HA1011 Applied Quantitative Methods T3 2024 Assignment Help

 

HA1011 Applied Quantitative Methods T3 2024 Assessment

Group Assignment

Assessment Detailsand Submission GuidelinesTrimesterT3 2024Unit CodeHA1011Unit TitleApplied Quantitative MethodsAssessment TypeGroup AssignmentDue Date + time:

Date:22/01/2025

11.59 pm ( Melbourne / Sydney time)

Purpose of the assessment (with ULO Mapping)

Students are required to demonstrate understanding and application of basic statistics concepts to make business decision. Moreover, students should demonstrate their capability of data analysis and presentation using Microsoft Excel.

• Integrate theoretical and practical knowledge from the discipline of statistics to support business decisions;
• Analyse a scenario and apply and justify statistical techniques to solve business problems and the articulate the results to a range of stakeholders.
• Work well autonomously as well as within group settings to identify and apply statistical solutions to a business scenario

Weight40 %Total MarksAssignment (40 marks)Word limit2000 ± 500 wordsSubmission Guidelines

• All work must be submitted on Blackboard by the due date alongwith a completed Assignment Cover Page.
• The assignment mustbe in MS Word formatunless otherwise specified.

AcademicIntegrity InformationHolmes 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 anyquestions. Violating academic integrity is serious and punishable by penalties thatrange from deduction of marks, failure of the assessment task or unit involved, suspension of courseenrolment, or cancellation of course enrolment.Penalties

• All work mustbe submitted on Blackboard by the duedate and time,along with a completed Assessment Cover Page. Late penalties apply.
• Your answers must be based on Holmes Institute syllabus of this unit. Outside sources may not amount to more than10% of any answer and must be correctly referenced in full. Over-reliance on outside sources will be penalized.
• Reference sources mustbe 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 withincorrect citation and referencing.

Group Assignment Guidelines and Specifications

Instructions:

• Your assignment report must be submitted in WORD format only. However, you are required to attach the excel file to support your answers.
• When answering questions, wherever required, you should copy/cut and paste the Excel output (e.g., plots, regression output etc.) 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 is failed and/or you are asked to resubmit.
• Please check your Holmes email prior to reporting your assignment mark regularly for possible communications due to failure in your submission.

Group Assignment Questions

Part A (10 marks)

For this group assessment, you will analyze data from the Australian Bureau of Statistics (ABS) on “Average Weekly Ordinary Time Earnings, Full-time Adults by State,” with a focus on weekly earnings disaggregated by gender. Your task is to explore the patterns and trends in earnings across different states and gender groups. As a group, you will apply appropriate quantitative methods covered in class to present your findings in a concise report, demonstrating your ability to interpret and communicate the results of your analysis clearly and effectively.

1. You are required to present this data in a suitable graphical format. (3marks)
2. Comment on the key observations to identify key trends, differences between Male and Female. (3marks)
3. Comment on the key observations to identify key trends, differences between the States. (2 marks)
4. Suggest potential factors influencing these variations between the States. (2 marks)

Note: Refer the data given the excel file “Data Set for Part A_Average Weekly ordinary time earnings, full- time adults by state”

Part B (30 marks)

You are tasked with analyzing the determinants of weekly rent prices for houses across various regions in Australia. The primary objective of this assessment is to identify and investigate the factors that influence weekly rent prices and develop a predictive model to estimate rents based on these variables.

Instructions:

Data Collection:

• Collect data from a sample of at least 40 houses available for rent. The sample should be based on the state and city where your campus is located.
• The data should pertain to houses listed for rent from July 1st onwards.

Variables to Record:

For each property, you are required to collect the following information:

• Weekly Rent: The rent being asked for the property per week.
• Number of Bedrooms: The total number of bedrooms in the property.
• Distance to the Nearest Primary School: Measured in kilometres or meters, depending on the data available.
• Property Type: Specify whether the property is a House, Townhouse, Apartment, Unit, or Villa.
• Hyperlink to Advertisement: Provide the URL of the online listing where the property is advertised.
• Property Address: This will help in verifying the validity and location of the data.

Guidelines:

• Ensure the accuracy and consistency of your data.
• Pay particular attention to the reliability of the advertisement hyperlinks and the correctness of addresses. This will be essential for assessing the validity of your data and ensuring the sample represents properties available in the area.

(5 marks)

Once the data is collected, you will use it to answer the following questions:

i.Conduct descriptive statistical analysis on the collected data, including measures of central tendency, dispersion, and graphical representations to summarize the characteristics of the variables.

(5 marks)

ii,Calculate the correlation coefficients between weekly rent and each of the independent variables (number of bedrooms, distance to the nearest primary school), and discuss the strength and direction of the relationships between weekly rent and determinants of weekly rent.

