School of Engineering and Technology, (SET)

The objective of this course is to impart knowledge on various statistical methods with a special emphasis on design of experiments.

The students on the completion of this course would be able to:

      Determine basic descriptive statistics of ungrouped and grouped data sets that are needed for statistical inferential processes
      Estimate and test of hypotheses on population parameters for decision making purpose
      Apply analysis of variance technique through different experimental designs to make decision for various design problems in industries

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I.          Basic Concepts
1.      Descriptive and Inferential Statistics
2.      Statistical Inferences
3.      Data Representation, Measures of Central Tendency and Dispersion
4.      Sampling Methods and Sampling Distributions

II.         Introduction to Probability Theory
1.      Experiments, Events, Sample Space
2.      Laws of Addition and Multiplication
3.      Conditional Probability and Expectation
4.      Posterior Probability and Bayes’ Theorem

III.        Random Distributions
1.      Discrete vs. Continuous Random Variables
2.      Expectation and Variance
3.      Function of Random Variables
4.      Limit Theorems

IV.        Statistical Inferences
1.      Point Estimation : Methods of Point Estimation
2.      Properties of Point Estimators
3.      Interval Estimation : Methods and Properties of Confidence Interval
4.      Sample Size Determination

V.         Test of Hypothesis
1.      Statements, Simple and Composite Hypothesis
2.      Type I and Type II Errors
3.      Power of Test
4.      Observed Significance Level
5.      Choice of Sample Size

VI.        Analysis of Variance and Design of Experiments
1.      Introduction to Design of Experiments
2.      Analysis of Variance (ANOVA)
3.      Fixed/Random Effects Models
4.      Model Adequacy Checking
5.      Approaches for Determining Sample Size
6.      Nonparametric Methods in ANOVA

VII.       Randomized Blocks, Latin Squares and Related Designs
1.      Randomized Complete Block Design
2.      Latin Square Design
3.      Graeco- Latin Square Design
4.      Balanced Incomplete Block Design

VIII.      Factorial Designs
1.      Principles
2.      Two-Factor Factorial Design
3.      General Factorial Design
4.      Blocking in Factorial Design

IX.        The 2k Factorial Designs
1.      Orthogonal Contrast
2.      Single Replicate of the 2k Design
3.      Addition of Center Points to the 2k Design
4.      Confounding in the 2k Design
5.      Partial Confounding
6.      Fractional Factorial Design

X.         Statistical Regression and Response Surface Method
1.      Linear Regression Models and Least Square Method
2.      Residual Analysis
3.      Multiple Regression
4.      Goodness of Fit Tests
5.      Respond Surface Method in Optimal Design of Parameters

D.C. Montgomery: Design and Analysis of Experiments, 8th edition, John Wiley and Sons, 2013.

1.   G. Taguchi, S. Chowdhury, and Y. Wu: Taguchi Quality Engineering Handbook, 1st edition, John Wiley & Sons, 2005
2.    P.D. Berger: Experimental Design, 1st edition, Duxbury Thomson Learning, 2002.
3.    M.H. DeGroot, and M.J. Scherrvish: Probability and Statistics, 4th edition, Pearson, 2012.
4.    G. Taguchi, S. Chowdhury, and S. Taguchi: Robust Engineering, 1st edition, McGraw-Hill, 2000.
5.    W.G. Cochran: Sampling Techniques, 3rd edition, John Wiley & Sons, 1977.
6.    I. Newton: Minitab Cookbook, Packt Publishing Ltd., 2014
1.     International Journal of Quality and Reliability Management, Emerald.
2.     International Journal of Experimental Design and Process Optimisation, Inderscience.

Others: Lecture Notes
Lecture hours                   : 45 hours
Tutorials                            : 09 hours
Home assignments       : 45 hours
Self-study                          : 90 hours

The teaching is done via lectures by the instructor. The learning method includes tutorial and individual assignments.

           

Mid-semester examination 50%, final examination 50%.   The examinations are open-book.

An “A” would be awarded if a student shows a deep understanding of all statistical analysis procedures and design techniques discussed in this course through analysis of practical problems. 

A “B” would be awarded if a student shows an overall understanding of all statistical analysis procedures and basic design techniques. 

A “C” would be given if a student meets below average expectation in understanding and application of basic design techniques. 

A “D” would be given if a student does not meet expectations in understanding and application of basic techniques.
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