54,80 €
Versandkostenfrei per Post / DHL
Lieferzeit 1-2 Wochen
Structured to deliver a clear overview of statistics and data analysis for scientific research, the book begins with fundamental concepts, including random variables, outcome spaces, and the distinction between descriptive and inferential statistics. It then explores data types, measures of central tendency, dispersion, and position. The discussion continues with an examination of outliers and various methods for identifying them. As the chapters progress, more complex topics such as distributions, hypothesis testing, and regression analysis are introduced in a step-by-step manner. This structure makes the book suitable for readers ranging from beginners to those seeking a quick refresher.
The author has selected key concepts that anyone interested in using statistics should be familiar with. Some topics, such as hypothesis testing, are covered briefly; a more comprehensive treatment would require a stronger background in statistics and mathematics (such as calculus). With pedagogical elements that include text boxes with Definitions, Examples, and Warnings, this book introduces the necessary concepts of statistics for scientists described in a short and concise way, enriched with tips and rigorous explanations. This book is an invaluable resource for scientists seeking to improve their data analysis skills and contribute to the growing body of scientific knowledge through rigorous and reliable research.
Structured to deliver a clear overview of statistics and data analysis for scientific research, the book begins with fundamental concepts, including random variables, outcome spaces, and the distinction between descriptive and inferential statistics. It then explores data types, measures of central tendency, dispersion, and position. The discussion continues with an examination of outliers and various methods for identifying them. As the chapters progress, more complex topics such as distributions, hypothesis testing, and regression analysis are introduced in a step-by-step manner. This structure makes the book suitable for readers ranging from beginners to those seeking a quick refresher.
The author has selected key concepts that anyone interested in using statistics should be familiar with. Some topics, such as hypothesis testing, are covered briefly; a more comprehensive treatment would require a stronger background in statistics and mathematics (such as calculus). With pedagogical elements that include text boxes with Definitions, Examples, and Warnings, this book introduces the necessary concepts of statistics for scientists described in a short and concise way, enriched with tips and rigorous explanations. This book is an invaluable resource for scientists seeking to improve their data analysis skills and contribute to the growing body of scientific knowledge through rigorous and reliable research.
Umberto Michelucci is an Award-winning artificial intelligence researcher, lecturer, advisor, and mentor with 20 years of experience in solving complex problems with innovative and advanced technologies. As lecturer I help universities and research groups to learn and use machine learning techniques in their research projects and publications. He is responsible for artificial intelligence in large European funded projects with large international consortia. He brings his passion and experience as long-distance runner into research and helping companies into generate value from artificial intelligence by knowing how to plan and use the right techniques to get to the finish line of a "long distance" project. He believes that artificial intelligence will make our society better, and he is willing to do everything in his power to make this possible. He has a PhD in machine learning and physics, and is the founder of TOELT, a company focused on research in AI and of the AI Center of Excellence at Helsana Versicherung AG in Switzerland. He is also a Google Developer Expert in Machine learning based in Switzerland.
He published three books with Apress on Deep Learning and TensorFlow, and a textbook on mathematical methods for Machine Learning with Springer.
Introduction to Statistics.- Types of Data.- Data Collection Methods (Sampling Theory).- Measures of Central Tendency.- Measures of Dispersion.- Measures of Positions.- Outliers.- Introduction to Distributions.- Skewness, Kurtosis and Modality.- Data Visualisation.- Confidence Intervals.- Hypothesis Testing.- Correlation and Linear Regression.- Statistical Project - Steps and Process.- Appendix A - Partioning of the Ordinary Least Square Variance.- Appendix B - Big-O and Little-o Notation.
| Erscheinungsjahr: | 2025 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik, Medizin, Naturwissenschaften, Technik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Inhalt: |
XXIV
167 S. 11 s/w Illustr. 8 farbige Illustr. 167 p. 19 illus. 8 illus. in color. |
| ISBN-13: | 9783031781469 |
| ISBN-10: | 3031781465 |
| Sprache: | Englisch |
| Einband: | Gebunden |
| Autor: | Michelucci, Umberto |
| Hersteller: |
Springer
Springer Nature Switzerland Springer International Publishing AG |
| Verantwortliche Person für die EU: | Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com |
| Maße: | 241 x 160 x 17 mm |
| Von/Mit: | Umberto Michelucci |
| Erscheinungsdatum: | 19.07.2025 |
| Gewicht: | 0,457 kg |