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Course Description Course Name : Probability And Statistics I Course Code : ENM219 Course Type : Compulsory Level of Course : First Cycle Year of Study : 2 Course Semester : Fall (16 Weeks) ECTS : 5 Name of Lecturer(s) : Assoc.Prof.Dr. MAHMUDE REVAN ÖZKALE Learning Outcomes of the Course : Organize and analyze data Understand measures of central tendency and dispersion measures Solve the problems of permutations, combinations, ordered and unordered disruptions Use the probability of an event, probability axioms, and some of the rules of probability Apply gonditional probability, independent events, Bayes´ theorem Know the concept of a random variable, the distribution of a random variable Describe the expected value of a random variable, the variance and their properties Use the concepts of moments, Chebyshew inequality Learn some discrete distributions Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : Learn to design and analyze the data , to learn permutation, combination and the basic concepts of probability, random variables and their properties and discrete probability distributions . Course Contents : Measures of central tendency , permutation, combination, random variables, discrete probability distributions Language of Instruction : Turkish Work Place : Endüstri Mühendisliği Derslikleri   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Data Organization, Frequency Distribution, Graphical Representations Source reading Lecture, problem solving 2 Measures of Central Tendency, Measures of Dispersion Source reading Lecture, problem solving 3 Skewness and Kurtosis Measure, Permutation, Combination Source reading Lecture, problem solving 4 Ordered and unordered disruptions, Binomial Expansion Source reading Lecture, problem solving 5 Probability of an event, Probability Axioms, Conditional Probability Source reading Lecture, problem solving 6 Independent Events, Bayes´ Theorem Source reading Lecture, problem solving 7 The Concept of Random Variable, Distribution of Discrete Random Variable Source reading Lecture, problem solving 8 Midterm exam Review the topics discussed in the lecture notes and sources Written exam 9 Distribution of Discrete Random Variable Source reading Lecture, problem solving 10 Distribution of Continous Random Variable Source reading Lecture, problem solving 11 Expected Value, Variance and their properites Source reading Lecture, problem solving 12 Moments, Chebyshew Inequality Source reading Lecture, problem solving 13 Bernouli, Binomial, Multinomial Distribution Source reading Lecture, problem solving 14 Geometric distribution, Negative Binomial Distribution, Hypergeometric Distribution Source reading Lecture, problem solving 15 Poisson and Discrete Uniform Distribution Source reading Lecture, problem solving 16/17 Final exam Review the topics discussed in the lecture notes and sources Written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  1. Akdeniz,F. (2010). Olasılık ve İstatistik , Nobel Kitabevi Required Course Material(s)  1. Montgomery, D. C., Runger, G. C. (2002). Applied Statistics and Probability for Engineers. John Wiley and Sons   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 100     Homeworks/Projects/Others 0 0 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Can collect and analyze data required for industrial engineering problems ,develops and evaluates alternative solutions. 5 2 Has sufficient background on topics related to mathematics, physical sciences and industrial engineering. 5 3 Gains ability to use the acquired theoretical knowledge on basic sciences and industrial engineering for describing, formulating and solving an industrial engineering problem, and to choose appropriate analytical and modeling methods. 5 4 Gains ability to analyze a service and/or manufacturing system or a process and describes, formulates and solves its problems . 0 5 Gains ability to choose and apply methods and tools for industrial engineering applications. 0 6 Can access information and to search/use databases and other sources for information gathering. 2 7 Works efficiently and takes responsibility both individually and as a member of a multi-disciplinary team. 0 8 Appreciates life time learning; follows scientific and technological developments and renews himself/herself continuously. 0 9 Can use computer software in industrial engineering along with information and communication technologies. 0 10 Can use oral and written communication efficiently. 1 11 Has a conscious understanding of professional and ethical responsibilities. 1 12 Uses English skills to follow developments in industrial engineering and to communicate with people in his/her profession. 0 13 Has a necessary consciousness on issues related to job safety and health, legal aspects of environment and engineering practice. 0 14 Becomes competent on matters related to project management, entrepreneurship, innovation and has knowledge about current matters in industrial engineering. 0 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 4 56     Out of Class Study (Preliminary Work, Practice) 14 4 56 Assesment Related Works     Homeworks, Projects, Others 0 0 0     Mid-term Exams (Written, Oral, etc.) 1 10 10     Final Exam 1 15 15 Total Workload: 137 Total Workload / 25 (h): 5.48 ECTS Credit: 5