 Название: Engineering Data Analysis with MATLAB
Автор: Tanvir Mustafy, Tauhid Rahman, Nafisa Siddiqui
Издательство: CRC Press
Год: 2024
Страниц: 903
Язык: английский
Формат: pdf (true)
Размер: 93.7 MB
This book provides a concise overview of a variety of techniques for analyzing statistical, scientific, and financial data, using MATLAB to integrate several approaches to data analysis and statistics.
The chapters offer a broad review of computational data analysis, illustrated with many examples and applications. Topics range from the basics of data and statistical analysis to more advanced subjects such as probability distributions, descriptive and inferential statistics, parametric and non-parametric tests, correlation, and regression analysis. Each chapter combines theoretical concepts with practical MATLAB applications and includes practice exercises, ensuring a comprehensive understanding of the material.
So overall we could say that data analysis is defined as a process of cleaning and transforming data so that it becomes easier to interpret and analyze to detect trends, discover useful information, and draw conclusions from that information. To achieve this, we use a number of data analysis tools like Microsoft Excel, Tableau, SAS, Python, R, MATLAB, etc. This book introduces basic procedures and methods of data analysis in MATLAB. MATLAB provides an integrated environment for a wide range of scientific and engineering applications like signal and image processing, control designing, machine learning, and most importantly data analysis. It is widely used by scientists and engineers in a range of industries for its excellent capabilities in performing analyses and optimizations and in deploying mission-critical calculations. Moreover, it has data exploration features such as graphs, descriptive statistics, and visualization which allow users to get valuable insights from the data warehouse.
With coverage of both basic and more complex ideas in applied statistics, the book has broad appeal for undergraduate students up to practicing engineers.
Contents:
1. Getting Started.
2. Data Types and Visualization.
3. Random Variable and Probability Distribution.
4. Discrete Probability Distribution.
5. Continuous Probability Distribution.
6. Descriptive Statistics.
7. Inferential Statistics.
8. Parametric Tests.
9. Non-Parametric Testing.
10. Correlation.
11. Regression.
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