# STATISTICS FOR ECONOMICS - 2019/0

Module code: ECO1020

## Module Overview

This module covers probability and statistical inference thus enabling students to undertake empirical work. The emphasis is on applications rather than mathematical rigour.

### Module provider

Economics

PAREY Matthias (Economics)

### Module cap (Maximum number of students): N/A

Independent Learning Hours: 123

Lecture Hours: 22

Tutorial Hours: 5

Semester 2

None

## Module content

• Probability – independent and dependent events; Bernoulli, Binomial, Normal, t and Chi-squared distributions; joint, marginal, and conditional distributions; expectation, variance, covariance, corrrelation; Law of Large Numbers; Central Limit Theorem

• Statistical inference – random samples; sampling distributions; point and interval estimation; hypothesis testing with critical values and p-values; confidence intervals

## Assessment pattern

Assessment type Unit of assessment Weighting
School-timetabled exam/test CLASS TEST 1 (MULTIPLE CHOICE, 1 HOUR) 20
School-timetabled exam/test CLASS TEST 2 (MULTIPLE CHOICE, 1 HOUR) 20
Examination FINAL EXAMINATION (MULTIPLE CHOICE AND ANALYTICAL PROBLEMS, 2 HOURS) 60

Not applicable.

## Assessment Strategy

The assessment strategy is designed to provide students with the opportunity to demonstrate:

Understanding of probability concepts, confidence intervals, hypothesis tests and the context in which to apply this knowledge.

Thus, the summative assessment for this module consists of:

• 2 hour exam at the end of the Semester.

• 2 x multiple choice exams to take place (respectively) at week 5 and week 10. Each test will take a maximum of one hour to complete.

Formative assessment and feedback

Students will receive their multiple choice tests and have the opportunity to discuss this with the lecturer during office hours.

The lecturer will discuss the tests (providing answers) the week following the multiple choice test.

The students  have fortnightly feedback sessions. For these, students are being provided with a set of exercises relating to the lecture material which they solve independently or in teams. In the sessions, they receive feedback on their answers, and guidance on how the answers could be improved.

## Module aims

• To  further develop good data handling skills
• To understand the role statistical inference plays in economic analysis

## Learning outcomes

 Attributes Developed 1 Have a good understanding of probability concepts including both discrete and continuous probability distributions; 2 Be able to construct and interpret point estimates and confidence intervals; 3 Be able to formulate and conduct hypothesis tests for a wide range of applications.

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

## Methods of Teaching / Learning

The learning and teaching strategy is designed to:

Develop skills in statistical inference that will enhance and develop students’ skills for independent academic study in economics and form a basis which can be built upon in subsequent years.

The learning and teaching methods include:

• 2 hour lecture per week x 11 weeks

• 1 hour feedback session x 5 weeks.

Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.