Course Details
Subject {L-T-P / C} : FP4573 : Experimental Design and Statistical Methods Laboratory { 0-0-0 / 2}
Subject Nature : Practical
Syllabus
| Module 1 : |
Basic statistical concepts, strategy of experiments, Descriptive statistics mean, variance, probability, probability distribution. Data and its nature, data representation, diagrams, and graphs using MS Excel. |
| Module 2 : |
Introduction to the theory of estimation and confidence intervals, correlation and regression, simple and multiple linear regression models, partial correlation coefficient, a test of significance of correlation coefficient and regression coefficient, the coefficient of determination, testing of heterogeneity. |
| Module 3 : |
Analysis of variance (ANOVA). Multivariate relationships, multiple linear regression, multiple and partial correlations, the significance of testing in multiple correlations, variable selection in multiple regression. Multiple regression analysis, variable selection. |
| Module 4 : |
basic principles, guidelines for designing experiments and the importance of designed experiments in research. Full factorial design, 2K design, completely randomized design, randomized block design, central composite design, factorial design, Box Behnken design, |
Course Objective
| 1 . |
To identify data and its nature, data representation, diagrams, and graphs using MS Excel.
|
| 2 . |
To do descriptive data analyses and data representation using data visualization software Tableau. |
| 3 . |
To do regression and correlation analyses, data wrangling, and screening for big data sets using basic data analysis software. |
| 4 . |
To identify factors and variables and prepare experimental design using any software. |
Course Outcome
| 1 . |
To identify data and its nature, data representation, diagrams, and graphs using MS Excel. |
| 2 . |
To do descriptive data analyses and data representation using data visualization software Tableau. |
| 3 . |
To do regression and correlation analyses, data wrangling, and screening for big data sets using basic data analysis software. |
| 4 . |
To identify factors and variables and prepare experimental design using any software. |
Essential Reading
| 1 . |
G. W. Snedecor and W. G. Cochran, Statistical Methods, , Oxford & IBH Co |
| 2 . |
R H Myers, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Wiley |
Supplementary Reading
| 1 . |
NIST/SEMATECH, e-Handbook of Statistical Methods, U S Department of Commerce. |
Journal and Conferences
| 1 . |