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Code ENS 505
Term 201802
Title Methods of Statistical Inference
Faculty Faculty of Engineering and Natural Sciences
Subject Engineering Sciences(ENS)
SU Credit 3
ECTS Credit 10.00
Instructor(s) Hans Frenk -frenk@sabanciuniv.edu,
Language of Instruction English
Level of Course Doctoral
Master
Type of Course Click here to view.
Prerequisites
(only for SU students)
--
Mode of Delivery Formal lecture
Planned Learning Activities Discussion based learning,Simulation
Content

The main objective of this course is to review the basic concepts of the theory of statistics and further develop understanding of some fundamental applied statistical methods. Our emphasis will be on applications of the theory in the development of statistical procedures. Practical applications of statistics to some problems in engineering and management will be given. Computational assignments will be given to help the students to understand the concepts and to have an opportunity to practice applying them. Computer aided analysis of data; fundamental concepts of statistics and related distributions; design of experiments and analysis of variance; regression and correlation analysis; methods for stationary time series data; linear methods for classification.

Objective

The main objective of this course is to review the basic concepts of the theory of statistics and further develop an advanced-level understanding of fundamental statistical inference procedures.

Learning Outcome

Describe the types of statistics: descriptive statistics, parametric inferential statistics and non-parametric statistics
Obtain descriptive statistics and employ the basic graphical visualization techniques to summarize and analyze the data
Describe the general properties of estimators: biasedness, mean square error, consistency and efficiency
Determine the point estimators of unknown parameters of interest based on three widely-applied methods: maximum likelihood estimation, the method of moments and Bayes estimation
Derive the confidence interval estimators of unknown parameters of interest based on three approaches: exact methods, approximations based on the large sample properties and approximations using bootstrapping
Discuss the basic principals of the methods of hypothesis testing
Identify key points in Diagnostics and Remedial Measures for the regression analysis
Perform simple and multiple regression analyses by the help of a software such as SPSS and MATLAB.

