Methods of Statistical Inference (ENS 505)

2021 Spring
Faculty of Engineering and Natural Sciences
Engineering Sciences(ENS)
3
10.00
Sinan Yıldırım sinanyildirim@sabanciuniv.edu,
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English
Doctoral, Master
--
Formal lecture
Project based learning,Task based learning,Other
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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.

ASSESSMENT METHODS and CRITERIA

  Percentage (%)
Final 30
Assignment 40
Individual Project 30

RECOMENDED or REQUIRED READINGS

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.