Undergraduate
Faculty of Engineering and Architecture
Civil Engineering
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Civil Engineering Main Page / Program Curriculum / Introduction to Probability and Statistics

Introduction to Probability and Statistics

Course CodeSemester Course Name LE/RC/LA Course Type Language of Instruction ECTS
MCB1007 3 Introduction to Probability and Statistics 4/0/0 CC English 6
Course Goals
Upon completion of this course, students are expected to understand and apply basic
concepts in probability theory and mathematical statistics.
Prerequisite(s) -
Corequisite(s) -
Special Requisite(s) Read, understand, formulate, explain, and apply mathematical statements, and state and apply important results in key mathematical areas (Calculus I and Calculus II).
Instructor(s) Assist. Prof. Dr. UĞUR GÖNÜLLÜ
Course Assistant(s) -
Schedule Section A: Mon 11:00-13:00, Wed 09:00-11:00; Section B: Mon 13:00-15:00, Wed 11:00-13:00
Office Hour(s) Tuesday15:00-16:00, 3-A-15
Teaching Methods and Techniques -Lectures and recitation.
Principle Sources -I. Miller, M. Miller, John E. Freund's Mathematical Statistics with Applications, Pearson Prentice Hall, Seventh Edition, New Jersey, ISBN: 0978-0-13-124646-1, 2004.
Other Sources -Murray R. Spiegel, John J. Schiller, and R. Alu Srinivasan, Probability and Statistics, Schaum's Outline Series, Third Edition, New York, ISBN: 978-0-07-154426-9, 2009.
Course Schedules
Week Contents Learning Methods
1. Week Sets, Combinatorial Methods, Binomial Coefficients Lectures and recitation
2. Week Sample Spaces, The Probability of an Event, Some Rules of Probability, Conditional Probability, Independent Event, Bayes' Theorem Lectures and recitation
3. Week Random Variables, Discrete Probability Distributions Lectures and recitation
4. Week Continuous Random Variables, Multivariate Distributions, Lectures and recitation
5. Week Marginal Distributions, Conditional Distributions Lectures and recitation
6. Week The Expected Value of a Random Variable, Moments Lectures and recitation
7. Week Chebyshev’s Theorem, Moment Generating Functions, Product Moments, Conditional Expectation Lectures and recitation
8. Week The Discrete Uniform Distribution, The Bernoulli Distribution, The Binomial Distribution, The Negative Binomial and Geometric Distribution Lectures and recitation
9. Week The Hypergeometric Distribution, The Poisson Distribution Lectures and recitation
10. Week The Uniform Distribution, The Normal Distribution Lectures and recitation
11. Week The Normal Approximation to the Binomial Distribution, The Normal Approximation to the Poisson Distribution Lectures and recitation
12. Week Population and Sample. Statistical Inference, Sampling With and Without Replacement Lectures and recitation
13. Week Random Samples, The Sampling Distribution of the Mean, The Sampling Distribution of the Mean: Finite Population Lectures and recitation
14. Week Interval Estimation Lectures and recitation
15. Week
16. Week
17. Week
Assessments
Evaluation tools Quantity Weight(%)
Midterm(s) 1 40
Final Exam 1 60


Program Outcomes
PO-1Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in the solution of complex engineering problems.
PO-2Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose.
PO-3Ability to design a complex systemi process, device or product under realistic constraints and conditions, in such a way as to meet the desired results; ability to apply modern design methods for this purpose.
PO-4Ability to select and use modern techniques and tools needed for analyzing and Solving complex problems encountered in engineering practice; ability to employ information technologies effectively.
PO-5Ability to design and conduct experiments, gather data, analyze and interpret results for investing complex engineering problems or discipline specific research questions.
PO-6Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
PO-7Ability to communicate effectivley, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instruction.
PO-8Awareness of the need for lifelong learning; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
PO-9Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices.
PO-10Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development.
PO-11Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions.
Learning Outcomes
LO-1Compute permutations and combinations. (KNOWLEDGE)
LO-2Understand what a random variable is. (KNOWLEDGE)
LO-3Define basic probability terminology, e.g., experiment, outcome, sample space, event, etc. (KNOWLEDGE)
LO-4Describe a probability distribution and a probability density function. (KNOWLEDGE)
LO-5Understand mathematical expectation. (KNOWLEDGE)
LO-6Explain joint, marginal and conditional probability distributions. (KNOWLEDGE)
LO-7Understand where/when special probability distribution functions should be used. (KNOWLEDGE)
LO-8Describe the various special continuous distributions. (KNOWLEDGE)
LO-9Able to solve problems independently. (SKILL)
Course Assessment Matrix:
Program Outcomes - Learning Outcomes Matrix
 PO 1PO 2PO 3PO 4PO 5PO 6PO 7PO 8PO 9PO 10PO 11