NAME OF COURSE/MODULE: CALCULUS
COURSE CODE: SMS1033
NAME(S) OF ACADEMIC STAFF: NORHANA ABD RAHIM
RATIONALE FOR THE INCLUSION OF THE COURSE/MODULE IN THE PROGRAMME: This subject discusses the basic concepts, as the foundation (mainly mathematical) for further undergraduate and even postgraduate courses in probability and statistics
SEMESTER AND YEAR OFFERED: Semester 1 & 2 / Year 1
TOTAL STUDENT LEARNING TIME (SLT) FACE TO FACE TOTAL GUIDED AND INDEPENDENT LEARNING

L = Lecture

T = Tutorial

P = Practical

O= Others

L

28

T

 

14

P

 

0

O

 

78

L + T + P + O = 120 HOURS

CREDIT VALUE: 3
PREREQUISITE (IF ANY): None
OBJECTIVES: 1.     To provide an introduction to the various basic concepts in statistics and probability theory that are required in the higher levels undergraduate courses,

2.     To provide the applications of these concepts to the real life problems.

LEARNING OUTCOMES: Upon successful completion of this course students should have the ability to:

1.     Analyse the problems using probability theory, such as sample space, sample points, simple event, compound event, probability of an event  (C4 – LO1)

2.     Demonstrate decision making abilities and propose alternative solution based on evidence using various basic rules and concepts in probability theory to numerous disciplines (CTPS3, P4 – LO3)

3.     Propose the techniques in statistics to solve a variety of practical problems (LL2, A3 – LO7)

TRANSFERABLE SKILLS: Students should be able to develop problem solving skills through a process of lectures and tutorials.
TEACHING-LEARNING AND ASSESSMENT STRATEGY: Teaching-learning strategy:

  • The course will be taught through a combination of formal lectures, assignments, group work, blended learning using authentic materials, informal activities and various textbooks.

Assessment strategy:

  • Formative
  • Summative
SYNOPSIS: The course will discusse: the three concepts of probability measurement, axioms of probability; sample space and events, mutually exclusive events and addition rule; conditional probability, independent events, multiplication rule; and Bayes’ theorem.  These will be followed by the discussion of  the concept of random variable, probability distribution, and expectation. On the applied aspects sampling distributions, especially the z ,t and  distributions and their use in hypothesis testing are discussed.
MODE OF DELIVERY: Lecture and Tutorial
ASSESSMENT METHODS AND TYPES:
A. Continuous Assessment (40%)
Category Percentage
·        Assignment

·        Tests

·         Participations (more than 80%)

15 %

20 %

5 %

B. Final Examination (60%)
Examination 60 % ·         Decision making questions

·         Problem solving type questions

MAIN REFERENCES SUPPORTING THE COURSE Walpole,  D.E., Myers,  R.H. & Myers S.L. (2012) Probability and Statistics for Engineers and Scientists (9th ed): Prentice Hall
ADDITIONAL REFERENCES SUPPORTING THE COURSE 1.     Milton, J.S. & Arnold, J.C. (2004) An Introduction to Probability and Statistics (6th ed): McGraw Hill

2.     Triola, M. F. (2004). Essentials of Statistics (2nd ed): Pearson Addison Wesley.

3.     Freund, J.E. (2006) Mathematical Statistics (7th ed): Prentice Hall.

4.     Spiegel, M.R. (1999) Theory and Problems of Statistics (2nd ed): Schaum’s Outline Series: McGraw Hill.