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UNIT BUSA3015 Business Forecasting, Session 2, 2024

Assessment Task Report 2

Due date 11:59pm Friday 25th October

Weight (%) 20%

Task description Individual assessment

Submission Method

The submission of this assessment requires:

§ A numerical submission for Exercise 1 and Exercise 2 via an iLearn quiz tool.

§ A written submission for Exercise 3 via a PDF submission through Turn-It-In.

§ An Excel file submission using an iLearn submission link.

The main tables, charts and results should be presented throughout the report to highlight your responses to the questions. There is no need for an appendix.

For the numerical submission: an online quiz tool will be available on iLearn from the 21st of October where you can type in your numerical answers. All answers are to be rounded to 2 decimal places.

For the written submission: 750 words (+/- 10%) not counting labels and numbers on graphs AND no more than four A4 sheets in portrait/vertical mode (use the template DOC file provided on iLearn). A Turn-It-In submission link will be available on iLearn from the 21st of October.

Consider the guidelines above as an authentic assessment that mirrors actual business practice of documentation guidelines when preparing applications for jobs or business tenders.

There will be a deduction of 10% of the total available marks for every page above 4 pages and/or every 50 words above 863 words.

There will be a deduction of 5% of the total available marks made from the total awarded mark for each 24-hour period, or part thereof, that the submission is late (for example, 25 hours late in submission – 10% penalty). This penalty does not apply for cases in which an application for Special Consideration is made and approved.

For the written part of this assignment – the answers must be typed on the pre-formatted DOC file that has been uploaded on iLearn. Convert your DOC file into a PDF prior to submission. You will also have to upload your XLS file through iLearn. Only the PDF file will be marked, the XLS file will not be marked.

Please do not alter the formatting of the pre-formatted DOC file:

§ Do not change the font size.

§ Do not change the line spacing.

§ Do not change the paragraph settings.

§ Do not change the page margins.

§ Do not change the headers or footers.

§ Do not edit or delete the questions.

§ Do not edit any other component of the file apart from typing your answers and cutting and pasting relevant output.

As per the pre-formatted DOC file on iLearn, your answers will be in Times New Roman font, size 11. The answers are to be in black font.

Not adhering to the above will results in a penalisation of marks. This includes a 10% penalty per page over the limit. A critical thinking skill is about making judgments about the information that is relevant and can be presented in an efficient and effective way.

If you want relevant output to be marked, you can cut-and-paste relevant output into these pages. Any pages including appendices (beyond the required 4 pages) will not be marked.

Do not use appendices, all relevant output from Excel or Minitab must be included within the body, within your answer. Appendices will not be marked.

All questions about the assignment must be via the iLearn “Q&A discussion forum for report 2”.

Feedback mechanism(s) Rubric (iLearn Turn-it-in)

Feedback available (anticipated date) Week 14 (6th or 7th November)

Links to Unit Learning Outcomes ULO2, ULO3, ULO4

ASSESSMENT DESCRIPTION

The Case Study

You have been appointed as a consultant for the Business Council of Australia. Given the current economic conditions in Australia they would like to forecast Total Turnover for Department Stores.

As part of your role in the Business Analytics and Data Analytics team, you have been asked to forecast ‘Turnover ; Total (State) ; Department stores’, as part of a wider report being commissioned by the Business Council of Australia. Your role requires you to follow the “Assessment Instructions” in the next page and complete Report 2.

Skills in focus for this assessment

• Critical Thinking and Problem Solving

• Data, Information, and Digital Skills

• Discipline Knowledge - Business Analytics

ASSESSMENT INSTRUCTIONS

The Case Study

IMPORTANT NOTE: You are to use the exact same data sets that you used in Report 1. You do not need to re-download the data. If your data is lost, the instructions for obtaining it are repeated (from Report 1) below:

Questions

§ Obtain the ABS statistics for Retail Trade, Australia – 8501.0 – available at: https://www.abs.gov.au/statistics/industry/retail-and-wholesale-trade/retail-trade-australia/jun-2024#data-downloads

§ Download Table 1.

§ For the purposes of this report you are to consider the ‘Turnover ; Total (State) ; Department stores’ data. There are three series in Table 1: Original, Seasonally-adjusted, and Trend (please choose carefully throughout this report!)

§ For the purposes of this report, only consider the data from July 2015 to June 2023 as the sample of data that is available to you – that is, ignore any recent observations.

§ This means that the first actual observation in your Excel file is from July 2015 and your last actual observation in your Excel file is from June 2023.

§ Use Excel and no other statistical software for the purposes of this report.

§ You may use Minitab for constructing correlograms.

This report will require two separate submissions.

The numerical responses need to be submitted via a quiz tool in iLearn.

The written responses need to be submitted via a PDF uploaded via Turn-It-In in

iLearn. Instances of plagiarism will be dealt with according to the relevant policies and procedures.

Exercise 1 – Application (10 marks)

This exercise involves numerical responses to be submitted via a quiz tool on iLearn

For the purposes of this report, only consider the data from July 2015 to June 2023 as the sample of data that is available to you – that is, ignore any recent observations. This means that the first actual observation in your Excel file is from July 2015 and your last actual observation in your Excel file is from June 2023.

For the Seasonally-adjusted data for the Turnover ; Total (State) ; Department stores (Series ID: A3348621L) available in Table 1: Retail Turnover, By Industry Group: Forecast the out-of-sample values for every month in the period July 2023 – June 2024 (both months inclusive) using a simple linear regression with an intercept, and time (where time (t) = 1, 2, 3,… ) as the explanatory variable.

