The Introduction to Model Development for Prediction, Simulation, and Optimization is one of a kind book where you will learn about different techniques of model development for prediction, simulation, and optimization. Questions like error, uncertainty, risk, and reliability of the models were discussed in great detail and in lucid language. This book starts with outlier detection and finishes with error metrics. Complete steps of development, application, and testing of statistical models are discussed. In addition, this book consists of 100 numerical,100 case studies followed by 50 project ideas.

Here is the content of the book :

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

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