JNTUK B.Tech Design and Analysis of Experiments gives you detail information of Design and Analysis of Experiments R13 syllabus It will be help full to understand you complete curriculum of the year.
Learning Objectives
- The general philosophy of designing and carrying experiments and analyzing the data generated from experiments.
- Factorial and fractional factorial designs and their relevance to simultaneously increase experimentation efficiency and reduce cost.
- Mathematical methodologies for the efficient analysis of the data generated from experimentation to instill confidence in the data for utilization towards industrial process modeling and simulation efforts.
- Linear and non-linear regression analysis.
- Overview of various software packages for statistical design and analysis of experiments.
UNIT-I: Introduction to probability, Probability laws, Baye’s theorem, Probability distributions, Parameters and statistics.
UNIT-II: Normal and t-distributions, Central limit theorem, Random sampling and declaration of independence significance tests.
UNIT-III: Randomization and blocking with paired comparisons significance tests and confidence interval for means, variances, proportions and frequencies.
UNIT-IV: Analysis of variance, Experiments to compare k-treatment means
UNIT-V: Two-way factorial designs, blocking, Yate’s algorithm Fractional factorial designs at two levels, Concept of design resolution
UNIT-VI: Simple modeling with least squares (Regression analysis), Matrix versions of normal equations
Course Outcomes
A student with sound knowledge in this course shall be able to do the following tasks:
- Design an experiment with minimal experimental runs and maximum diversity in the data obtained.
- Analyze obtained data for its consistency to represent the natural phenomena associated in the experiment.
- Improve experimental approaches by rigorous data analysis
- Utilization of probability and statistical knowledge to define and refine experimental data consistency.
- Develop process models using linear and non-linear regression for experimental data.
- Analyze the competence of regressed models to represent experimental data.
Text Book
- Statistics for Experimenters, G.E.P. Box, William G. Hunter and J.S. Hunter, John Wiley & Sons. 1978.
Reference Books
- Design and Analysis of Experiments, D.C. Montgomery, 2nd Edition John Wiley and Sons, 1984.
- Design of Experiments in Chemical Engineering: A Practical Guide, Zivorad R. Lazic, Wiley – VCH, 2005.
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