Before you begin Exercise 1, let’s check that you have the right data! The average should be 1617!

Once you perform this simple linear regression model, what are the following numerical values:

1. The value of the intercept.

2. The coefficient of time.

3. The value of R2 .

4. The standard error of the regression.

5. The MSE of the regression.

6. The p-value for the coefficient of time.

7. The t-stat for the coefficient of time.

8. The within-sample forecast for January 2023.

9. The out-of-sample forecast for July 2023.

10. The total observations in regression statistics table.

Exercise 2 – Application (10 marks)

This exercise involves numerical responses to be submitted via a quiz tool on iLearn

For the purposes of this report, only consider the data from July 2015 to June 2023 as the sample of data that is available to you – that is, ignore any recent observations. This means that the first actual observation in your Excel file is from July 2015 and your last actual observation in your Excel file is from June 2023.

For the Original-adjusted data for the Turnover ; Total (State) ; Department stores (Series ID: A3348618X) available in Table 1: Retail Turnover, By Industry Group: Forecast the out-of-sample values for every month in the period July 2023 – June 2024 (both months inclusive) using a multiple linear regression with an intercept, time as an explanatory variable, and 11 dummy variables for all months except January (Note: January is the base month).

Before you begin Exercise 2, let’s check that you have the right data! The average should be 1620!

Once you perform the multiple linear regression model, what are the following numerical values:

11. The value of the intercept.

12. The coefficient of time.

13. The value of R2 .

14. The standard error of the regression

15. The t-statistic for the coefficient of time.

16. The p value of the coefficient of time.

17. The within-sample forecast for June 2022.

18. The out-of-sample forecast for January 2024.

19. The Degrees of Freedom for residuals from the ANOVA table.

20. The F statistic in the ANOVA table (not the ‘F significance’!).

Exercise 3 (60 marks)

This exercise requires written responses submitted via a PDF upload via Turn-It-In in iLearn.

You are expected to generate a written report using 750 words (+/- 10%) not counting labels and numbers on graphs AND no more than four A4 sheets in portrait/vertical mode (use the template DOC file provided on iLearn):

Your Exercise 3 responses should refer mostly to Exercise 2.

For the model in Exercise 2, given that you have the actual data for the out-of-sample period (you considered the within-sample period to end in June 2023 – but you do have data for July 2023 and onwards) – discuss your forecasting method, your forecasts, and the business insights from these, using the following steps:

§ Attribution (5 marks)

§ Scope (5 marks)

§ Application (5 marks)

§ Analysis (10 marks)

§ Articulation of Issues (10 marks)

§ Critique (15 marks)

§ Position (10 marks)

You must use the above steps as sub-headings in your response. Failure to do so will result in a loss of marks.

Note in the rubric on iLearn – "sources" are from within the assignment including your own sources of generated results. You do not need to cite the materials provided via iLearn. Given the nature of this task, you will not be penalised for not referring to other sources (although other sources may give you unique insights for your responses). However, in your report, you should consider referring to the information provided by the ABS on the site that is used to download the data.

Pointers

For each of these sub-headings below, at least consider the notes that follow (you can consider more!). If you use a generative artificial intelligence (AI) tool (such as ChatGPT or similar), without citing the source, you will be penalised for violating academic integrity. As we have around 400 students in the unit, you also run the risk of plagiarism against other students by using such tools.

If you wish, you may include screenshot/s of any such AI response, and then showcase your own response (in typed words) which exhibits your critical thinking where you have modified the AI response to display higher-level thinking skills in line with the unit’s learning outcomes.

Attribution – Consider the marking rubric.(Do not write the reference list rather include a paragraph clearly mentioning data source information)

Scope – Explain the model in Exercise 2 by using language that is understood by a non-technical audience.

Application - Describe and explain how you applied the data and your knowledge to perform the forecasts in Exercise 2. Describe and explain using language that is understood by a technical audience.

Analysis – Consider the marking rubric, to assist you, you should include:

• A plot of the considered sample (July 2015 to June 2023) and the forecasts (within and out-of-sample) on one chart.

• You will need to critically think about whether you plot the pre-optimised or post-optimised models. A description of the chart and an analysis of your forecast is expected.

• Another plot of the actual data that is beyond the considered sample (July 2023 to June 2024) and the forecasts.

• A description of the chart and an analysis your forecast.

Articulation of Issues – Consider the marking rubric, to assist you, you should: Perform the appropriate check/s and test/s to check the validity of your model– provide some of this evidence.

What are the issues based on your check/s and test/s above?

Note: we have discussed and conducted several check/s and test/s when we are forecasting in this unit – and it is up to you to determine which checks and tests are appropriate – to determine issues, if any.

Critique – Consider the marking rubric, to assist you, you should: Critically evaluate your model 2 on its own merits, and critically evaluate the factors you would need to consider when forecasting in light of recent events.

Compare and contrast alternative models.

In the context of business forecasting, critically think and discuss any other considerations that need to be taken into account for your forecasts / forecasting to be useful for business purposes.

Position – Consider the marking rubric, to assist you, you should consider: This is an informed and justified conclusion that draws upon your discussion above. Given all of your discussion/s above, state your position regarding the business insights to be obtained by your forecasts, by referring to the evidence and ideas that you have discussed above.

Exercise 1 (10 marks) + Exercise 2 (10 marks) + Exercise 3 (60 marks) = Report 1 (80 marks)