Programme Outcomes
 
Common Outcomes For All Programs
1 Develop and deepen the current and advanced knowledge in the field with original thought and/or research and come up with innovative definitions based on Master's degree qualifications 4
2 Conceive the interdisciplinary interaction which the field is related with ; come up with original solutions by using knowledge requiring proficiency on analysis, synthesis and assessment of new and complex ideas. 4
3 Evaluate and use new information within the field in a systematic approach. 4
4 Develop an innovative knowledge, method, design and/or practice or adapt an already known knowledge, method, design and/or practice to another field; research, conceive, design, adapt and implement an original subject. 5
5 Critical analysis, synthesis and evaluation of new and complex ideas. 4
6 Gain advanced level skills in the use of research methods in the field of study. 5
7 Contribute the progression in the field by producing an innovative idea, skill, design and/or practice or by adapting an already known idea, skill, design, and/or practice to a different field independently. 4
8 Broaden the borders of the knowledge in the field by producing or interpreting an original work or publishing at least one scientific paper in the field in national and/or international refereed journals. 4
9 Demonstrate leadership in contexts requiring innovative and interdisciplinary problem solving. 4
10 Develop new ideas and methods in the field by using high level mental processes such as creative and critical thinking, problem solving and decision making. 4
11 Investigate and improve social connections and their conducting norms and manage the actions to change them when necessary. 1
12 Defend original views when exchanging ideas in the field with professionals and communicate effectively by showing competence in the field. 5
13 Ability to communicate and discuss orally, in written and visually with peers by using a foreign language at least at a level of European Language Portfolio C1 General Level. 4
14 Contribute to the transition of the community to an information society and its sustainability process by introducing scientific, technological, social or cultural improvements. 4
15 Demonstrate functional interaction by using strategic decision making processes in solving problems encountered in the field. 3
16 Contribute to the solution finding process regarding social, scientific, cultural and ethical problems in the field and support the development of these values. 4
Common Outcomes For All Programs
1 Develop the ability to use critical, analytical, and reflective thinking and reasoning 5
2 Reflect on social and ethical responsibilities in his/her professional life. 4
3 Gain experience and confidence in the dissemination of project/research outputs 4
4 Work responsibly and creatively as an individual or as a member or leader of a team and in multidisciplinary environments. 4
5 Communicate effectively by oral, written, graphical and technological means and have competency in English. 5
6 Independently reach and acquire information, and develop appreciation of the need for continuously learning and updating. 5
Common Outcomes ForFaculty of Eng. & Natural Sci.
1 Design and model engineering systems and processes and solve engineering problems with an innovative approach. 4
2 Establish experimental setups, conduct experiments and/or simulations. 5
3 Analytically acquire and interpret data. 5
Common Outcomes ForSchool of Management
1 Develop, interpret and use statistical analyses in decision making. 5
Industrial Engineering (with thesis) Program Outcomes Core Electives
1 Establish a strong theoretical background in several of a broad range of subjects related to the discipline, such as manufacturing processes, service systems design and operation, production planning and control, modeling and optimization, stochastics, statistics. 5
2 Develop novel modeling and / or analytical solution strategies for problems in integrated production and service systems involving human capital, materials, information, equipment, and energy, also using an interdisciplinary approach whenever appropriate. 4
3 Implement solution strategies on a computer platform for decision-support purposes by employing effective computational and experimental tools. 4
4 Acquire skills to independently explore and tackle problems related to the discipline that were not encountered previously. Develop appropriate modeling, solution, implementation strategies, and assess the quality of the outcome. 5
1 Employ mathematical methods to solve physical problems and understand relevant numerical techniques.
2 Conduct basic experiments or simulations.
3 Analytically acquire and interpret data.
4 Establish thorough understanding of the fundamental principles of physics.
1 Apply software, modeling, instrumentation, and experimental techniques and their combinations in the design and integration of electrical, electronic, control and mechanical systems.
2 Interact with researchers from different disciplines to exchange ideas and identify areas of research collaboration to advance the frontiers of present knowledge and technology; determine relevant solution approaches and apply them by preparing a research strategy.
3 Take part in ambitious and highly challenging research to generate value for both the industry and society.
1 Apply knowledge of mathematics, science, and engineering in computer science and engineering related problems.
2 Display knowledge of contemporary issues in computer science and engineering and apply to a particular problem.
3 Demonstrate the use of results from interpreted data to improve the quality of research or a product in computer science and engineering.
1 Apply a broad knowledge of structure & microstructure of all classes of materials, and the ability to use this knowledge to determine the material properties.
2 Apply a broad understanding of the relationships between material properties, performance and processing.
3 Apply a broad understanding of thermodynamics, kinetics, transport phenomena, phase transformations and materials aspects of advanced technology.
4 Demonstrate hands-on experience using a wide range of materials characterization techniques.
5 Demonstrate the use of results from interpreted data to improve the quality of research, a product, or a product in materials science and engineering.
1 Assess and identify developments, strategies, opportunities and problems in energy security and energy technologies.
2 Define and solve technical, economic and administrative problems in energy businesses.
3 Establish knowledge and understanding of energy security, energy technologies, energy markets and strategic planning in energy enterprises.
4 Demonstrate an awareness of environmental concerns and their importance in developing engineering solutions and new technologies.
5 Acquire a series of social and technical proficiencies for project management and leadership skills.
1 Apply knowledge of key concepts in biology, with an emphasis on molecular genetics, biochemistry and molecular and cell biology.
2 Display an awareness of the contemporary biological issues in relation with other scientific areas.
3 Demonstrate hands-on experience in a wide range of biological experimental techniques.
1 Use advanced Math (including probability and/or statistics), advanced sciences, advanced computer and programming, and advanced Electronics engineering knowledge to design and analyze complex electronics circuits, instruments, software and electronic systems with hardware/software.
2 Analyze and design advanced communication networks and systems, advanced signal processing algorithms or software using advanced knowledge on diff. equations, linear algebra, complex variables and discrete math.
Assessment Methods and Criteria
  Percentage (%)
Final 35
Midterm 25
Individual Project 25
Other 15
Recommended or Required Reading
Readings

Some Reference Books:

Mathematical Statistics and Data Analysis (with CD Data Sets) 3rd Edition John A. Rice.

Probability and statistics in engineering and management science, William W. Hines and Douglas C. Montgomery.

Applied Linear Statistical Models, Fourth Edition, John Neter, Michael H. Kutner, Christopher J. Nachtsheim and W. Wasserman.

Applied Multivariate Statistical Analysis, 2002, R.A. Johnson, D.W. Wichern.

A Second Course in Statistics: Regression Analysis, Sixth Edition 2003, W. Mendenhall, T. Sincich.

The Elements of Statistical Learning: data mining, inference and prediction, 2001, T. Hastie, R. Tibshirani, and J. Friedman, Springer Verlag.