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Module

ECO1007 : Statistical Methods for Economics

  • Offered for Year: 2024/25
  • Module Leader(s): Professor John Wildman
  • Lecturer: Dr Diego Zambiasi, Dr Ian Corrick
  • Owning School: Newcastle University Business School
  • Teaching Location: Newcastle City Campus
Semesters

Your programme is made up of credits, the total differs on programme to programme.

Semester 1 Credit Value: 10
Semester 2 Credit Value: 10
ECTS Credits: 10.0
European Credit Transfer System

Aims

To develop an understanding of basic probability theory, so that decision making under uncertainty can be analysed.
To develop an understanding of statistical inference as a foundation for applied economics.

This module is an introduction to statistics and data analysis for economists. The first semester deals with the fundamental issues of statistics, building from basic probability theory, through sampling, distributions, hypothesis testing and interpretation. A wide range of examples is considered. The second semester moves into data analysis. We use a variety of statistical methods to investigate and interpret real world data.

The module will also include an introduction to statistical software.

Outline Of Syllabus

Probability
Discrete and Continuous Random Variables
Probability Distributions
Estimation and Sampling Distributions
Hypothesis Testing
Describing Data
Learning from Data
Graphical and Numerical Analysis of Data
Correlation versus Causation
Introduction to Regression Analysis
Multiple Regression
Statistical inference for Regression: Hypothesis Testing, Confidence Intervals, Prediction
Nonlinear Models
Estimation Problems
An introduction to Stata

Teaching Methods

Teaching Activities
Category Activity Number Length Student Hours Comment
Scheduled Learning And Teaching ActivitiesLecture301:0030:00PiP Lectures
Guided Independent StudyAssessment preparation and completion165:0065:00N/A
Guided Independent StudyDirected research and reading155:0055:00N/A
Scheduled Learning And Teaching ActivitiesSmall group teaching51:005:00PiP PC Labs
Scheduled Learning And Teaching ActivitiesSmall group teaching51:005:00PiP Seminars
Guided Independent StudyIndependent study140:0040:00N/A
Total200:00
Teaching Rationale And Relationship

Lectures deliver the main concepts.
Seminars and PC labs provide students with an opportunity to enhance both theoretical and practical (computer-based) aspects of statistical analysis.

Assessment Methods

The format of resits will be determined by the Board of Examiners

Exams
Description Length Semester When Set Percentage Comment
Written Examination901A40When social-distancing measures prevent PiP invigilated examinations, this will be an online, time-limited PC exam.
Written Examination602A40When social-distancing measures prevent PiP invigilated examinations, this will be an online, time-limited PC exam
Exam Pairings
Module Code Module Title Semester Comment
Analysing Economic Data2N/A
Other Assessment
Description Semester When Set Percentage Comment
Prof skill assessmnt2M20Group project - 2000 words (with peer review)
Formative Assessments

Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.

Description Semester When Set Comment
Prob solv exercises1MIn-class tests, Canvas quizzes, homework exercises
Prob solv exercises2MIn-class tests, Canvas quizzes, homework exercises
Assessment Rationale And Relationship

Written examinations ensure learning of theoretical concepts and are an appropriate way to assess problem solving skills under the time constraint as required in industry.

In the case of alternative assessments for semester 1 and Semester 2 being necessary due to circumstances, the Module Leader will in discussion with the DPD and the University, discuss possible acceptable online alternatives, such as a take home exam delivered online with a set time limit to complete (24 hours or less as deemed appropriate).

Group project demonstrates that students can analyse and interpret real world data.

Reading Lists

Timetable