# FOUNDATIONS OF STATISTICS AND ECONOMETRICS - 2023/4

Module code: MANM467

## Module Overview

This module is an introduction to the methods of specification, estimation and testing of statistical and econometric models. The techniques are applied to business data making use of the statistical computer packages (e.g. Stata).

### Module provider

TAVALAEI Mahdi (SBS)

## Overall student workload

Independent Learning Hours: 97

Tutorial Hours: 11

Laboratory Hours: 11

Guided Learning: 20

Captured Content: 11

Semester 1

N/A

## Module content

Review of descriptive statistics and statistical inference (e.g., estimation, hypotheses testing, etc.); Analysis of variance; Linear Regression basics, and OLS model; Multiple regression using cross sectional and panel data; Regression diagnostics (e.g., multicollinarity, serial correlation and heteroskedasticity); Endogeneity, causal inference, instrumental variables, and two-stage least squares (2SLS) regression; Supplementary topics (e.g., difference in difference method, nonlinear regressions, etc.)

## Assessment pattern

Assessment type Unit of assessment Weighting
Coursework A FINAL REPORT ON APPLYING ECONOMETRIC ANALYSES ON A REAL WORLD DATA SET USING STATISTICAL COMPUTER PACKAGE 100

## Alternative Assessment

Coursework exercise which can be completed off-campus.

## Assessment Strategy

The assessment emphasises work based on implementing theoretical knowledge of econometrics and statistics in the form of an off-class project. The summative assessment for this module consists of a final written report applying econometric analyses on a real-world business dataset using statistical analysis software.Formative assessment and feedback are done via Surreylearn, individualised written feedback, meetings with students if required, and general comments in classes.

## Module aims

• Provide knowledge of fundamental concepts and models widely used in statistical and econometric analyses
• Apply theoretical concepts and use their understanding and insight gained throughout the unit to implement and interpret econometric models
• Use various types of quantitative data to make decisions in the business world

## Learning outcomes

 Attributes Developed 001 Display a fundamental understanding of the statistical and econometric analysis (such as assumptions, inference, and diagnostics), KC 002 Be able to build econometric models and analyse various types of quantitative data, and interpret the results, KPT 003 Be able to evaluate empirical research in the field of business critically, and propose and apply robustness tests, and KCPT 004 Be able to write up a full-fledged econometric analysis report on real-world data, and to communicate clearly and evaluate the findings critically. KCPT

Attributes Developed

C - Cognitive/analytical

K - Subject knowledge

T - Transferable skills

P - Professional/Practical skills

## Methods of Teaching / Learning

The learning and teaching strategy are designed to give students the theoretical knowledge and practical tools they need for building and implementing empirical analysis; encourage rigour in their approach to real-world quantitative problems, and encourage critical evaluation of quantitative models. The learning and teaching methods include reading (textbooks, articles, and reports), watching videos (in forms lectures and additional content), participating in computer labs, off-class exercises, and discussion in class.

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.