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The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
Course Name: Statistics and probability theory
Course Code: BAS 212
I. Basic Course Information
Program(s) on which the course is given: Engineering Common Course
Department offering the course: Basic Science Department
Academic level: 3rd level
Semester in which course is offered: Fall
Course pre-requisite(s): None
Credit Hours: 3hrs
Contact Hours Through:
Lecture
2.0
Tutorial*
2.0
Practical*
0.0
Total
4.0
Approval date of course specification: September 2014
II. Overall Aims of Course
To provide the student with a clear and detailed presentation of the statistics. Upon
completion of this course, participants will be able to: Appreciate the value of
exploratory methods in preliminary data analysis. Explore, characterize and identify
problems and trends in data using graphical tools. Use descriptive statistics to
summarize data. Master the concepts of hypothesis testing, confidence intervals, risk
and power. Perform the appropriate statistical test based on the study objective.
Analyze data more quickly and more accurately. Interpret results reliably and
confidently
III. Program ILOs covered by course
Program Intended Learning Outcomes (By Code)
Knowledge &
Intellectual Skills
Professional Skills
Understanding
K1, K5
I1, I3
P1, P6
General
Skills
G1, G6
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The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
IV. Intended Learning Outcomes of Course (ILOs)
a. Knowledge and Understanding
On completing the course, students should be able to:
k. 1 [Explain types of data and methods of presenting these data.
k. 2 Discuss measures of tendency and dispersion.
k. 3 State the different rules of probabilities.
k. 4 Give practical examples of total probability and Bayes’ Rule.
k. 5 Define the random variable and its related concepts such as probability distribution.
k. 6 Recognize when to apply the uniform and exponential distributions.
k. 7 Describe the characteristics of the normal distribution.
k. 8 Translate normal distribution problems into standardized normal distribution
problems.
k. 9 Define the interval estimate of a parameter and confidence level of an interval
estimate.
k. 10 Explain the hypothesis testing.]
b. Intellectual/Cognitive Skills:
On completing the course, students should be able to
i.1 Differentiate between different types of data.
i.2 Construct an appropriate graph for a set of observed data.
i.3 Evaluate the coefficient of correlation and coefficient of determination.
i.4 Differentiate the different rules of probabilities.
i.5 Formulate Engineering problems in mathematical model using random variables and
random processes.
i.6 Write an appropriate distribution for a set of observed data.
i.7 Differentiate between different types of probability density function.
c. Practical/Professional Skills
On completing the course, students should be able to:
p.1 Solve problems using the appropriate methods.
p.2 Manipulate probability and random variables
p.3 Employ probability methods in solving engineering problems
p.4 Compute probabilities using a normal distribution table
p.5 Compute the mean and standard deviation for a continuous probability distribution
p.6 Prepare hypothesis testing and simple linear regression.
d. General and Transferable Skills
On completing the course, students should be able to
g.1 React with each other through brain storming questions.
g.2 Use the available resources effectively.
V. Course Matrix Contents
Main Topics / Chapters
1- Descriptive statistics
2- Probabilities
Duratio
n
(Weeks)
3
3
Course ILOs Covered by Topic
(By ILO Code)
K&U
I.S.
P.S.
G.S.
k1, k2
i1, i2
p1
g1, g2
k3, k4
i4
p1, p3
g1, g2
2
The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
Random variables and
probability distributions
4- Regression and correlation
5- Confidence intervals
6- Test of hypotheses
7Net Teaching Weeks
3-
3
1
1
1
k5, k6 , k7
, k8
p1, p2,
p3, p4, p5
p6
i5, i6, i7
i3
k9
k10
p6
g1, g2
g1, g2
g1, g2
g1, g2
12
VI. Course Weekly Detailed Topics / hours / ILOs
Week
No.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Total
Hours
Sub-Topics
Types of data-Data Organization Presenting data
Measures of Tendency
Measures of dispersion
Fundamentals of probability
Counting rules
Conditional probability
Contact Hours
Theoretical
Practical
Hours
Hours*
2
2
4
2
2
4
4
4
4
Midterm Exam
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
26
24
[Characteristics of a probability
4
distributions and random variables ]
Discrete random variable- Discrete
4]
probability distributions
Continues random variable- Continues
4
probability distributions
Test of Hypotheses
4
Confidence interval
4
Simple Linear regression and correlation
4
coefficient
Revision
4
Final Exam
Total Teaching Hours
50
Teaching/Learning
Method
Lectures & Seminars
Tutorials
Computer lab Sessions
Practical lab Work
Reading Materials
Web-site Searches
Selected
Method
VII. Teaching and Learning Methods
Course ILOs Covered by Method (By ILO Code)
√
√
All
All
Intellectual
Skills
All
All
√
√
All
All
All
All
K&U
Professional
Skills
All
All
General
Skills
All
All
All
All
g2
g2
3
The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
Research & Reporting
Problem Solving /
Problem-based Learning
Projects
Independent Work
Group Work
Case Studies
Presentations
Simulation Analysis
√
All
All
All
g2
Assessment
Weight /
Percentage
Week
No.
Others (Specify):
Selected
Method
VIII. Assessment Methods, Schedule and Grade Distribution
Course ILOs Covered by Method
(By ILO Code)
Assessment
Method
K&U
I.S.
P.S.
G.S.
Midterm Exam
√
Final Exam
√
Quizzes
√
Course Work
Report Writing
Case Study
Analysis
Oral
Presentations
Practical
Group Project
Individual Project
√
k1, k2, k3
i1,i2, i3, i4
k4
All
All
k1, k2,
i1, i2, i3
k3, k4, k5,
i4, i5, i6, i7
, k6, k7, k8
All
All
p1, p2, p3
g2
20%
7
All
g2
50%
15
10%
3, 5, 9
20%
2, …, 13
p1, p2, p3
p4, p5
All
All
Others (Specify):
IX. List of References
Essential Text Books
Course notes
Recommended books
Statistics for Engineers and Scientists
- Textbook ISBN #: 978-0-07-110222-3.
 Lecture Notes
 Daren S. Starnes, Dan Yates, David Moore, “The Practice of
Statistics”, 4th edition, W. H. Freeman, 2010.
 John A. Gubner, “Probability and Random Processes for
Electrical and Computer Engineers”, Cambridge University Press; 1st
edition, ISBN: 978-0521864701, 2006.
 Oliver C. Ibe, “Fundamentals of Applied Probability and Random
Processes”, Academic Press; 1st edition, ISBN: 978-0120885084, 2005.
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The Higher Canadian Institute for Business
and Engineering Technology
Quality Assurance Unit
Course Specification
 Richard H. Williams, “Probability, Statistics, and Random
Processes for Engineers”, Cengage-Engineering; 1st edition, ISBN:
978-0534368883, 2002.
 Steven Kay, “Intuitive Probability and Random Processes using
MATLAB”, Springer, 1st edition, ISBN: 978-0387241579, 2005.
Periodicals, Web sites, 
etc …
www.libgen.org
X. Facilities required for teaching and learning
Computer-Data show.
Course coordinator: Dr. Hanaa Moussa
Head of Department: Professor Dr. Refaat Salem
Date: September 2014
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