Course Code	Semester	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-1 Adequate 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-2 Ability to formulate, and solve complex engineering problems; ability to select and apply proper analysis and modeling methods for this purpose. PO-3 Ability 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-4 Ability 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-5 Ability to design and conduct experiments, gather data, analyze and interpret results for investing complex engineering problems or discipline specific research questions. PO-6 Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually. PO-7 Ability 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-8 Awareness 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-9 Knowledge on behavior according ethical principles, professional and ethical responsibility and standards used in engineering practices. PO-10 Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development. PO-11 Knowledge 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-1 Compute permutations and combinations. (KNOWLEDGE) LO-2 Understand what a random variable is. (KNOWLEDGE) LO-3 Define basic probability terminology, e.g., experiment, outcome, sample space, event, etc. (KNOWLEDGE) LO-4 Describe a probability distribution and a probability density function. (KNOWLEDGE) LO-5 Understand mathematical expectation. (KNOWLEDGE) LO-6 Explain joint, marginal and conditional probability distributions. (KNOWLEDGE) LO-7 Understand where/when special probability distribution functions should be used. (KNOWLEDGE) LO-8 Describe the various special continuous distributions. (KNOWLEDGE) LO-9 Able to solve problems independently. (SKILL)

Course Assessment Matrix:

	PO 1	PO 2	PO 3	PO 4	PO 5	PO 6	PO 7	PO 8	PO 9	PO 10	PO 11