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Provide a summary of the key variables, to include both descriptive statistics and visualisations.


Task Details/Description:


Complete all of the following tasks, using R where called-for:


You have been provided with a dataset (in Stata .dta format) which considers household water bills and a range of other variables (dataset description attached at the end of this document):


1) Provide a summary of the key variables, to include both descriptive statistics and visualisations. You should briefly explain why you believe the variables you are summarising are 'key' and ensure that you use visualisations appropriate to the type of data you are describing. (200 words max, 20 marks)


2) Specify and estimate an OLS regression model which estimates the relationship between the dependent bill variable (bill) and a selection of relevant independent variables? Briefly explain and justify your choice of included independent variables (providing any evidence you see fit) and demonstrate (by your chosen means) that your chosen specification is a superior model to alternatives (250 words max, 30 marks)


3) Present your results in a well-formatted table and provide a short, written, interpretation of your key estimated regression coefficients, whether the results match your expectations, and the implications of your model for household water bills (250 words max, 30 marks)


4) Do you believe your model demonstrate a causal relationship and that causal effects can be measured? Provide a short written explanation to justify your belief (include any diagrams you feel relevant) (Hint: what type






of problems would invalidate the identification and unbiased measurement of causal effects?) (200 words max, 20 marks)


The key to success in this assignment is to demonstrate informed judgement, that you can justify your choices, and that you can demonstrate that you understand the econometrics you are executing and the results that you elicit. Presentation is important, so ensure that your formulae, tables, and diagrams are properly presented, and that you follow the rest of the submission guidelines .



Module Learning Outcomes Assessed:


This assessment covers both technical and theoretical material from the first part of the course. It partially assesses all learning outcomes associated with the module:

1. Manage and describe economic data and datasets from a range of


2. Demonstrate and communicate a conceptual understanding of a range of econometric issues and approaches relevant to microeconomic data

3. Demonstrate the ability to appropriately select, and use computer software to implement, a range of econometric approaches dependent on context and suitability

4. Critically interpret microeconometric results, to demonstrate the ability to understand their implications, and to relate them to broader real- world settings



Presentation Requirements:


Submission guidelines:

Your work should be submitted electronically and should consist of a single document in two parts:

1) Your written responses to the questions, including numbered and properly

formatted tables and any figures/graphs you produce

2) The R script used to generate your answers. Ensure this is complete, tidy, and free of superfluous commands or draft experimentation with commands. It should be in such a format that (barring changing source directory) I can run it myself.


General guidelines:

This is a final-year assessment and, as such, it is expected that you uphold appropriate standards of presentation:






Graphs and tables should be produced using computer software

Formulae and equations should be produced using an appropriate equation editor with correct subscripts and accents

Language, descriptions, and explanations should be formal and should not resort to simplistic or childlike exposition—students will be penalised for misuse of statistical terms

Short written sections should be concise and should be no more than 200 words each.

Text should be written in a non-serif font (Calibri, Arial, Helvetica), size 11,

1.5 line spacing.

R output is acceptable for reporting of regression output, but please ensure it is formatted in such a way that it is legible.

It is expected that your work is legible, written in formal academic language, and spelling and grammar checked.


Academic integrity

It is expected that you uphold the expected standards of academic integrity:

You produce your own work.

You do not plagiarise and that any sources you use in your answer are properly acknowledged in a properly formatted bibliography (a list of sources relied upon, not just a list of sources specifically cited).

This is individual assessed work: conversations and collaboration with colleagues is kept at a general level and you do not share or generate specific answers to questions with one another.

Failure to adhere to guidelines or standards of academic integrity will result in a

lower mark.


Submission Date & Time:


Assessment available 2pm, Tuesday 1st November 2022 Assessment due 2pm, Friday 4th November 2022 Assessment Weighting for the Module:



Assessment Criteria

This module is assessed via: Coursework (20%)

Final take-home exam (80%)






Both assessment tasks will require a combination of theoretical and conceptual knowledge on the topics we have covered during the course (up to that point), and on the technical requirements to analyse data using the software package R.


This coursework therefore accounts for 20% of the final grade for the module. You will receive feedback on your work which will help you when it comes to the final take-home assessment.



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