COMM5000 Data Literacy Vinho Verde Final Report

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COMM5000 Data Literacy Vinho Verde Final Report

Assessment Summary

Assessment Task
 Weighting
 Due Date*
Course Learning Outcomes
Milestone 1: Case Study Preliminary Insight Development
 20%
 Week 4
 1, 2
Milestone 2: Case Study project proposal: hypothesis tests
 20%
 Week 7 (Friday 5PM) – 25 October
 1, 2, 3, 4
Smarthinking/Studiosity/Feedback Hub feedback (formative task)
 0%
 Week 9 (Thursday 5PM) – 7 November
Case Study business report
 60%
 Week 10 (Friday 5PM) – 15 November
 2, 3, 4, 5




* Due dates are set at Australian Eastern Standard/Daylight Time (AEST/AEDT). If you are located in a different time-zone, you can use the time and date converter.

Assessment Administrative Details
Turnitin
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If you are instructed to submit your assessment via Turnitin, you will find the link to the Turnitin submission in your Moodle course site. You can submit your assessment well before the deadline and use the Similarity Report to improve your academic writing skills before submitting your final version.

You can find out more information on the Turnitin information site for students.
Late Submissions

The parameters for late submissions are outlined in the UNSW Assessment Implementation Procedure. For COMM5000, if you submit your assessments after the due date, you will incur penalties for late submission unless you have Special Consideration (see below). Late submission is 5% per day (including weekends), calculated from the marks allocated to that assessment (not your grade). Assessments will not be accepted more than 5 days late.

Extensions

You are expected to manage your time to meet assessment due dates. If you do require an extension to your assessment, please make a request as early as possible before the due date via the special consideration portal on myUNSW (My Student profile > Special Consideration). You can find more information on Special Consideration and the application process below. Lecturers and tutors do not have the ability to grant extensions.

Smarthinking/Studiosity/Feedback Hub

The Feedback Hub at UNSW is a valuable resource for students looking to improve their writing skills. It offers personalized feedback on drafts of essays, reports, and literature reviews, focusing on structure, grammar, referencing, and language choice. The service is free for all UNSW students and is available 24/7, ensuring help is always accessible. Students can expect comprehensive feedback within 24 hours, making it a quick and efficient way to enhance their writing. For more details, visit the Feedback Hub website.

Please keep the following in mind when submitting your drafts to Studiosity:
• Make sure you click on the Studiosity link in the Moodle course (circled below in red) and NOT on the side bar (highlighted in yellow)
• If you receive an error message indicating that Studiosity in unable to connect, please use a different browser from the one that you are using.
• If you have any technical issues, please contact Studiosity directly on [email protected] or for a faster response, visit their website: https://www.studiosity.com/ and use the chat box (yellow icon on the bottom right) to report your issue.UNSW Business School
Special Consideration
Special consideration is the process for assessing the impact of short-term events beyond your control (exceptional circumstances), on your performance in a specific assessment task.

What are circumstances beyond my control?

These are exceptional circumstances or situations that may:

• Prevent you from completing a course requirement,
• Keep you from attending an assessment,
• Stop you from submitting an assessment,
• Significantly affect your assessment performance.

Available here is a list of circumstances that may be beyond your control. This is only a list of examples, and your exact circumstances may not be listed.

You can find more detail and the application form on the Special Consideration site, or in the UNSW Special

Consideration Application and Assessment Information for Students.

CASESTUDYINFORMATION—Vinho Verde

Business context: In recent years, the growing interest in wine has fuelled the expansion of the wine industry. As a result, companies are investing in new technologies to enhance both wine production and sales. Quality certification plays a vital role in these processes and currently relies heavily on wine tasting by human experts.

Case/Scenario: You consult a winery and help this company to predict or estimate human wine taste preferences at the certification step. Knowing the wine quality will allow the winery to be better positioned to predict available amounts and yearly sales. It will also support the oenologist wine tasting evaluations by potentially improving the quality and speed of their decisions, and improve wine production. Furthermore, similar techniques can help in target marketing by modelling consumer tastes from niche markets. In order to predict wine quality you will use a dataset consisting of 4898 white and 1599 red vinho verde samples from Portugal's northwest region, and the statistical methods covered in this course.UNSW Business School

FINAL REPORT: Case Study Project Proposal
Report details
Deadline as per terms above
60%
Report: Maximum 2000 Words (excluding tables, charts, etc). This is individual work. Reports will be checked for plagiarism.
Via Moodle course site

Description of Final Report task

In M1, students investigated all the variables included in the dataset. M2 used hypothesis testing to explore and further investigate from a more quantitative perspective some of the patterns students may have observed in their M1 analysis. In M2 students were asked to study whether there are statistically significant differences in alcohol content and wine quality, when considering the two wine types: red and white.

Hypothesis testing is very informative, but does not provide us with a means to control for confounding effects. For example, suppose you conclude that you reject a null hypothesis of equal population means of alcohol contentbetween wine types. In that case, you can’t conclude that this difference is solely due to  alcohol content. Other factors may affect alcohol content even if you control for wine type. In other words, rejecting the null hypothesis here is driven by factors other than differences in wine type.

In this Final Report, students will use the information you have collected in your analysis in M1 and M2 to build a linear regression model. This model will capture the key objectives we have been pursuing since M1.
(1) We want to investigate what wine characteristics, if any, can explain/predict its quality.
(2) We also want to know if wine type has an effect on our variables.

