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Module Code and Title |
RE3QTS, Quantitative Techiques and Statisitics |
Academic Year |
3 |
Module Convenor |
Dr Ren Ren |
Type of Assessment |
Project |
Weighting of Assessment |
100% |
Individual or Group Assessment |
☒ Individual ☐ Group |
Module Convenor Office Hours/Opportunities for advice and feedback |
Office hours: Tuesday 9-10 am; Friday 3-4 pm.
Office: HBS 129
Discussion forums on BB
Email: [email protected]
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1. Submission details
Submission deadline |
13 Dec 2024, Friday at 2 pm |
Submission point |
☐ Blackboard ☒ Turnitin ☐ Other: Enter text here |
Item(s) to be submitted |
One report |
File type |
☐ PDF ☒ Word ☐ PPT ☐ Excel ☐ Video
☐ Other: Enter text here
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Formatting guidelines |
One MS Word file |
Structure (e.g. required subsections)
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You may follow the report structure below.
1. Introduction: a summary of research goals, hypotheses, methods, findings.
2. Data Description: justification of variable selection, data sources, data descriptions (e.g., tables and graphs).
3. Cross-sectional OLS Regression: model specification, regression results, interpretation of the results, connection to the hypotheses and limitations.
4. Pooled OLS Regression: model specification, regression results, interpretation of the results, connection to the hypotheses and limitations.
5. Conclusion: the implications of the research project (who can learn and benefit and how)
6. Reference lists (e.g., data sources).
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Size of assessment (word limit or length) and penalty applied |
Word limits of 3000 words, excluding reference lists. Tables and figures are presented in reader-friendly screenshots, which makes the report equivalent to 4000 words. |
Referencing style |
☒ Harvard ☐ Other: Enter text here |
2. What is the purpose of this assessment?
Module learning outcomes to be assessed |
• Describe data and analysis results. |
• Develop, implement, and test simple econometric models. |
• Apply quantitative methods for real estate data analysis. |
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3. What is the task for this assessment?
Task (attach an assignment brief if required) |
You are interested in investigating the share of green space (the independent variable of interest) on the average of housing prices (the dependent variable) of wards in London. You are going to write a report to describe and discuss your hypotheses, data, regression models and results. Three tasks are the guidance to help you to complete this research project.
Task 1: Data Description (25%)
Apart from the share of green space, the variable of interest,
please find two other variables which you believe can also
explain the average housing prices of wards in London.
Present justification of the variable selections, summary of
statistics and graphs.
Task 2: Cross-sectional OLS Regression (25%) Perform an OLS regression using the average housing prices in December 2017. Present and interpretate the regression results. Apply the results to comment on your hypotheses. Discuss the limitation of the methods, including but not limited to applying appropriate diagnostic tests.
Task 3: Pooled OLS Regression (25%)
Perform a Pooled OLS regression using the average housing prices in December 2000 and December 2010. The research goal is to know whether the impact of the share of green space on the average housing price change between the ten years. (Hint: You will need to create a time dummy variable to differentiate data from the two year groups, and design a proper model which enable you to answer the question about time-varying effect). The remaining 25% is for the professional rewriting. |
4. What is required of me in this assessment?
Guidelines/details of how to prepare your submission |
Do the three tasks one by one |
Expectations for group work (if applicable) |
N/A |
Self-regulation: Make sure that you… |
Start early to make use of the drop-in sections. |
Three key pieces of advice based on the feedback given to the previous cohort who completed this assignment |
1. Attend lectures and seminars. All techniques are taught and practised in class. The classroom is the best place to help with your study and assignments. Asking questions in or after class will save you lots of time. 2. Search for open data and try them out in the models. The results do not have to be significant. But it is just better to show results really matter. 3. Pay attention to the writing and presentation. Screenshots are fine, but they need to be reader friendly. Markers are not expected to zoom in or read flurry screenshots. In that case, marks will be deducted. |
Formative assessment opportunities/activities
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In class exercises |
5. What resources might I use to prepare my work?
Lectures and seminar notes, datasets provided on BB and other open data
6. Academic integrity
Guidance on academic misconduct (including using Turnitin practice area) |
The work you produce must be your own or that of members of your group if it is a group assessment. It must not have been submitted as part of other assessments, at this or another institution. You should ensure that the work you produce adheres to the University’s statement on academic integrity and to the regulations regarding academic misconduct (such as plagiarism and cheating). You can find information about this at: University of Reading Academic misconduct policies. You are encouraged to put a draft of your work through the Turnitin practice area to satisfy yourself that the work is your own original work. You can seek advice from the Module Convenor.
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Use of Generative Artificial Intelligence (GenAI) tools *What do we mean by GenAI? GenAI tools create new content in the form of text, code, images, videos, music etc. The most common GenAI tools at the time of writing are ChatGPT, CoPilot, Claude, Llama and Gemini. Please note that this list is not exhaustive. Please refer to the article here which frequently updates its list of well-known GenAI tools. |
There are three categories of GenAI use in assessments in operation at the University of Reading. The ticked box below indicates which category this assignment belongs to:
☐ 1. GenAI tools cannot be used in any way in the assessment
☒ 2. GenAI tools can be used to support student learning and development (to sketch initial ideas, find sources, explore unfamiliar concepts, provide structure etc.)
