STATISTICS AND ECONOMETRICS - 2023/4

Module Overview

This module introduces methods for building, estimating, and interpreting statistical and econometric models focusing on the area of business analytics, and analyzing quantitative data for making better decisions. The module provides the theoretical foundation and intuitive knowledge, applied to business data by making use of econometric/statistical software.

Module provider

Surrey Business School

Module Leader

TAVALAEI Mahdi (SBS)

Number of Credits: 15

ECTS Credits: 7.5

Framework: FHEQ Level 7

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

Module Availability

Semester 1

Prerequisites / Co-requisites

None

Module content

Review of concepts such as descriptive statistics and graphical representation of data; inferential statistics (e.g., estimation, hypotheses testing, etc.);

Analysis of variance; Linear Regression basics, and Ordinary Least Square (OLS) model;

Multiple regression using cross-sectional and panel data; OLS assumptions and regression diagnostics (e.g., multicollinearity, heteroskedasticity, influential observations);

Endogeneity and causal inference,

Supplementary topics such as two-stage least squares (2SLS) regression and instrumental variables.

The aforementioned concepts are covered theoretically and applied in computer labs, in the business analytics context aiming to aid decision-making by generating insights from data.

Assessment pattern

Alternative Assessment

Not applicable

Assessment Strategy

The assessment emphasizes implementing theoretical knowledge of econometrics and statistics in the business context, 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 econometric/statistical software (e.g., Stata).

Module aims

• Provide fundamental and intuitive knowledge of concepts and models widely used in statistical and econometric analyses with an emphasis on the business-related problems
• Apply theoretical concepts and use their understanding and insight gained throughout the module to implement and interpret econometric models aiming to aid business-related decisions
• Analyse various types of quantitative data to make decisions in the business world

Learning outcomes

Methods of Teaching / Learning

The learning and teaching strategies are designed to give students the theoretical knowledge and practical tools they need for building and implementing empirical analysis to transform data into business insights; encourage rigor in their approach to real-world quantitative problems; and encourage critical evaluation of quantitative models to improve business decision making.

The learning and teaching methods include: reading (textbooks, articles, and reports), watching videos (e.g., theoretical explanations and additional content), participating in computer labs, guided learning , 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.

Reading list

https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: MANM526

Other information

Employability: empirical skills and writing a report on quantitative analysis of real-world business data; guest speakers; industry-related examples embedded in the module's content.

Digital Capabilities: various digital learning materials (captured content, additional videos, supplementary digital content, and datasets for practice, etc.); applying theories using econometric/statistical software in computer labs and for the assessment.

Programmes this module appears in

Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.