(5 marks)

iii. Perform simple linear regression analysis with weekly rent as the dependent variable and each independent variable (Number of bedrooms, distance to the nearest primary school) separately. Based on your regression outputs from Excel, answer the following questions.

a. Comment on the coefficient of determination (R-squared) and the standard error of the estimate for each regression model.

b. Interpret the regression results and discuss the predictive power of each independent variable on weekly rent.

c. Assess the significance of the independent variable in each model.

(10 marks)

vi.Prepare a brief report that reflects your findings based on the answers to questions I through IV.

Note: Your report should primarily focus on the identified relationships and other key observations from your analysis.

(5 marks)

Note: Analysis should be performed in Microsoft Excel ONLY.

Marking criteria

Marking criteriaWeightingPart A: Analysis of economic data10 marksPart BCollection of data setwhich meets the given guidelines (Students must attachthe excel data file together with analysis and tables)5 marksReview of descriptive statistical analysis(Q1)5 marksCorrelation analysis (Q2)5 marksSimple regression analysis (Q3)10 marksCritical review of the findings(Q4)5 marksTOTAL Weight40 Marks

Assessment Feedback to the Student:

Marking Rubric

Excellent Very Good Good Questionable Unsatisfactory Part A: Analysis ofDemonstrate Demonstrate Demonstrate Demonstrate basicDemonstrate pooreconomic dataoutstandingvery goodgood knowledgeknowledge in theknowledge in the knowledge in theknowledge in thein the analysis ofanalysis of data andanalysis of data and analysis of data andanalysis of datadata and thethe review ofthe review of the review ofand the reviewofreview ofinformation derivedinformation derived information derivedinformationinformationfrom chartsfrom charts from chartsderived fromderived from chartscharts Collection of dataDemonstrateDemonstrateDemonstrate goodDemonstrate basicDemonstrate poorset which meetsoutstandingvery goodknowledge inknowledge inknowledge inthe givenknowledge inknowledge incollecting data andcollecting data andcollecting data andguidelinescollecting data andcollecting datapreparing accuratepreparing accuratepreparing accurate preparing accurateand preparingdata sets fordata sets fordata sets for data sets foraccurate dataanalysis.analysis.analysis. analysis.sets for analysis. Review ofDemonstrateDemonstrateDemonstrate goodDemonstrate basicDemonstrate poordescriptiveoutstandingvery goodknowledge inknowledge inknowledge instatisticalknowledge inknowledge instatisticalstatisticalstatistical descriptiveanalysis(Q1)

statistical

descriptive measures.

statistical descriptive

measures.

descriptive

measures.

descriptive

measures.

measures.CorrelationDemonstrateDemonstrate veryDemonstrateDemonstrate basicDemonstrate pooranalysisoutstandinggood knowledgegood knowledgeknowledge in theknowledge in the knowledge in thein the applicationin the applicationapplication ofpresentation of data application ofof correlationof correlationcorrelation analysis.using suitable chart correlation analysis.analysis.analysis. types.Simple regressionDemonstrateDemonstrate very

Demonstrate good knowledge in the application of

regression models

Demonstrate basic knowledge in the application of

regression models

Demonstrate poor knowledge in the application of

regression models

analysis (Q4)outstandinggood knowledge knowledge in thein the application application ofof regression regression modelsmodelsCritical reviewofDemonstrateDemonstrateDemonstrateDemonstrate basicDemonstrate poorthe findingsoutstandingvery goodgood knowledgeknowledge inknowledge in knowledge inknowledge inin understandingunderstanding theunderstanding the understanding theunderstandingthe usefulness ofusefulness ofusefulness of

usefulness of

regression models for predictive purposes.

the usefulness of

regression models for predictive purposes.

regression models

for predictive purposes.

regression models

for predictive purposes.

regression models

for predictive purposes.

Your final submission is due Wednesday of Week 9 before midnight.

The following penalties will apply:

1. Late submissions -5% per day.
2. No cover sheet OR inaccuracies on the cover sheet -10%
3. No title page -10%
4. Inaccuracies in referencing OR incomplete referencing OR not in Holmes-adapted- Harvard style -10%
5. Appendix missing or incomplete or not in prescribed format -10%

Student Assessment Citation and Referencing Rules

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

1. Reference sources in assignments are limited to sources that provide full-text access to the source’s content for lecturers and markers.

2. The reference list must be located on a separate page at the end of the essay and titled: “References”.

3. 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 (1 to 10, etc.) and each reference MUST include a hyperlink to the full text of the cited reference source.

For example:

1. Hawking, P., McCarthy, B. & Stein, A. 2004. Second Wave ERP Education, Journal of Information Systems Education, Fall, http://jise.org/Volume15/n3/JISEv15n3p327.pdf

4. 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 intelligence strategy (Hawking et al., 2004, p3(4)).”

Non-Adherence to Referencing Rules

Where students do not follow the above rules, penalties apply:

1. For students who submit assignments that do not comply with all aspects of the rules, a 10% penalty will be applied.
2. Students who do not comply with guidelines BUT their citations are ‘fake’ will be reported for academic misconduct

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