Probability and Statistics I (MATH291) Course Detail

Course Name	Course Code	Season	Lecture Hours	Application Hours	Lab Hours	Credit	ECTS
Probability and Statistics I	MATH291		3	0	0	3	5

Pre-requisite Course(s)
None

Course Language	English
Course Type	N/A
Course Level	Bachelor’s Degree (First Cycle)
Mode of Delivery	Face To Face
Learning and Teaching Strategies	Lecture, Question and Answer, Problem Solving.
Course Coordinator
Course Lecturer(s)
Course Assistants
Course Objectives	In addition to some tools for classification, summarization and making sense of data, to provide students with basic probability knowledge and certain probability distributions
Course Learning Outcomes	The students who succeeded in this course; Upon completing of the course, students are expected to: 1- learn how to organize a set of data 2- be able to summarize the data by using the measures of central tendency and dispersion 3- calculate the probability with the assistance of basic concept of probability including some counting techniques, permutations and combinations 4- have the ability to use conditional probability, Bayesian approach and statistically independency within probability problems 5- be able to calculate the mean and standard deviation with expected value concept by understanding the difference between discrete and continuous random variables, 6- have the ability to use some probability distributions such as binomial and normal probability functions.
Course Content	Basic definitions, tables and graphs, central tendency measures, central dispersion measures, probability concept, conditional probability, Bayesian approach, random variables, expected value, binomial and normal distributions.

Weekly Subjects and Releated Preparation Studies

Week	Subjects	Preparation
1	Basic Definitions, Frequency Distributions	pp. 3-5
2	Relative, Cumulative, Cumulative Relative Frequency Distributions, Graphs, Stem and Leaf Display	pp. 24-28
3	Central Tendency Measures; Mean, Median and Mode for Unclassified and Classified Data	pp. 73-76
4	Central Dispersion Measures; Variance, Standard Deviation, Coefficient of Variation, Chebyshev Theorem	pp. 93-100
5	Probability Concept, Random Event-Experiment, Sample Space,	pp. 127-130
6	Classical / Postrerior Probability Definitions , Rule of Counting; Permutation and Combination, Multiplication Rule	pp. 135-137
7	Midterm Exam
8	Venn Diagrams, Contingency Table, Conditional Probability	pp. 138-140
9	Bayesian Approach, Statistical Indpendency	pp. 142-145
10	Random Variables, Probability Function	pp. 147-150
11	Expected Value and Its Properties, Mean and Standard Deviation	pp. 155-157
12	Binomial Distribution	pp. 167-168
13	Normal Distribution, Standard Normal Variable, Z table	pp. 182-185
14	Problems on Normal Distribution and Vice-Verse Usage of Z table (Cut-off value )	pp. 199-205
15	Review
16	Final Exam

Sources

Course Book	1. D.H. Sanders, R. K. Simidt, Statistics, A First Course, 1990
Other Sources	2. D.H. Sanders, R. K. Simidt, Statistics, A First Course, 1990

Evaluation System

Requirements	Number	Percentage of Grade
Attendance/Participation	-	-
Laboratory	-	-
Application	-	-
Field Work	-	-
Special Course Internship	-	-
Quizzes/Studio Critics	-	-
Homework Assignments	2	10
Presentation	-	-
Project	-	-
Report	-	-
Seminar	-	-
Midterms Exams/Midterms Jury	2	50
Final Exam/Final Jury	1	40
Toplam	5	100

Percentage of Semester Work	60
Percentage of Final Work	40
Total	100

Course Category

Core Courses	X
Major Area Courses
Supportive Courses
Media and Managment Skills Courses
Transferable Skill Courses

The Relation Between Course Learning Competencies and Program Qualifications

#	Program Qualifications / Competencies	Level of Contribution
#	Program Qualifications / Competencies	1	2	3	4	5
1	Adequate knowledge in mathematics, science and subjects specific to the Materials Engineering; the ability to apply theoretical and practical knowledge of these areas to solve complex engineering problems and to model and solve of materials systems
2	Understanding of science and engineering principles related to the structures, properties, processing and performance of Materials systems
3	Ability to identify, define, formulate and solve complex engineering problems; selecting and applying proper analysis and modeling techniques for this purpose
4	Ability to design and choose proper materials for a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; the ability to apply modern design and materials selection methods for this purpose
5	Ability to develop, select and utilize modern techniques and tools essential for the analysis and solution of complex problems in Materails Engineering applications; the ability to utilize information technologies effectively
6	Ability to design and conduct experiments, collect data, analyse and interpret results using statistical and computational methods for complex engineering problems or research topics specific to Materials Engineering
7	Ability to work effectively in inter/inner disciplinary teams; ability to work individually
8	Effective oral and written communication skills in Turkish; knowlegde of at least one foreign language; the ability to write effective reports and comprehend written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions
9	Recognition of the need for lifelong learning; the ability to access information; follow recent developments in science and technology with continuous self-development
10	Ability to behave according to ethical principles, awareness of professional and ethical responsibility; knowledge of standards used in engineering applications
11	Knowledge on business practices such as project management, risk management and change management; awareness in entrepreneurship and innovativeness; knowledge of sustainable development
12	Knowledge of the effects of Materials Engineering applications on the universal and social dimensions of health, environment and safety, knowledge of modern age problems reflected on engineering; awareness of legal consequences of engineering solutions

ECTS/Workload Table

Activities	Number	Duration (Hours)	Total Workload
Course Hours (Including Exam Week: 16 x Total Hours)
Laboratory
Application
Special Course Internship
Field Work
Study Hours Out of Class	14	3	42
Presentation/Seminar Prepration
Project
Report
Homework Assignments
Quizzes/Studio Critics
Prepration of Midterm Exams/Midterm Jury	2	10	20
Prepration of Final Exams/Final Jury	1	15	15
Total Workload			77