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BS3397 Microeconometrics CW Assignment Brief 2026 | Aston University

Request Plagiarism Free Answer Published: 02 Jan, 2026
Category Assignment Subject Economics
University Aston University (AU) Module Title BS3397 Microeconometrics

BS3397 Coursework Assignment Brief

Academic Year: 2025/26
Module Code: BS3397
Module Name: Microeconometrics
Module Leader: Tim Burnett 

Coursework Title:  72-hour Take-home Mid-term Assessment

Task Details/Description: 

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:

  • This take-home component is worth 2/3 of the grade you will receive for this assessment
  • The linked in-class confirmatory task is worth 1/3 of the grade you will receive for this assessment

BS3397 Assessment Brief And Assessment Task Continue Overleaf

Assessment Brief  

Purpose

(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. 

1.0 Essential Information

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.)

2.0 Assessment Overview  

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

3.0 Purpose Of Assessment

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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4.0 Feedback Expectations

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.

5.0 Guidance On AI Usage 

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.

 

6.0 Assessment Top Tips 

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.

7.0 Assessment Criteria And Rubrics

What will my work be marked against? 

Your work will be marked against the Marking Criteria for BS3397, available in the Assessment and Feedback area on Blackboard.

Task Details/Description: 

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:

  • The module has a pre-requisite (Econometrics 1), which must be studied in the Autumn.
  • Assessment for the module takes the form of a mid-term technical essay-based assessment (worth 30% of the marks for module) and a final in-person exam, worth the remaining 70%.

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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