To provide a ceteris paribus analysis, students are asked to use a regression model to identify and estimate the wine type effect (if any) on wine quality, while controlling for other factors. Other factors are defined by the data included in the Vinho Verde dataset. No variables outside of the provided Vinho Verde dataset are to be considered.

This assessment will be marked by the teaching team. Academic Integrity violations will not be tolerated in any shape or form.

Data Considerations

In your Final Report, your dependent variable (Y) will be wine quality, from your dataset. The independent variables (X) will be one of more other variables from the dataset, and must include a dummy for wine type, when appropriate for the model specification.

Statistical Analysis Required for the Final Report

For the Final Report you need to run a number of models. More specifically:
1) Model_White_Wine: Consider all the variables included in the dataset but limiting your observations to only the white wines. How do you select which X variables go into the regression? One simple way is to start by putting all the variables in the dataset in the regression. You can follow this procedure:
(i) Estimate the model and use the estimation results to remove all the variables that are not statistically significant.
(ii) Repeat the regression with these significant variables and check if all are statistically significant.
(iii) Repeat this process until you get a model where all regressors are significant.
2) Model_Red_Wine: Same as above, but you are limiting your analysis to only the observations that refer to the red wines.
3) Pooled_Model, for both wine types: Repeat the same process, this time including all of the observations in the dataset, that is, including both white and red wines. To study the effect of wine type, you can create a dummy variable.

You can use a table similar to Table 1 for summarising the results of the three regression models (Model_White_Wine, Model_Red_Wine, Pooled_Model).

Using the results from the three estimated regression models, discuss what the explanatory variables - or determinant factors - for wine quality are, if any. More specifically:

• Consider the determinant factors (if any) in the Model_White_Wine and interpret the meaning of their estimated coefficients. What is the effect of a unit change of these factors on the wine quality, variable Y? If no determinant factors are found, discuss what the issues driving this finding could be.
• Is there a change in the factors determining Y when you compare it to the Model_Red_Wine results? Explain. If no determinant factors are found, discuss what the issues driving this finding could be.
• Lastly, what is the estimated effect of wine type as captured by the dummy variable in the Pooled_Model? Interpret the coefficient of the dummy variable you used for wine type.

Ethics Considerations

Considering what you have learnt in Week 8, are there any ethical considerations around data analysis and use/implementation of the report’s findings that the winery you are consulting for should be made aware of? Can you recommend to the winery how to make ethically informed decisions related to your report's statistical analysis and results?

Report Structure

The final report unifies the insights you have gathered in M1 and M2 and this final analysis. It showcases all the statistical techniques you have learnt in COMM5000. This report is a final take on this problem and should include the key results you wish the winery to get from the analysis. Your report will consist of the following core components. You can restructure the section as you see fit for your report.

  • Executive summary This is the punch line of this case study. This should tell the winery what you have found in clear and precise language. This should not be technical. It should clearly address the relevance of the findings for the winery, the usefulness, or lack thereof, of the statistical findings.
  • Introduction: The introduction should present the problem analysed in this case study. The background and the expected outcomes or target questions of the analysis. Your introduction should include some key conclusions from M1 or M2 and how these conclusions provide some basis for developing the model for wholesale company profit characteristics. This is not a copy/paste of all your reports for the first two milestones. It should report the take-home message(s) from the detailed analysis you have completed—no need to re- report the tables and graphs from those milestones.
  • Section 1: Data and analytical considerations, any major limitations in and approach to the data or analysis performed. This will inform the reader about the kind of approach that you have taken.
  • Section 2: Analysis of the determinants of wine quality, including your analysis of the wine type effect. Make sure you correctly identify the unit of measurement when you discuss coefficients. If not statistical relationships are detected, make sure you check the statistical significance of the overall model via a discussion of the F-test. This is where you would include Table 1 with the results of the three models. Remember to discuss not only statistical significance, but also the size of the coefficients.
  • Section 3: Robustness analysis and model limitations. Any statistical analysis is based on assumptions that ensure that the inference you perform, and the estimation of the model parameters is also correct. SomeUNSW Business School points to discuss in your analysis are the key assumptions:
  • Zero conditional means: does your model satisfies the no omitted variable bias condition? This will ensure that no confounding factors will bias the estimates of your key target parameters in the model. A brief explanation of what this assumption implies for the context of the model(s) you are estimating. Whether you believe it holds and why? What can be done to ensure it is satisfied if you had more data and more time to develop the model? If it isn’t satisfied, what are the implications on the model estimation and inference?
  • Multicollinearity: With the selected set of regressors in your models, check whether the regressors satisfy the assumption of no perfect collinearity and/or no large (multi)collinearity. Explain what it means in your context and the implications of the regressors failing the condition on the validity of the inference.
• Conclusions. In this section, you should summarise the main findings of your analysis. No new information should be added here, just the highlights of your analysis, with a focus on the usefulness of your work for the winery.

General advice

- For each section, you should summarise the key points and state briefly the statistical evidence/argument that supports those key points. Be concise, but be precise with your language.
- Include Excel regression outputs and any other data analysis output to the main body of the report, so as to facilitate the marking of the report. Keep the body of the report flowing with only some storytelling charts that must be included in the body.

Submission instructions

- Sign the UNSW declaration form andattachto yourreport
- Cheating and any other academic integrity violation will be investigated and treated as per University policy
- Submit via link on the course Moodle siteUNSW Business School

Marking Rubric

Final Report: See Final Report Rubric COMM5000.xls sheet on Moodle.

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