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: 2019/0

Module Convenor: Dr Ariane Kehlbacher

Email: a.kehlbacher@reading.ac.uk

Type of module:

Summary module description:

This module will provide students with the ability to analyse data using basic tools to answer questions in economics and other social sciences. This module covers the fundamentals of regression analysis: model specification, hypothesis testing, coefficient interpretation. At the end of the module students will be able 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.


Contact hours:
  Autumn Spring Summer
Lectures 16
Tutorials 4
Guided independent study:      
    Wider reading (independent) 15
    Advance preparation for classes 20
    Preparation of practical report 30
    Revision and preparation 5
       
Total hours by term 100 0 0
       
Total hours for module 100

Summative Assessment Methods:
Method Percentage
Report 80
Class test administered by School 20

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:
Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx

Assessment requirements for a pass:

A mark of 50% overall.


Reassessment arrangements:

Coursework assignment


Additional Costs (specified where applicable):

Last updated: 23 May 2019

THE INFORMATION CONTAINED IN THIS MODULE DESCRIPTION DOES NOT FORM ANY PART OF A STUDENT'S CONTRACT.

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