Syllabus

List of Experiments:
1. Comparison of means for two food diet groups using t-test.
2. Comparison of means for more than two food diets using ANOVA.
3. Posthoc statistical analysis for identification of significant group differences.
4. Development of General Linear Model (GLM) in Linear Regression.
5. Understanding Adjusted Sum of Squares and Sequential Sum of Squares in Linear Regression.
6. Design of Experiments using Central Composite Rotatable Design.
7. Analysis of Experiments using Central Composite Rotatable Design.
8. Model Development and Prediction using Artificial Neural Network (ANN).
9. Optimization using ANN and Response Surface Methodology (RSM).
10. Image Classification using Convolutional Neural Network (CNN).

To develop the ability to perform statistical analyses such as t-tests, ANOVA, and General Linear Models using tools like Minitab.

To train students in experimental design and regression modeling using Design Expert and Minitab.

To introduce machine learning-based modeling and optimization using Google Colab.

To equip students with the skills to perform image classification using Convolutional Neural Networks (CNN) implemented in Python environments like Colab.

Apply statistical techniques such as t-tests, one-way ANOVA, and post hoc analyses using tools like Minitab to evaluate differences between food diet groups.

Develop, interpret, and evaluate General Linear Models (GLM) using linear regression techniques in Minitab.

Design and analyze empirical models using Central Composite Rotatable Design (CCRD) in Design Expert.

Build and optimize Artificial Neural Network (ANN) models for nonlinear food process data modeling using Python-based platforms (like Colab).

Implement Convolutional Neural Networks (CNNs)-based image classification models in Python to identify patterns in food quality assessment or visual inspection tasks.

Rudolf J. Freund and William J. Wilson, Statistical Methods, Academic Press , 2nd Edition

Douglas C. Montgomery, Design and Analysis of Experiments, John Wiley & Sons , 5th Edition

R.H. Myers, D.C. Montgomery, C.M. Anderson-Cook, Response Surface Methodology: Process and Product Optimization Using Designed Experiments, Wiley

S P Gupta, Statistical Methods, S Chand & Sons