MA2506 Probability and Statistics

Part I

Course Duration: 1 Semester
Credit Units: 4
Level: B2
Medium of Instruction: English

Prerequisites: For 2011 cohort or before: Nil
For 2012 cohort or after: Grade B or above in MA1201 and subject to approval from MA; or
Grade C- or above in MA1301; or
Pass in MA1400; or equivalent
Precursors: Nil
Equivalent Courses: Nil
Exclusive Courses: MA2172
                               MA2177
                               MA3160
                               MA3181

Part II    

Course Aims:
This course introduces
probability theory and statistical inference. It will help students understand the theoretical basis and practical applications of probability distributions, and understand the theory of statistical inference as developed from the basis of probability. It trains students in thinking and analysing problems from a probabilistic and statistical point of view.


Course Intended Learning Outcomes (CILOs):
Upon successful completion of this course, students should be able to:

No.

CILOs

Weighting (if applicable)

1.

explain concepts at high levels and implement basic operations in probability and statistics.

3

2.

use the methods of hypothesis testing and parametric estimation for some statistical problems.

3

3.

develop mathematical models using probability and statistics.

2

4.

apply statistical and computational methods to a range of problems in science and engineering involving probability and statistics.

2

5.

the combination of CILOs 1-4

3

Teaching and learning Activities (TLAs):
(Indicative of likely activities and tasks students will undertake to learn in this course. Final details will be provided to students in their first week of attendance in this course) 

TLAs

CILO No.

Hours/week

Learning through teaching is primarily based on lectures.

1-5

40 hours in total

Learning through tutorials is primarily based on interactive problem solving allowing instant feedback.

 

1

4 hours

2

4 hours

3

2 hours

4

2 hours

Learning through take-home assignments helps students understand the theoretical basis and practical applications of probability and statistics, and develop the ability of analysing problems from a probabilistic and statistical point of view.

1-5

after-class

Learning through online examples for applications helps students set up probabilistic and statistical models and apply to some problems in science and engineering.

3, 4

after-class

Learning activities in Math Help Centre provides students extra help.

1, 2

after-class


Assessment Tasks/Activities:
(Indicative of likely activities and tasks students will undertake to learn in this course. Final details will be provided to students in their first week of attendance in this course)  

30%
Coursework
70% Examination (Duration: 3
hours, at the end of the semester)

For a student to pass the course, at least 30% of the maximum mark for the examination must be obtained.
 

Assessment Tasks/Activities

CILO No.

Weighting
(if applicable)

Remarks

Test

1, 3

15-30%

Questions are designed for the part of probability theory to see how well the students have learned the basic concepts, fundamental theory and some applications  of probability.

Hand-in assignments

1-4

0-15%

These are skills based assessment to enable students to demonstrate the basic concepts and fundamental theory of probability and statistics and some applications.

Examination

5

70%

Examination questions are designed to see how far students have achieved their intended learning outcomes. Questions will primarily be skills and understanding based to assess the student’s versatility in probability theory and statistical inference.

Formative take-home assignments

1-4

0%

The assignments provide students chances to demonstrate their achievements on probability and statistics learned in this course.


Grading of Student Achievement: Refer to Grading of Courses in the Academic Regulations



Part
III
   

Keyword Syllabus:

Probability.  Sample Space.  Discrete and Continuous Random Variables.  Discrete and Continuous Probability Distributions.  Central Limit Theorem.  Chebyshev’s Theorem.  Mathematical Expectation and Variances.  Moment Generating Functions.  Estimation of Parameters.  Hypothesis Testing for one and two samples.

Related Links
Department of Mathematics