Download Introduction to Probability and Statistics

MODULE SPECIFICATION
KEY FACTS
Module name
Module code
School
Department or equivalent
UK credits
ECTS
Level
Delivery location
(partnership programmes
only)
Introduction to Probability and Statistics
AS0006
Cass Business School
UG Programme
20
10
3
MODULE SUMMARY
Module outline and aims
The aim of this module is to provide you with familiarity of fundamental statistical
techniques and an understanding of their application in practice, along with the theory of
probability which underlies them.
You will learn fundamental tools in probability and statistics which are necessary for the
understanding of modules later in the programme and for the Foundation year
Introduction to Finance and Accounting module.
Content outline
Probability:
Events, measures of probability, conditional probabilities, independence, Bayes' theorem.
Discrete and continuous probability distributions: uniform, binomial, Poisson, normal,
exponential. Central Limit Theorem.
Data analysis:
Pictorial displays, sample statistics. Simple parameter estimation, confidence intervals and
simple tests of significance, for one sample. Straight line fitting: least squares, fitted values,
residuals.
WHAT WILL I BE EXPECTED TO ACHIEVE?
On successful completion of this module, you will be expected to be able to:
Knowledge and understanding:
 Understand the axiomatic foundations of probability.
 Understand the concept of a random variable and show familiarity with common
Distributions.
 Understand the theory underlying statistical techniques.
Skills:
 Construct probabilistic models appropriate to a problem described in words.
 Construct statistical displays and probabilistic diagrams appropriate to the situation.
 Explain in words the results of probabilistic or statistical analysis having regard to the
situation being modelled.
 Use statistical tables.
 Test hypotheses and derive confidence intervals in well-defined circumstances.
Values and attitudes:
 Demonstrate an awareness of the ethical responsibility of a statistician to draw
conclusions only as justified by the data and to provide illustrations which are not
likely to mislead the user.
HOW WILL I LEARN?
A variety of learning and teaching methods will be used in this course.
Lectures are used to introduce context, concepts and techniques illustrated with practical
examples. You will also have the opportunity to participate in class discussions and work
through examples and exercises with the support of the lecturer. It is strongly
recommended that you attend ALL lectures.
Key learning and teaching resources will be put on the module website on Moodle.
In the independent study time you are encouraged to consolidate your knowledge from
the lectures by reading around the subject and spending time working through sample
exercises and questions. In addition you will be preparing and undertaking your
coursework assignments.
Teaching pattern:
Teaching
component
Teaching Contact
Self-directed
Placement
type
hours
study hours
hours
(scheduled) (independent)
Lectures
Lecture
Totals
60
140
Total
student
learning
hours
200
60
140
200
WHAT TYPES OF ASSESSMENT AND FEEDBACK CAN I EXPECT?
Assessments
This module is assessed by coursework divided into four sets covering different parts of
the syllabus. Each coursework set will consist of two exercises and two tests. The
weighting of the individual assessments within each set will vary, and full details will be
given at the start of the course. Each coursework set must be passed with an aggregate
mark of 55% in addition to achieving the module pass mark.
Assessment pattern:
Assessment
component
Coursework Set
1
Coursework Set
2
Coursework Set
3
Coursework Set
4
Assessment Weighting Minimum Pass/Fail?
type
qualifying
mark
Set exercise 25%
55%
N/A
Set exercise
25%
55%
N/A
Set exercise
25%
55%
N/A
Set exercise
25%
55%
N/A
Assessment criteria
Assessment criteria are descriptions of the skills, knowledge or attributes you need to
demonstrate in order to complete an assessment successfully and Grade-Related
Criteria are descriptions of the skills, knowledge or attributes you need to demonstrate to
achieve a certain grade or mark in an assessment. Assessment Criteria and GradeRelated Criteria for module assessments will be made available to you prior to an
assessment taking place. More information will be available in the UG Assessment
Handbook and from the module leader.
Feedback on assessment
Following an assessment, you will be given your marks and feedback in line with the
University’s Assessment Regulations and Policy. More information on the timing and
type of feedback that will be provided for each assessment will be available from the
module leader.
Assessment Regulations
The Pass mark for the module is 60%. Any minimum qualifying marks for specific
assessments are listed in the table above. The weighting of the different components
can also be found above. The Programme Specification contains information on what
happens if you fail an assessment component or the module.
INDICATIVE READING LIST
Lipschutz, S. and Schiller, J., 2011. Schaum’s Outline of Introduction to Probability and
Statistics. New York: McGraw-Hill Education.
Clarke, G.M. and Cooke, D., 2004. A Basic Course in Statistics. 5th ed. London: Hodder
Education
Milton, J.S. and Arnold, J.C., 2002. Introduction to Probability and Statistics: Principles and
Applications for Engineering and the Computing Sciences. 2nd ed. New York: McGraw-Hill
Education.
Grimmett, G. and Welsh, D., 2014. Probability: An Introduction. 2nd ed. Oxford: Oxford
University Press.
Version: 1.0
Version date: April 2016
For use from: 2016-17
Appendix: see
http://www.hesa.ac.uk/component/option,com_studrec/task,show_file/Itemid,233/mnl,12
051/href,JACS3.html/ for the full list of JACS codes and descriptions
CODES
HESA Cost Centre
122
Description
Mathematics
Price Group
C
JACS Code
G300
Description
The study of the collection
and analysis of numerical
data
The mathematical study of
chance
Percentage (%)
50%
G320
50%