☐ 3. The use of GenAI tools is actively encouraged to help students develop their skills in the use of such tools and understand how their use can be incorporated into authentic writing tasks. Specifically: Enter text here If option 2. or 3. above is checked, you must include a statement to acknowledge your use of GenAI tools* within the assessment itself. This statement should be written in complete sentences and include the following information:
Name and version of the GenAI tool used; e.g. ChatGPT-3.5
Publisher (company that provides the GenAI system); e.g. OpenAI
URL of the AI tool (if applicable)
Brief description (single sentence) of the way in which the GenAI tool was used
Confirmation that the work is your own
For example: I acknowledge the use of ChatGPT 3.5 (OpenAI, https://chat.openai.com/) to generate an outline for background study. I confirm that no output generated by GenAI has been presented as my own work. Note: if you have not used GenAI tools to help with your assessment, you must still include a statement to acknowledge this fact, e.g. I declare that no GenAI tools have been used to produce this work. The misuse of GenAI tools, including the failure to appropriately acknowledge the use of such tools, is considered academic misconduct and carries sanctions, as detailed in the Assessment Handbook. Please also refer to student guidance on Using Generative AI Tools at University and GenAI and University Study |
Academic misconduct penalties
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☒ The University’s standard policy on academic misconduct applies.
☐ Other: Enter text here
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7. Late Submission Arrangements
Late submission point |
☐ Blackboard ☒ Turnitin ☐ Other: Enter text here |
Late submission penalties |
☒ The University’s standard penalties apply. See Standard Penalties.
☐ Other: Enter text here
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8. Feedback Arrangements
Timing of feedback |
☒ Within 15 days of submission deadline
☐ When examination marks are released
☐ Other: Enter text here
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Type of feedback |
☒ Mark ☒ Generic feedback
☒ Individual feedback ☐ Breakdown of mark
☐ Audio feedback ☐ Video feedback
☐ Comments written on the submitted work
☐ Other: Enter text here
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Location of feedback |
☐ Blackboard ☒ Turnitin ☐ Other: Enter text here |
9. Assessment criteria rubric for marking
Assessment criteria |
80+ Outstanding
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70–79 Excellent
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60–69 Very good
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50–59 Good
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49 and below Satisfactory
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39 and below |
Data Description 25%
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Outstanding effort of data collection, along with outstanding
evidence of understanding of the data based on statistics derived
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Great effort of data collection, along with excellent understanding of the data based on statistics derived from the data
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Very good effort of data collection, along with very good understanding of the data based on statistics derived from the data
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Good effort of data collection, along with sound understanding of the data based on statistics derived from the data
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Some effort of data collection, along with satisfactory evidence of understanding of the data
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Little effort of data collection, along with limited understanding of the data
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Cross -sectional OLS 25%
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Model specifications, regression results, interpretations and limitations are all correctly and clearly discussed.
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Model specifications, regression results, interpretations and limitations are all correctly discussed. There might be minor issues in th clarity.
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Model specifications, regression results, interpretations and limitations are mostly correctly and clearly discussed. There might be mirror errors or issues.
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Model specifications, regression results, interpretations and limitations are mostly correctly and clearly discussed. There might be a few errors or issues.
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There is clear weakness in one or two of the followings: model specifications, regression results, interpretations and limitation discussion.
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There is clear weakness in three or more of the followings: model specifications, regression results, interpretations and limitation discussion.
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Pooled OLS 25% |
Model specifications, regression results, interpretations and limitations are all correctly and clearly discussed.
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Model specifications, regression results, interpretations and limitations are all correctly discussed. There might be minor issues in the clarity.
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Model specifications, regression results, interpretations and limitations are mostly correctly and clearly discussed. There might be mirror errors or issues.
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Model specifications, regression results, interpretations and limitations are mostly correctly and clearly discussed. There might be a few errors or issues.
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There is clear weakness in one or two of the followings: model specifications, regression results, interpretations and limitation discussion.
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There is clear weakness in three or more of the followings: model specifications, regression results,
interpretations and limitation discussion.
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Structure and
presentation 25%
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Outstanding writing that makes the report ready for publish. Excellent level of presentation with strong attention to detail. Clear, comprehensive and accurate referencing throughout. |
Excellent writing. Excellent level of presentation with strong attention to detail. Clear, comprehensive and accurate referencing throughout. |
Well-structured writing that conforms to brief. Good presentation and easy to follow. Clear and accurate referencing, perhaps with negligible or minor errors. |
Reasonably structured writing largely conforms to the brief. Acceptable presentation, but there are issues with formatting / appearance. |
Reasonably structured writing largely conforms to the brief. Acceptable presentation, but there are issues with formatting / appearance.
There may be errors in the completeness or accuracy of references.
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Structure does not conform to brief or is otherwise unclear. Significant issues with formatting and appearance of the report. References are absent or are incomplete and inaccurate w heregiven. |