ULMS Electronic Module Catalogue
The information contained in this module specification was correct at the time of publication but may be subject to change, either during the session because of unforeseen circumstances, or following review of the module at the end of the session. Queries about the module should be directed to the member of staff with responsibility for the module.
Title QUANTITATIVE METHODS FOR ACCOUNTING AND FINANCE
Code ACFI111
Coordinator Dr RR Hizmeri Canales
Finance and Accounting
R.Hizmeri@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2022-23 Level 4 FHEQ First Semester 15

Pre-requisites before taking this module (other modules and/or general educational/academic requirements):
 

Modules for which this module is a pre-requisite:
 

Programme(s) (including Year of Study) to which this module is available on a required basis:
 

Programme(s) (including Year of Study) to which this module is available on an optional basis:
 

Teaching Schedule
  Lectures Seminars Tutorials Lab Practicals Fieldwork Placement Other TOTAL
Study Hours 24

 6

         30
Timetable (if known) 120 mins X 1 totaling 24
  		60 mins X 1 totaling 6
  		         
Private Study 120
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Assessment 1: Report Assessment Type: Coursework Duration: 2000 words Weighting: 50% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL penalty applies Anonymous Ass  0    50       
Assessment 2: Report Assessment Type: Coursework Duration: 2000 words Weighting: 50% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty Applies Anonymous Ass  0    50       

Aims

The module aims to provide an introduction to quantitative methods that will assist students in establishing basic quantitative and statistical skills for the study of accounting and finance.

Learning Outcomes

(LO1) Demonstrate a basic understanding of statistical tools and their applications to accounting and finance.

(LO2) Understand the fundamental concepts of statistics and probability

(LO3) Understand basic principles of random sampling, the nature of sampling error and the need for estimation

(LO4) Explain the rules of hypothesis testing

(LO5) Explain the relation between two random variables using correlation and regression analyses

(LO6) Use Excel for data handling and statistical modelling.

(S1) Problem solving skills

(S2) Numeracy

(S3) Written Communication Skills

(S4) IT skills

(S5) Data Presentation

Teaching and Learning Strategies

Teaching Method: Lecture
Scheduled Directed Student Hours: 24
Attendance Recorded: No

Teaching Method: Seminar
Description: The sessions will develop discussion on statistical analysis with real-life cases. The focus will then be upon using this learning to support the two assessments. Materials for all the sessions are available online.
Scheduled Directed Student Hours: 6
Attendance Recorded: Yes

Self-Directed Learning Hours: 120
Description: This time should be used to read the course textbooks, work through examples, solve problems and exercises in addition to those covered in learning materials.

Skills/Other Attributes Mapping
Skills/attributes: Problem-solving skills
How this is developed: During lectures, labs and undirected learning
Mode of assessment (if applicable): Coursework 1 and Coursework 2

Skills / attributes: Numeracy
How this is developed: During lectures, labs and undirected learning
Mode of a ssessment (if applicable): Coursework 1 and Coursework 2

Skills/attributes: Written communication skills
How this is developed: During lectures and undirected learning
Mode of assessment (if applicable): Coursework 1 and Coursework 2

Skills/attributes: IT skills
How this is developed: During labs and undirected learning
Mode of assessment (if applicable): Coursework 1 and Coursework 2

Syllabus
 

Describing the Distribution of a Variable
Data visualisation
Finding Relationships among Variables
Probability and Probability Distribution
Decision Making under Uncertainty
Statistical Inference
Sampling and Sampling Distributions
Confidence Interval Estimation
Hypothesis Testing
Regression Analysis: Estimating Relationships
Regression Analysis: Statistical Inference
Software and data sources: Excel, Bloomberg, etc.

Recommended Texts
Reading lists are managed at readinglists.liverpool.ac.uk. Click here to access the reading lists for this module.