• Course Information
  • Course Title Code Semester Laboratory+Practice (Hour) Pool Type ECTS
    Multivariate Data Analysis in Engineeering KMÜ528 FALL-SPRING 3+0 University E 6
    Learning Outcomes
    1-Applies multivariate data analysis to different data types
    2-Relate multivariate data techniques to research objectives
    3-Test the assumptions of multivariate analysis
    4-Interpet the obtained results in a realistic way
    5-Conduct technical and scientific research
  • ActivityPercentage


    NumberTime (Hours)Total Workload (hours)
    Course Duration (Weeks x Course Hours)14342
    Classroom study (Pre-study, practice)14684
    Short-Term Exams (exam + preparation) 2021224
    Midterm exams (exam + preparation)3011515
    Laboratory 0000
    Final exam (exam + preparation) 5011515
    Other 0000
    Total Workload (hours)   180
    Total Workload (hours) / 30 (s)     6 ---- (6)
    ECTS Credit   6
  • Course Content
  • Week Topics Study Metarials
    1 Introduction Lecture Note
    2 Basic Statistics Lecture Note
    3 Basic Statistics Lecture Note
    4 Introduction to Multivariate Data Analysis in Engeineering Lecture Note
    5 Data Modelling Techniques in Engineering Lecture Note
    6 Manipulation of the Data Lecture Note
    7 Factor Analysis in Engineering Lecture Note
    8 Midterm exams
    9 Regression Analysis in Engineering Lecture Note
    10 Discriminant Analysis in Engineering Lecture Note
    11 Logistic Regression in Engineering Lecture Note
    12 Cluster Analysis in Engineering Lecture Note
    13 Multivariate Variance Analysis in Engineering Lecture Note
    14 Investigating the problems in Regression Lecture Note
    15 Investigating the problems in Cluster Analysis Lecture Note
    Prerequisites -
    Language of Instruction Turkish
    Coordinator -
    Instructors -
    Assistants -
    Resources Multivariate Data Analysis ,Joseph F. Hair (Author), William C. Black (Author), Barry J. Babin (Author), Rolph E. Anderson (Author), Pearson Education, 7th Edition, 2009. Byrne (2009) Structural Equation Modeling With Amos: Basic Concepts, Applications, And Programming, 2nd Edition. Taylor And Francis Hair, J., F., Anderson, R., E., (2010). Multivariate Data Anlaysis, Lousiana State Universit.
    Supplementary Book -
    Goals Frequently used analyzes in engineering and basic multivariate data analysis techniques will be explained. The analysis will focus on the selection of the appropriate method, the analysis of the assumptions, the analysis and interpretation of the results.
    Content Introduction Basic statistics Introduction to multivariate statistics in engineering Data modeling techniques in engineering Data manipulation and data adaptation Factor analysis in engineering Regression analysis in engineering Discriminant analysis in engineering Logistic regression in engineering Clustering analysis in engineering Multivariate analysis of variance in engineering (Multiple Anova, MANOVA) Regression problem solutions Cluster analysis problem solutions
  • Program Learning Outcomes
  • Program Learning Outcomes Level of Contribution
    1 To make scientific researches and reach the knowledge in depth; analyze interpret and apply the knowledge. 3
    2 To have knowledge about current technics, methods and their limitations applied in engineering. 4
    3 To have the ability to define and practice the knowledge by using scientific methods and limited or restricted data and to use the knowledge from other disciplines. 3
    4 To have awareness about the new and developing implementations in engineering and to research and learn them when required. -
    5 To define and formulate problems concerning chemical engineering , to develop methods for solution and to apply innovative methods for solutions. 3
    6 To develop new and/or original ideas and methods, to design complex systems and processes and to improve alternative/innovative solutions. 3
    7 To design and apply theoretical, applied and simulative researches, to analyse and solve complicated problems encountered during these processes. 3
    8 To lead in multidisciplinary teams, improve solutions in complex situation and to work independently and take responsibility. 4
    9 To use English at least in European Language Portfolio B2 level for both oral and written skills. -
    10 To declare the results and processes of studies both orally and written in national and international platforms with a systematically and concisely manner. 4
    11 To have awareness about the social, enviromental, health, security and law perspectives and project management and career applications of engineering practices and restrictions of all these. -
    12 To regard social, scientific liabilities, and ethics during the collection, evaluation, and publication steps of data. 3
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