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Course Description Course Name : Data Analysis Course Code : EM 210 Course Type : Optional Level of Course : First Cycle Year of Study : 2 Course Semester : Spring (16 Weeks) ECTS : 4 Name of Lecturer(s) : Assoc.Prof.Dr. S.BİLGİN KILIÇ Learning Outcomes of the Course : Provides the ability to perform statistical analysis using Computer Reinforces the basic statistical concepts Provides the learning of the basic properties of SPSS Allows statistical hypothesis testing, t-test, chi-square test and ANOVA test in decision analysis Gives the ability to perform correlation analysis, multiple regression analysis and analyzes the interpretation of outcomes Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : To gain the abilty to process of inspecting, cleaning, transforming, and modeling the data with the goal of discovering useful information, suggesting conclusions, and supporting decision making process. Course Contents : Remember and reinforce some of the basic concepts of probability theory and statistics, random number generation, Monte Carlo simulation, some basic statistical distributions (t, z) is obtained by the analysis of sampling theory and the central limit theorem simulation comprehend the principles of statistical decision theory, basic SPSS characteristics, variables and calculation of summary descriptive statistics, hypothesis testing, one-way and two-way ANOVA tes, one-sample t-test and independent two-sample t-test, chi-square test, correlation analysis, a simple regression model, estimation and interpretation of model outputs, the basic assumptions of regression model Language of Instruction : Turkish Work Place : Classroom, Compurer Labrotary   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Remember and reinforce some of the basic concepts of probability theory and statistics Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the labrotory 2 Random number generation, Monte Carlo simulation, obtaining some basic statistical distributions (t, z) Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the laboratory 3 Simulation analysis and teaching of the principles of sampling theory and the central limit theorem Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the labrotory 4 The basic features of SPSS, variables and calculation of summary descriptive statistics Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the laboratory 5 Statistical decision theory Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the laboratory 6 One-way and two-way hypothesis testing Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the laboratory 7 Single-sample t-test and independent two-sample t-test Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the llaboratory 8 Midterm examination - - 9 Chi-square goodness of fit test Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the llaboratory 10 One-way ANOVA test Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the llaboratory 11 Two-way ANOVA test Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the labrotory 12 Correlation Analysis Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the laboratory 13 Simple regression model estimation and interpretation of model outputs Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the laboratory 14 Multi variable regression model estimation and interpretation of model outputs Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the labrotory 15 Basic assuptions of regression model Students will be prepared by studying relevant subjects from source books according to the weekly program Lectures with the computer application in the laboratory 16/17 Final examination - -   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Methods of Data Analysis with SPSS, U. Erman EYMEN This e-book is downloadable free from" "www.istatistikmerkezi.com" adress Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 80     Homeworks/Projects/Others 10 20 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 Models problems with Mathematics, Statistics, and Econometrics 4 2 Explains Econometric concepts 4 3 Estimates the model consistently and analyzes & interprets its results 5 4 Acquires basic Mathematics, Statistics and Operation Research concepts 4 5 Equipped with the foundations of Economics, and develops Economic models 3 6 Describes the necessary concepts of Business 3 7 Acquires the ability to analyze, benchmark, evaluate and interpret at conceptual levels to develop solutions to problems 5 8 Collects, edits, and analyzes data 5 9 Uses a package program of Econometrics, Statistics, and Operation Research 5 10 Effectively works, take responsibility, and the leadership individually or as a member of a team 2 11 Awareness towards life-long learning and follow-up of the new information and knowledge in the field of study 1 12 Develops the ability of using different resources in the form of academic rules, synthesis the information gathered, and effective presentation in an area which has not been studied 4 13 Uses Turkish and at least one other foreign language, academically and in the business context 2 14 Good understanding, interpretation, efficient written and oral expression of the people involved 2 15 Questions traditional approaches and their implementation while developing alternative study programs when required 4 16 Recognizes and implements social, scientific, and professional ethic values 4 17 Follows actuality, and interprets the data about economic and social events 4 18 Improves himself/herself constantly by defining educational requirements considering interests and talents in scientific, cultural, art and social fields besides career development 3 * 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 10 2 20     Mid-term Exams (Written, Oral, etc.) 1 3 3     Final Exam 1 3 3 Total Workload: 110 Total Workload / 25 (h): 4.4 ECTS Credit: 4