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Course Description Course Name : Computer Aided Statistical Methods -I Course Code : ISB-539 Course Type : Optional Level of Course : Second Cycle Year of Study : 1 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Assoc.Prof.Dr. GÜZİN YÜKSEL Learning Outcomes of the Course : Have the ability to analyze data by using SPSS. Have the ability to apply statistics structures using SPSS in a business environment. Develop the skills in problem analysis and problem solving Develop the skills in data handling and manipulation Have the ability to interpret the data. Have the ability to find solutions to the problems of operational work. Learn how to use SPSS. Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : The purpose of this course is to supply the students with the ability to analyze and interpret the problems both theoretically and practically by using the data analysis program used in different fields and basic statistical methods in the SPSS. Course Contents : Introduction to basic computer skills, Preparation of data, Descriptive Statistics, Correlation, Statistical Tests, ANOVA Analysis, Regression Analysis, Coding survey data, Reliability Analysis Language of Instruction : Turkish Work Place : Laboratory   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 (With computerized statistical software), Data Entry, and File Operations. Reading the source ,preparing the project Face to face and computer application 2 Creating Tables and Charts. Reading the source ,preparing the project Face to face and computer application 3 Descriptive Statistics. Reading the source ,preparing the project Face to face and computer application 4 Hypothesis Testing. Reading the source ,preparing the project Face to face and computer application 5 Cross Tables and Correlation Coefficients. Reading the source ,preparing the project Face to face and computer application 6 Survey Editing and Multiple Answer Analysis. Reading the source ,preparing the project Face to face and computer application 7 Analysis of Variance and Multiple Comparisons. Reading the source ,preparing the project Face to face and computer application 8 Midterm Review the topics discussed in the lecture notes and sources Written exam 9 Correlation Analysis and Curve Fitting. Reading the source ,preparing the project Face to face and computer application 10 Regression Analysis 1. Reading the source ,preparing the project Face to face and computer application 11 Regression Analysis 2. Reading the source ,preparing the project Face to face and computer application 12 Goodness of Fit And Tests Based on Equality of Variances. Reading the source ,preparing the project Face to face and computer application 13 Reliability Analysis Reading the source ,preparing the project Face to face and computer application 14 Factor Analysis. Reading the source ,preparing the project Face to face and computer application 15 Parametric Tests Reading the source ,preparing the project Face to face and computer application 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)  Kazım Özdamar (2010)Paket programlar ile istatistiksel veri analizi 1, Kaan Kitabevi  Hamza Erol(2013) SPSS paket programı ile İstatistiksel Veri Analizi, Akademisyen Kitabevi. Required Course Material(s)   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 0 60     Homeworks/Projects/Others 5 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 Possess advanced level of theoretical and applicable knowledge in the field of Probability and Statistics. 4 2 Conduct scientific research on Mathematics, Probability and Statistics. 4 3 Possess information, skills and competencies necessary to pursue a PhD degree in the field of Statistics. 4 4 Possess comprehensive information on the analysis and modeling methods used in Statistics. 3 5 Present the methods used in analysis and modeling in the field of Statistics. 4 6 Discuss the problems in the field of Statistics. 4 7 Implement innovative methods for resolving problems in the field of Statistics. 5 8 Develop analytical modeling and experimental research designs to implement solutions. 5 9 Gather data in order to complete a research. 5 10 Develop approaches for solving complex problems by taking responsibility. 5 11 Take responsibility with self-confidence. 5 12 Have the awareness of new and emerging applications in the profession 4 13 Present the results of their studies at national and international environments clearly in oral or written form. 3 14 Oversee the scientific and ethical values during data collection, analysis, interpretation and announcment of the findings. 5 15 Update his/her knowledge and skills in statistics and related fields continously 4 16 Communicate effectively in oral and written form both in Turkish and English. 5 17 Use hardware and software required for statistical applications. 5 * 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 5 8 40     Mid-term Exams (Written, Oral, etc.) 0 0 0     Final Exam 1 20 20 Total Workload: 144 Total Workload / 25 (h): 5.76 ECTS Credit: 6