APME84-Introductory Statistics and Econometrics

Module Provider: School of Agriculture, Policy and Development
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Autumn term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded: APME71 Econometrics
Current from: 2021/2

Module Convenor: Prof Kelvin Balcombe
Email: k.g.balcombe@reading.ac.uk

Type of module:

Summary module description:

Learn how to analyse data using basic tools to answer questions in economics and other social sciences, through a combination of lectures and practical classes. Understand the fundamentals of regression analysis: model specification, hypothesis testing, coefficient interpretation. Learn how to translate data into models to make forecasts and to support decision making in a wide variety of fields, ranging from microeconomics to finance and marketing. The prerequisites for this course are familiarity with elementary mathematics and statistics.

Aims:

This module provides an introduction to two different regression techniques. At the end of this module students should be able to

• translate data into a regression model to make forecasts and to support decision making


• conduct hypothesis testing and interpret results


• handle data sets and use the software Gretl to carry out basic regression analyses


• interpret and critically evaluate regression model outputs

Assessable learning outcomes:

At the end of the modules, students should be able to:

• Understand how basic regression techniques are used to analyse data


• Combine data handling skills and econometric software skills to undertake applied econometric analysis and evaluate and interpret results

Additional outcomes:

Outline content:

1. Probability Theory I


2. Probability Theory II


3. Simple regression Models


4. Multiple Regression Models I


5. Multiple Regression – Application


6. Multiple Regression Models II


7. Single & joint restrictions


8. Hypothesis Testing – p-values


9. Logistic regression


10. Logistic Regression – Application

Brief description of teaching and learning methods:

Lectures will provide an understanding of fundamental concepts and demonstrate the use of data analysis methods. Practical classes will involve students analysing real data sets with a focus on learning the concepts taught in the lectures.

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

• 1 In-class test (20% of final mark,15 minutes, week 7)


• 1 Report (80% of final mark, 1,500 words)

Formative assessment methods:

Penalties for late submission:

The below information applies to students on taught programmes except those on Postgraduate Flexible programmes. Penalties for late submission, and the associated procedures, which apply to Postgraduate Flexible programmes are specified in the policy 􀀓Penalties for late submission for Postgraduate Flexible programmes􀀔, which can be found here: