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 BUSINESS STATISTICS
Code ECON154
Coordinator Dr M Chaturvedi
Economics
Mayuri.Chaturvedi@liverpool.ac.uk
Year CATS Level Semester CATS Value
Session 2023-24 Level 4 FHEQ Second 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

       6

 36
Timetable (if known)              
Private Study 114
TOTAL HOURS 150

Assessment
EXAM Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
Assessment 2: Unseen Examination Assessment Type: Written Examination Duration: 2 hour Weighting: 70% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty Applies Ano  2    70       
Assessment 1: Midterm Assessment Type: Written Examination Duration: 1 hour Weighting: 30% Reassessment Opportunity: Yes Penalty for Late Submission: Standard UoL Penalty Applies Anonymous Asse  1    30       
CONTINUOUS Duration Timing
(Semester)		 % of
final
mark		 Resit/resubmission
opportunity		 Penalty for late
submission		 Notes
             

Aims

The purpose of the module is to provide an introduction to business statistics for the non-specialist.   The course aims to provide a broad understanding of the nature of variability and why it is an issue for managers.   It will also provide students with the ability to derive and understand a variety of graphs and statistics which can be produced in Excel and which provide a means for managers to make intelligent use of statistics in the process of management and decision-making.

Learning Outcomes

(LO1) Explain the nature of variability and why it is important for managers

(LO2) Describe and analyse data using graphs and summary statistics

(LO3) Explain basic principles of sampling and apply them to management contexts

(LO4) Model data using standard probability distributions

(LO5) Describe and analyse attribute data

(LO6) Explain the nature of random sampling error and the need to place a margin of error around estimates

(LO7) Calculate a margin of error to place confidence limits on estimates

(LO8) Explain and interpret control charts and propose appropriate improvement strategies

(LO9) Analyse the relationship between quantitative variables using simple regression and correlation techniques

(S1) Numeracy/computational skills - Confidence/competence in measuring and using numbers

(S2) Numeracy/computational skills - Problem solving

(S3) Numeracy/computational skills - Numerical methods

(S4) Numeracy/computational skills - Reason with numbers/mathematical concepts

(S5) Critical thinking and problem solving - Critical analysis

(S6) Create and manipulate Excel spreadsheets and create tables and graphs in Excel and export them to Word

(S7) Use standard data analysis tools in Excel

Teaching and Learning Strategies

Teaching Method: Lecture
Scheduled Directed Student Hours: 24
Attendance Recorded: Yes

Teaching Method - Seminar
Description: The seminar for this module will take place in face-to-face. Students will be given real-world case studies to solve. The teaching team will provide the solutions to the case studies. These will generally be based and aligned with the lectures
Scheduled Directed Student Hours: 6
Attendance Recorded: Yes

Teaching Method : Group Study
Description: Bi-weekly 1 hour session to foster student community and engagement by working with others on their ‘active learning’ activities
Scheduled Student Hours: 6
Attendance Recorded: No

Self-Directed Learning Hours: 114
Description: Participation in recordings and tutorials is insufficient in order to maximize performance on this module. Students are expected to engage in regular self-directed learning. This should include attempting tutoria l exercises, data gathering and analysis, and good use of the suggested reading material.

Skills/Other Attributes Mapping

Skills / attributes :Use standard data analysis tools in Excel
How this is developed: Practiced in tutorials and through homework
Mode of assessment (if applicable)

Skills / attributes: Create and manipulate Excel spreadsheets and create tables and graphs in Excel and export them to Word
How this is developed: Practiced in tutorials and through homework
Mode of assessment (if applicable):

Skills / attributes: Critical thinking and problem solving - Critical analysis
How this is developed: Lectures and tutorials
Mode of assessment (if applicable): In-semester tests

Skills / attributes: Numeracy/computational skills - Reason with numbers/mathematical concepts
How this is developed: Lectures and tutorials
Mode of assessment (if applicable): In-semester tests

Skills / attributes: Numeracy/computational skills - Numerical methods
How this is developed: Lectures and tutorials
Mode of assessment (if applicable): In-semester tests

Skills / attributes: Numeracy/computational skills - Problem solving
How this is developed: Lectures and tutorials
Mode of assessment (if applicable): In-semester tests

Skills / attributes: Numeracy/computational skills - Confidence/competence in measuring and using numbers
How this is developed: Lectures and tutorials
Mode of assessment (if applicable): In-semester tests

Syllabus
 

Data and Statistics; categorical and quantitative data; measures of location and variation.

The normal and other continuous distributions.

Surveys and sampling; sampling distributions; confidence intervals for proportions.

Testing hypotheses about proportions; confidence intervals and hypothesis tests for means.

More about tests and intervals; comparing means.

Inference for counts: Chi-Square Tests.

Correlation and linear regression.

Inference for regression; understanding residuals.

Control charts and improvement strategies

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