代写BUSA3015 Business Forecasting, Session 2, 2024代写留学生Matlab语言程序

UNIT BUSA3015 Business Forecasting, Session 2, 2024

Assessment Task             Report 1

Due date                           11:59pm Friday 13th September

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 9 of September 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 9th of September.

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 1”.

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

Feedback available (anticipated date)         Week 9 (5 October)

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 1.

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

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.

If you use any Exponential Smoothing Models in this Report, please note the following:

§ For Simple Exponential Smoothing – for the seed of the level use the first observation, Y1.

§ For Holt’s Exponential Smoothing – for the seed of the level use the first observation, Y1. For the seed of the trend – take the difference of the first two observations (Y2 – Y1).

§ For Winters’ Exponential Smoothing – for the seeds of the level, trend, and seasonal components – utilise the methods described and discussed in class.

§ Choose Multiplicative over Additive models where applicable.

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 only one appropriate exponential smoothing model (either simple exponential smoothing or Holt exponential smoothing models that you think is most appropriate given the data). Your starting value for any parameters should be 0.5. Please see the notes on page 5 of this document – regarding seeds.

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

Once you identify and develop an appropriate exponential smoothing model with the starting values for parameter(s)= 0.5, what are the following numerical values:

1. The within-sample forecast for January 2023.

2. The out-of-sample forecast for October 2023.

3. The out-of-sample forecast for June 2024.

4. The MAPE.

5. The MAE.

Critically think for a way to optimise alpha and beta (if there is no beta, you can input ‘0’ for question 7) via the MSE, and report the following values after your optimisation:

6. Alpha.

7. Beta.

8. The MAPE.

9. The within-sample forecast for January 2023.

10. The out-of-sample forecast for June 2024.

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 Winter’s Exponential Smoothing. Your starting value for any parameter should be 0.5. Please see the notes on page 5 of this document – regarding seeds.

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

Once you perform. Winters Exponential Smoothing with alpha, beta and gamma, what are the following numerical values:

11. The seasonal component for May 2023.

12. The out-of-sample forecast for May 2024.

13. The out-of-sample forecast for June 2024.

14. The MAPE.

15. The MAE.

Critically think for a way to optimise alpha, beta, and gamma via the MSE, and report the following values after your optimisation:

16. Alpha

17. Beta

18. Gamma

19. The MAPE

20. The out-of-sample forecast for June 2024.

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 (you may also refer to exercise 1).

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. You will need to critically think about whether you discuss the pre-optimised or post-optimised models.

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. You will need to critically think about whether you discuss the pre-optimised or post-optimised models.

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, 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)