FACULTY OF SCIENCES
STATISTICS
 Course Name INTRODUCTION TO PROBABILITY II Semester Course Code Theoretical / Practice time ECTS 2 2705254 4 / 0 5
 Course Degree Bachelor's degree Course Language Turkish Format of Delivery: Face to Face Course Coordinator Prof.Dr. Coşkun KUŞ Coordinator e-mail coskun selcuk.edu.tr Instructors Asistant Instructors Course Objectives Learning basic probability rules
 Basic Sciences Engineering Scinces Social Sciences Educational Sciences Artistic sciences Medical Science Agricultural sciences 70 30 0 0 0 0 0
 Course Learning Methods and Techniquies
 Week Course Content Resource 1 Random variable concept, distribution function and its properties Akdeniz, F. Probability and Statistics 2 Probability function and probability density functions Shahbazov, A., Introduction to probability theory 3 Transformations of Random Variables, Probability integral transformation and randomize number from distribution Probability and Statistics 4 Expected value, variance, standard deviation, mod, median, quantiles and properties Probability and Statistics 5 Life function, skewness and kurtosis coefficients, variation coefficient, hazard functions Probability and Statistics 6 moment generating, probability generating and acteristic functions and properties Introduction to probability theory 7 Random vector, multivariate distribution function and its properties, common probability density function, marginal distribution Introduction to probability theory 8 Independence of random variables , conditional distributions, conditional expected value, covariance and correlation coefficients and matrix and properties Introduction to probability theory 9 Some discrete distributions: uniform, bernoulli, binomial, poisson, negative binomial, hypergeometric, multinomial distribution A Course in Mathematical Statistics 10 Midterm Exam A Course in Mathematical Statistics 11 Some continuous distributions: uniform, normal, exponential, Weibull, Burr XII, gamma, chi-square, beta, t,F A Course in Mathematical Statistics 12 Markov, Chebysheff, Jensen, other inequalities and applications Fundamentals of Probability with Stochastic Processes 13 Some convergence types and relations among them, weak and strong law of large numbaers,central limit theorem Fundamentals of Probability with Stochastic Processes 14 Slutsky theorem, delta method and related examples, convergence of binomial distribution to poisson and normal distribution Fundamentals of Probability with Stochastic Processes 15 Preparation for final exam Fundamentals of Probability with Stochastic Processes
 Assesment Criteria Mid-term exam Final exam Quantity Percentage Quantity Percentage Term Studies : - - - - Attendance / Participation : - - - - Practical Exam : - - - - Special Course Exam : - - - - Quiz : - - - - Homework : 4 20 2 20 Presentations and Seminars : - - - - Projects : - - - - Workshop / Laboratory Applications : - - - - Case studies : - - - - Field Studies : - - - - Clinical Studies : - - - - Other Studies : - - - - Mid-term exam 1 20 - - Final exam - - 1 40
 ECTS WORK LOAD TABLE Number Duration Course Duration : 14 4 Classroom Work Time : 14 3 Presentations and Seminars : - - Course Internship : - - Workshop / Laboratory Applications : - - Field Studies : - - Case studies : - - Projects : - - Homework : - - Quiz : - - Mid-term exam : 1 27 Final Exam : 1 25 ECTS 5
 No COURSE LEARNING OUTCOMES CONTRIBUTION D.Ö.Ç. 1 1. to develop the ability of statistical analysis with basic concepts and methodological knowledge 4 D.Ö.Ç. 2 2. to be able to use and comment statistical methods and distrubitions 4 D.Ö.Ç. 3 3. to be able to test population parameters 3 D.Ö.Ç. 4 4. to be able to carry out interval estimation 3 D.Ö.Ç. 5 5. to be able to derive statistical inference 3
 * 1: Zayıf - 2: Orta - 3: İyi - 4: Çok İyi
 COURSE LEARNING OUTCOMES AND PROGRAM OUTCOMES AND RELATIONSHIPS MATRIX

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