June 2015
The Hong Kong Polytechnic University
Hong Kong Community College
Subject Description Form
Subject Code
CCN1050
Subject Title
Introduction to Probability and Statistics
Level
1
Credit Value
3
Medium of
Instruction
English
Pre-requisite /
Co-requisite/
Exclusion
Nil
Objectives
This subject aims to introduce students to some fundamental principles
and knowledge of statistics. Studying this subject also helps develop
students’ ability to compile statistical data, carry out simple statistical
calculation and understand the elements of probability and probability
distributions. Applications of statistical techniques are emphasised to
solve practical problems in science and engineering.
Intended Learning
Outcomes
Upon completion of the subject, students will be able to:
(a) understand common practices in data collection.
(b) acquire techniques to describe/present the data set.
(c) apply probabilistic and statistical reasoning to describe and analyse
essential features of data sets and problems in real-life situations.
(d) use and extend knowledge of statistical inference techniques and
their applications in real-life situations.
Subject Synopsis/
Indicative Syllabus
Descriptive Statistics
Introduction to statistics; Displaying numerical data through the use of
tables and charts; Measures of central tendency; Measures of variation.
Probability
Experiment; Events; Sample space and probability; Probability rules;
Conditional probability; Bayes’ Theorem.
Random Variables and Expectation
Types of random variables; Jointly distributed random variables;
Expectation; Variance.
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June 2015
Discrete Probability Distributions
Discrete random variables and probability distributions; Bernoulli
Distribution;
Binomial
Distribution;
Poisson
Distribution;
Hypergeometric Distribution.
Continuous Probability Distributions
Continuous random variables and probability distributions; Uniform
Distribution; Normal Distribution; Exponential Distribution; ChiSquared Distribution.
Distributions of Sampling Statistics
Sampling distribution of sample mean; Sampling distribution of sample
variance; Central Limit Theorem.
Inferential Statistics
Point estimation; Interval estimation; Determination of sample size
required.
Teaching/Learning
Methodology
Lectures will focus on the introduction and explanation of probabilistic
and statistical concepts, theories and terminologies supported by real
examples wherever appropriate.
Tutorials will provide students with the opportunity to practise their
newly learnt concepts on data examples. Activities will include
numerical exercises and peer discussions of data analysis results.
Assessment Methods A variety of assessment tools will be used to develop and assess
students’ achievement of the subject intended learning outcomes.
in Alignment with
Intended Learning
Outcomes
Specific assessment
%
Intended subject learning
methods/tasks
weighting outcomes to be assessed
(Please tick as appropriate)
a
b
c
d
Continuous Assessment*
40
 Test
20



 Assignment 1
10



 Assignment 2
10



Final Examination
60



Total
100

*Continuous assessment items and/or weighting may be adjusted by the subject
team subject to the approval of the College Programme Committee.
To pass this subject, students are required to obtain Grade D or above in
both the Continuous Assessment and Final Examination.
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June 2015
Student Study
Effort Expected
Class contact
Hours
 Lecture
26
 Tutorial
13
Other student study effort
Reading List and
References
 Self-Study
52
 Continuous Assessment
39
Total student study effort
130
Recommended Textbook
Tong, H.Y et al. (2013). Introduction to Probability and Statistics for
Science Students. Cengage Learning.
References
Devore, J.L. (2012) Probability & Statistics for Engineering and the
Sciences. (8th ed.), Cengage Learning.
Walpole, R.E., Myers, R.H., Myers, S.L. & Ye, K.Y. (2012).
Probability and Statistics for Engineers and Scientist. (9th ed.), Prentice
Hall.
Weiss, N.A. (2012). Introductory Statistics. (9th ed.), Addison Wesley.
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