers in PART A above, write a summary of your analysis addressing the research question

(100-150words).(3 marks)

Based on the answers in PART B above, write a summary of your analysis addressing the research question

(100words).(2marks)

Marking criteria

Marking criteria	Weighting
PART A(25 marks)
Data collection(Excel spreadsheet)	5marks
Descriptive statistical analysis and review(Questions I and ii)	7marks
Graphical representations of data (Questions iii and iv)	5marks
Regression and correlation output and interpretation of coefficients(Questions v)	8marks
PARTB(10marks)
Derive the multiple regression equation and interpret the meaning of all the coefficients in the regression equation (Question I, ii & iii)	5marks
Assessing the overall model significance and Compare Male and Female employment income (Question iv)	5marks
PARTC(5marks)
Summary(I and ii)	5marks
TOTAL Weight	40Marks
Assessment Feedback to the Student:

Marking Rubric

	Excellent	Very Good	Good	Satisfactory	Unsatisfactory
Performing	Demonstration of	Demonstration of	Demonstration of	Demonstration of	Demonstration of
descriptive	outstanding	very good	Good knowledge	Basic knowledge	Poorknowledge on
Statistical analysis	Knowledge on	Knowledge on	On descriptive	on descriptive	Descriptive measures
And review of the	descriptive	descriptive	measures	measures
Calculated values	measures	measures
Deriving suitable	Demonstration of	Demonstration of	Demonstration of	Demonstration of	Demonstration of poor
Graph to represent	outstanding	Very good	Good knowledge	Basic knowledge on	Knowledge on
The relationship between variables	knowledge on presentation of data	knowledge on presentation of data using presentation	On presentation of data using suitable Chart types.	Presentation of data using suitable chart	Presentation of data using suitable chart types.
	using suitable chart	Of data using		types.
	types.	Suitable chart types.
Deriving multiple regression equation based on the regression output.	Demonstration of outstanding knowledge On regression model estimation and interpretation	Demonstration of very good knowledge on regression model estimation and interpretation	Demonstration of good knowledge on regression model estimation and interpretation	Demonstration of basic knowledge on regression model estimation and interpretation	Demonstration of poor knowledge on regression Model estimation and interpretation
Interpreting the calculated coefficient of determination.	Demonstration of outstanding knowledge on coefficient of determination calculation and interpretation of relationship between variables	Demonstration of very good knowledge on coefficient of determination calculation and interpretation of relationship	Demonstration of good knowledge on coefficient of determination calculation and interpretation of relationship between variables	Demonstration of basic knowledge on coefficient of determination calculation and interpretation of relationship Between variables	Demonstration of poor knowledge on coefficient of determination calculation and interpretation of relationship between variables
		Between variables
Assessing the overall model significance.	Demonstration of outstanding knowledge on model significance	Demonstration of very good Knowledge on model significance	Demonstration of good knowledge on model significance	Demonstration of basic knowledge eon model significance	Demonstration of poor knowledge on model significance
Assessing the significance of independent variables in the model.	Demonstration of outstanding knowledge on significance of independent variables.	Demonstration of very good knowledge on significance of independent variables.	Demonstration of good knowledge on significance of independent variables.	Demonstration of basic knowledge on significance of independent variables.	Demonstration of poor knowledge on Significance of independent variables.
Examining the correlation between explanatory variables and check the possibility of Multi collinearity.	Demonstration of outstanding knowledge on correlation coefficient calculation, interpretation of relationship between variables and	Demonstration of very good knowledge on correlation coefficient calculation, interpretation of relationship between variables	Demonstration of good knowledge correlation coefficient calculation, interpretation of relationship between variables and assessing	Demonstration of basic knowledge on correlation coefficient calculation, interpretation of relationship between variables and assessing	Demonstration of poor knowledge on correlation coefficient calculation, interpretation of relationship between variables and assessing

assessing multi collinearity.

and assessing multicollinearity.

multicollinearity.

Addressing research questions based on data analysis

Demonstration of outstanding knowledgeon addressingresearch questions based on data analysis.

Demonstrationof very good knowledge on addressingresearch questionsbasedon

dataanalysis.

Demonstrationof good knowledge on addressing research questionsbased on data

analysis.

Demonstration of basicknowledgeon addressingresearch questionsbasedon data analysis.

Demonstration of poor knowledgeon addressingresearch questionsbased on data analysis.

Your final submission is due Friday of week ten before midnight.

The following penalties will apply:

Late submissions-5%perday.
1. No coversheet OR inaccuracies on the coversheet-10%
2. No title page-10%
3. Inaccuracies in referencing OR incomplete referencing OR not in Holmes-adapted- Harvard style -10%
4. Solo Group penalty-20%

Student Assessment Citation and Referencing Rules

Holmes has implemented a revised Harvard approach to referencing. The following rules apply:

Reference sources in assignments are limited to sources that provide full-text access to the source’s content for lecturers and markers.
The reference list must be located on a separate page at the end of the essay and titled: “References”.
The reference list must include the details of all the in-text citations, arranged A-Z alphabetically by author’s surname with each reference numbered(1to10,etc.)and each reference MUST include a hyperlink to the full text of the cited reference source.For example:

Hawking,P.,McCarthy,B.&Stein,A.2004.SecondWaveERPEducation,Journalof

Information Systems Education,Fall,http://jise.org/Volume15/n3/JISEv15n3p327.pdf

All assignments must include in-text citations to the listed references. These must include the surname of the author/s or name of the authoring body, year of publication, page number of the content, and paragraph where the content can be found. For example, “The company decided to implement an enterprise-wide data warehouse business intelligenceStrategy (Hawkingetal.,2004,p3(4)).”