| Category | Assignment | Subject | Economics |
|---|---|---|---|
| University | Aston University (AU) | Module Title | BS3397 Microeconometrics |
| Academic Year: | 2025/26 |
| Module Code: | BS3397 |
| Module Name: | Microeconometrics |
| Module Leader: | Tim Burnett |
Coursework Title: 72-hour Take-home Mid-term Assessment
Complete all of the tasks in this assessment (starting page 4). You should use R for all analysis and/or data manipulation. Guidelines for submission/presentation are featured on the final pages.
In addition to the take-home component, there will be a short (10-minute) confirmatory task in-class in week 8:
(How should I use this brief to help with my assessment?)
This brief gives you key information and guidance about your assessment. It’s the starting point for your work, and your module tutors will refer to it during assessment discussions. Read the brief carefully, along with any other materials your tutors provide, and use them to help you plan and complete your assessment.
| Module Code and Title | BS3397 |
| Module/Assessment Lead contact | Tim Burnett |
| Assessment Title | Mid-term Technical task |
| Assessment weighting | 20% |
| Word Count/Page Count/Lengths of presentation/ Fixed Window Assessment Period (in hours/days) | 700 words or less (not including tables, equations, etc.) |
Assessment Mode (What do I need to produce at the end?)
A well-formatted set of answers to the questions you have been set
Task Description (What do I have to do for this assessment?)
Complete the technical task you have been set, answering all questions.
Submission Instructions (When and Where do I submit?)
Submit to submission link on module Blackboard page
Link to Learning Outcomes (How does the assessment help you demonstrate the outcomes of this module?)
This assessment links to all of the LOs for the module (available on Blackboard under module information). It assesses the extent to which you have met the standards expected to pass the module.
Purpose of Assessment (Why am I doing this? How does this link to my future role as an professional in the discipline? What knowledge, concepts or skills should I demonstrate? How might this links to (name of specific PSRB?) )
The skills acquired on this module align with identified skills shortages in the UK workforce and are widely applicable acorss work or further study. Specifically, this assessment tests your ability to autonomously conduct and communicate statistical/quantitative analysis.
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Hire Assignment Helper Now!When and where can I find my grades and feedback?
Staff will aim to return provisional marks for coursework assessments to students within 4 weeks of the submission deadline during term time.
For this module (Formative/Summative) Feedback and/or provisional grade will be released/provided on [Date and time ] via [location in Blackboard]
Any grades that you see outside of My Aston Portal (MAP) should be thought of as provisional. Only final Exam Board approved grades will appear in MAP. Feedback and provisional grades can be accessed in Blackboard for both Turnitin and Blackboard Assignments. [Select one of the following depending on your submission] – For Turnitin assignments, see our guide to accessing your Turnitin feedback. For all other types of assignment, use this Grades guide.
| Can I use AI in my work and how? | |||
| Use of Gen AI is Optional
|
The work you submit, especially the text, should be prepared by yourself, but you may wish to use AI to assist you in your coding.
You should not submit unadulterated AI content or analysis, but may, for example, include generating initial ideas, tweaking commands, summarising notes or research articles, for translation, or creating a study plan. Any application or use of AI should always be acknowledged. Misuse of generative AI, as defined in Aston’s assessment regulations, will be treated as an academic offence and may be subject to disciplinary action. You may find this guidance by The Learning Services team on the effective and ethical use of AI for assessment useful. |
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What further information and advice might help me to complete this task?
All the skills and knowledge you need to excel in this assignment can be found in the lectures or homework activities.
Your work will be marked against the Marking Criteria for BS3397, available in the Assessment and Feedback area on Blackboard.
On BSc Economics programmes, students generally study a range of mathematical or technical modules, which generally build in complexity throughout the degree.
You have been provided with a set of data (in CSV format) concerning student characteristics and performance in the module Econometrics 2, a second-year module which takes place in the Spring term (full data key below).
Some specifics of interest:
You have been asked to conduct some analysis of the data to better understand the determinants of students’ academic performance.
Data Key
‘econometrics grades.csv’
N = 323
| Variable Name | Description |
| midterm_score | Student’s percentage score on the midterm assessment. |
| final_exam_score | Student’s percentage score on the final exam. |
| student_id | Unique identifier for each student (1-323). |
| female | Binary variable indicating student gender (1 = female, 0 = male). |
| international | Binary variable indicating whether the student is an international student (1 = yes, 0 = no). |
| maths_alevel | Binary variable indicating whether the student has Maths A-level qualification (1 = yes, 0 = no). |
| economics_alevel | Binary variable indicating whether the student has Economics A-level qualification (1 = yes, 0 = no). |
| first_gen | Binary variable indicating whether the student is a first-generation university student (1 = yes, 0= no). |
| prerequisite_score | Student’s percentage score in the prerequisite module from Term 1. |
| study_hours_weekly | Average number of hours the student spends studying per week. |
| attendance_rate | Percentage of classes attended (in multiples of 10%, representing 10 total classes). |
| office_hours_visits | Number of times the student visited office hours during the term. |
| study_group | Binary variable indicating whether the student participates in a study group (1 = yes, 0 = no). |
| parttime_hours | Average number of hours per week the student works in part-time employment. |
| birth_month | Month of birth (1 = January, 12 = December). |
| distance_km | Distance in kilometres between the student’s residence and campus. |
| coffee_cups_daily | Average number of cups of coffee consumed per day. |
Average number of minutes spent on social media social_media_minutes per day.
Student’s preferred study location (Library, Home, study_location or Cafe).
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