Syllabus

Basic statistical concepts,

Descriptive statistics, Mean, Variance,

Data and its nature, data representation, diagrams and graphs using MS Excel

Measure of central tendency, Dispersion, Swekness and Kurtosis.

Probability, Probability distribution.

Introduction to the theory of estimation and confidence-intervals

Analysis of variance.

Correlation and Regression, Simple and multiple linear regression models, Partial correlation coefficient, a test of significance of correlation coefficient and regression coefficient, multiple and partial correlations, the significance of testing in multiple correlations,

Strategy of experiments, 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,

Coefficient of determination, Testing of heterogeneity. Multivariate relationships, multiple linear regression, variable selection in multiple regression. Multiple regression analysis, variable selection.

To learn about various basic and advanced concepts of statistics.

To learn methods of experimental design, basic to advanced

To be able to articulate, case based analyses and apply the appropriate method of analyses.

To be able to understand the data distribution and pattern.

To be able to conduct descriptive analyses.

To be able to conduct ANOVA analyses based on the provided data set.

To decide factors and responses for a controlled experiment and prepare an experimental design.

To carry out a regression analysis for any data.

W. G. Cocharan, Statistical Methods, , Oxford & IBH Co., Bombay/New Delhi

S P Gupta, Statistical Methods, S Chand & Sons

Raymond H. Myers, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Wiley

A. I. Khuri & J. A. Cornell, Response surfaces design and analysis, CRC Press