# 2020/21 Undergraduate Module Catalogue

## LUBS2575 Statistics and Econometrics

### 20 creditsClass Size: 290

Module manager: Luisa Zanchi & Sandra Lancheros Torres
Email: S.P.LancherosTorres@leeds.ac.uk

Taught: Semesters 1 & 2 (Sep to Jun) View Timetable

Year running 2020/21

### Pre-requisite qualifications

LUBS1285 Mathematics and Statistics for Economics and Business 1B
OR
MATH1710 Probability and Statistics I
and
MATH1712 Probability and Statistics II

### This module is mutually exclusive with

 LUBS2224 Credit and Financial Analytics

Module replaces

LUBS2570 Introduction to EconometricsLUBS2670 Statistics for Business and Economics 2

This module is not approved as a discovery module

### Module summary

This module provides you with an intermediate-level understanding of mathematical statistics and an introduction of applied econometric techniques and relevant software. It requires you to have a good background in introductory statistical techniques. The module begins by considering the application of statistical theory to the solution of practical problems and; hence, provides you with essential tools to deal with the quantitative issues arising in most social sciences. The module then extends the intermediate-level statistical theory and problem-solving techniques to focus on econometrics. The econometrics part covers regression analysis with cross-sectional data using the method of Ordinary Least Squares (OLS). It begins with an introduction of the basic assumptions and interpretation of the linear regression model with one regressor. It extends this model to incorporate additional regressors in the multivariate regression analysis. Finally, the module provides a framework for assessing the validity of econometric analysis based on OLS.

### Objectives

Building on the knowledge of introductory statistics acquired at level 1, the aims of the module are to provide students with the essential techniques in mathematical statistics at an intermediate level and to use this platform to introduce students to the basic tools of econometrics to enable them to use these techniques to test economic theory.

Learning outcomes
Upon completion of this module students will be able to:
1.Identify and outline the statistical theory of continuous random variables, bivariate probability distributions and bivariate inferential procedures;
2. Explain and identify basic applied econometric techniques, and econometric theories and methodologies;
3. Interpret the outcomes of econometric analysis;
4. Assess the validity of the results from a regression analysis based on OLS. This will allow the students to interpret and appraise appropriate literature that utilises such analysis;
5. Recognise contexts in accounting, finance, economics, management and particularly econometrics in which intermediate-level concepts in mathematical statistics can be usefully employed.

Skills outcomes
- Analyse quantitative issues in the social sciences involving intermediate-level concepts in mathematical statistics
- Apply knowledge of intermediate-level techniques in mathematical statistics to solve problems in economics and business
- Apply econometric techniques and appropriate software to social sciences

### Syllabus

Indicative content:
- Random Variables and Probability Distributions (one and two variables)
- Properties of Probability Distributions (e.g. expected value, variance, covariance)
- Important Probability Distributions (e.g. normal distribution, t-distribution, F-distribution, Chi-square distribution)
- Samples and Sampling Distributions
- Estimation
- Hypothesis testing
- Confidence intervals
- The nature of econometrics
- The simple linear regression model and its assumptions
- The ordinary least squares (OLS) estimator
- Estimation and statistical inference.
- The multiple linear regression model
- Assessing the validity of the OLS estimator: factors affecting efficiency and consistency.

### Teaching methods

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information

 Delivery type Number Length hours Student hours Example Class 9 1.00 9.00 Computer Class 5 1.00 5.00 Supervised Practice 1 1.00 1.00 Lecture 36 1.00 36.00 Tutorial 4 2.00 8.00 Private study hours 141.00 Total Contact hours 59.00 Total hours (100hr per 10 credits) 200.00

### Private study

This could include a variety of activities, such as reading, watching videos, question practice and exam preparation.

### Opportunities for Formative Feedback

Your teaching methods could include a variety of delivery models, such as face-to-face teaching, live webinars, discussion boards and other interactive activities. There will be opportunities for formative feedback throughout the module.

### Methods of assessment

Due to COVID-19, teaching and assessment activities are being kept under review - see module enrolment pages for information

Coursework
 Assessment type Notes % of formal assessment Assignment Assessed Coursework (S2) 20.00 Total percentage (Assessment Coursework) 20.00

Normally resits will be assessed by the same methodology as the first attempt, unless otherwise stated

Exams
 Exam type Exam duration % of formal assessment Standard exam (closed essays, MCQs etc) (S1) 1 hr 40.00 Standard exam (closed essays, MCQs etc) (S2) 1 hr 40.00 Total percentage (Assessment Exams) 80.00

The resit for this module will be 100% by 2 hour examination.