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Course Description Course Name : Statistical Analysis Course Code : IEM 761 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Asst.Prof.Dr. GÜLSEN KIRAL Learning Outcomes of the Course : Gains the ability to apply the knowledge of statistical analysis techniques Gains the ability to analyze and evaluate the data and to design and do experiment Identifies,formulates and solves the problems in the field Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : NONE Aim(s) of Course : It is necessary to accurately analyze the data and interpret the results and evaluations of the examined case, which is very important in scientific research stage. In addition to this, businesses face many problems and they are waiting to be solved. Mostly, thanks to the results of the analyzed data, problems can be solved. This course aims to provide the students with the skills of gathering data correctly about the examined case, filtering them to be analyzed and analyzing the data and interpreting it by using SPSS, Minitab, SPLUS and choosing the suitable technique which is due to the aim. The students who take this lesson, have competence in solving the examined phenomenon like a researcher or academician. Course Contents : This lesson covers the concept of data and identify the data, choosing statistical analysis according to the research aim and in this context, descriptive statistics, tests of comparing two and more groups non-parametric tests, correlation and regression analysis, factor analysis, cluster analysis which are multivariable statistical analysis. Language of Instruction : Turkish Work Place : graduate classroom (1. Blok) Com.Lab. (1. Blok)   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 The concept of data and data collection techniques Reading the relavant parts in the source books Lecture 2 Choosing the statistical technique for data analysis Reading the relavant parts in the source books Lecture and computer applications 3 Introduction to Minitab, Plus SPSS Package Programmes Reading the relavant parts in the source books Lecture and computer applications 4 Data processes on computer Reading the relavant parts in the source books Lecture and computer applications 5 Data processes on computer Reading the relavant parts in the source books Lecture and computer applications 6 Descriptive Statistics Reading the relavant parts in the source books Lecture and computer applications 7 Tests for comparing two groups Reading the relavant parts in the source books Lecture and computer applications 8 Variance Analysis Reading the relavant parts in the source books Lecture and computer applications 9 Midterm exam 10 Non Parametric Tests Reading the relavant parts in the source books Lecture and computer applications 11 Homework presentation 12 Linear Regression and Correlation Analyses Reading the relavant parts in the source books Lecture and computer applications 13 Factor Analysis Reading the relavant parts in the source books Lecture and computer applications 14 Cluster Analysis Reading the relavant parts in the source books Lecture and computer applications 15 Homework presentation 16/17 Final exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Tatlıdil, H.(1992). Uygulamalı çok Değişkenli İstatistiksel Analiz, Ankara.  Jobson, J, D.(1991). Applied Multivariate Data Analysis, Volume I-II, Springer- Verlag, New York.  Özdamar, K.( 1999). Paket Programlar ile İstatistiksel Veri Analizi Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 60     Homeworks/Projects/Others 2 40 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 Explains Econometric concepts 2 2 Equipped with the foundations of Economics, develops Economic models 1 3 Models problems using the knowledge of Mathematics, Statistics, and Econometrics 2 4 Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems 3 5 Collects, edits, and analyzes data 4 6 Uses advanced software packages concerning Econometrics, Statistics, and Operation Research 3 7 Develops the ability to use different resources in an area which has not been studied in the scope of academic rules, synthesizes the information gathered, and gives effective presentations 3 8 Speaks Turkish and at least one other foreign language in accordance with the requirements of academic and business life. 2 9 Questions traditional approaches and their implementation and develops alternative study programs when required 2 10 Recognizes and implements social, scientific, and professional ethic values 1 11 Gives a consistent estimate for the model and analyzes and interprets its results 3 12 Takes responsibility individually and/or as a member of a team; leads a team and works effectively 2 13 Defines the concepts of statistics, operations research and mathematics. 3 14 Knowing the necessity of life-long learning, follows the latest developments in the field of study and improves himself continiously 2 15 Follows the current issues, and interprets the data about economic and social events. 1 16 Understands and interprets the feelings, thoughts and behaviours of people and expresses himself/herself orally and in written form efficiently 2 * 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 2 14 28     Mid-term Exams (Written, Oral, etc.) 1 14 14     Final Exam 1 14 14 Total Workload: 140 Total Workload / 25 (h): 5.6 ECTS Credit: 6