DEGREE PROGRAMS  Associate's Degree (Short Cycle)  Bachelor’s Degree (First Cycle)  Master’s Degree (Second Cycle)

Course Description Course Name : Linear Models Course Code : İSB402 Course Type : Compulsory Level of Course : First Cycle Year of Study : 4 Course Semester : Spring (16 Weeks) ECTS : 4 Name of Lecturer(s) : Assoc.Prof.Dr. DENİZ ÜNAL Learning Outcomes of the Course : Have knowledge of Linear Models Means, variances and covariances of linear functions of random vectors Have knowledge of quadratic form and its properties Have knowledge of matrix notations of full rank models Learn estiamtion, hypothesis and confidence intervals in full and non-full rank models Learn the reparametrization in non-full rank model Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : Student learns, quadratic forms, distribution of quadratic forms, estimation in the full rank model, estimation in the non full rank model, hypothesis testing and estimation in the less than full rank model Course Contents : Matrix Operations Quadratic Forms and Their Distributions, Independence of Quadratic Forms. Full Rank Model.Estimation and Hypothesis Testing in the Full Rank Model, Less than Full Rank Model,Estimation in the Less than Full Rank Model. Language of Instruction : Turkish Work Place : Faculty of Arts and Sciences Annex Classrooms   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Matrix and vector notation Studying related sources Face to face description, discussion and problem solving 2 Matrix and vector notation, eigenvalue and eigenvectors Studying related sources Face to face description, discussion and problem solving 3 Mean and variance of quadratic forms Studying related sources Face to face description, discussion and problem solving 4 Distributions of some special quadratic forms Studying related sources Face to face description, discussion and problem solving 5 Chi-square, t-distribuiton, F-distribution, independence of quadratic forms Studying related sources Face to face description, discussion and problem solving 6 Matrix notations and estiamtion in full rank models Studying related sources Face to face description, discussion and problem solving 7 Parameter estimation and confidence intervals in full rank models Studying related sources Face to face description, discussion and problem solving 8 Mid term exam Studying related sources written exam 9 Parameter estimation and confidence intervals in full rank models Studying related sources Face to face description, discussion and problem solving 10 Hypothesis testing in full rank models Studying related sources Face to face description, discussion and problem solving 11 Estiamtion and Hypothesis testing in non-full rank models Studying related sources Face to face description, discussion and problem solving 12 Reparametrization in non-full rank models Studying related sources Face to face description, discussion and problem solving 13 Testable hypotheses in non-full rank models Studying related sources Face to face description, discussion and problem solving 14 Parameter estimation and confidence intervals in non-full rank models Studying related sources Face to face description, discussion and problem solving 15 Parameter estimation and confidence intervals in non-full rank models Studying related sources Face to face description, discussion and problem solving 16/17 Final exam Studying related sources written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Linear Models in Statistics, Rencher, Alvin C., John Wiley&Sons, INC., New York, USA, 2010.  Lineer Modeller , Öztürk, F.; Akdeniz, F. , Ankara Üniversitesi Yayınları, 1996. Required Course Material(s)   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 Utilize computer systems and softwares 0 2 Apply the statistical analyze methods 4 3 Make statistical inference(estimation, hypothesis tests etc.) 4 4 Generate solutions for the problems in other disciplines by using statistical techniques 1 5 Discover the visual, database and web programming techniques and posses the ability of writing programme 0 6 Construct a model and analyze it by using statistical packages 0 7 Distinguish the difference between the statistical methods 3 8 Be aware of the interaction between the disciplines related to statistics 0 9 Make oral and visual presentation for the results of statistical methods 0 10 Have capability on effective and productive work in a group and individually 0 11 Develop scientific and ethical values in the fields of statistics-and scientific data collection 0 12 Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics 4 13 Emphasize the importance of Statistics in life 0 14 Define basic principles and concepts in the field of Law and Economics 0 15 Produce numeric and statistical solutions in order to overcome the problems 2 16 Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events 5 17 Use proper methods and techniques to gather and/or to arrange the data 2 18 Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs 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 3 42     Out of Class Study (Preliminary Work, Practice) 14 3 42 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: 109 Total Workload / 25 (h): 4.36 ECTS Credit: 4