Introduction to Model Development for Prediction, Simulation and Optimization

Book Cover

About the Book

This book which is comparable to an e-course is designed to provide learners with a comprehensive understanding of statistical model development. Through detailed tutorials and comprehensive notes, participants will gain the necessary knowledge and skills to apply statistical models for prediction, simulation, and optimization purposes. Whether you are a beginner or an experienced practitioner, this course will equip you with the tools and techniques needed to effectively develop statistical models in various domains. In addition to this, this Paper to PC book has 50 project ideas 100 numerical questions and 100 case studies included.

Table of Content

1)Introduction

1.1. Model Development for Prediction, Simulation and Optimization

1.2. Common Challenges in Model Development 

2)Outlier Detection

2.1. Chauvenet Method

2.2. Dixon Thompson Method

2.3. Rosner’s Method

3)Trend Detection

3.1. What is Trend?

3.2. Runs Test

4)Auto and Cross-Correlation

4.1. Auto and Cross-Correlation

4.2. Auto and Cross Regression Model

5. Risk analysis

5.1. What is Risk and Vulnerability?

5.2. Weibull’s Method

5.3. Numerical Analysis

6)Uncertainty Analysis

6.1. Definition

6.2. Procedure

6.3. Standard Uncertainty and Relative Standard Uncertainty

6.4. Total Percent Uncertainty 

6.5. Uncertainty and standard deviation are the same?

6.6. What is uncertainty in decision-making?

6.7. List of statistical functions used for uncertainty analysis

7)Development of the Regression Models

7.1. Linear Regression Models

7.2. Non-Linear Regression Models

8)Types of Model Development

8.1. How to use the model for Prediction? 

8.2. How to use the model for Simulation?

8.3 How to use the model for Optimization?

9) Performance Metrics

9.1. Regression Error Identification Metrics

9.2. Classification Error Identification Metrics

9.3. Correlation Identification Metrics

9.4. Efficiency Identification Metrics

9.5. Reliability Analysis

10) Project Ideas 

11) Numerical Problems

12) Case Studies