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For this new edition, the introductory chapters on Python, data input and visualization have been reworked and updated. The chapter on experimental design has been expanded, and programs for the determination of confidence intervals commonly used in quality control have been introduced. The book also features a new chapter on finding patterns in data, including time series. A new appendix describes useful programming tools, such as testing tools, code repositories, and GUIs.The provided working code for Python solutions, together with easy-to-follow examples, will reinforce the reader¿s immediate understanding of the topic. Accompanying data sets and Python programs are also available online. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis.
With examples drawn mainly from the life and medical sciences, this book is intended primarily for masters and PhD students. As it provides the required statistics background, the book can also be used by anyone who wants to perform a statistical data analysis.
For this new edition, the introductory chapters on Python, data input and visualization have been reworked and updated. The chapter on experimental design has been expanded, and programs for the determination of confidence intervals commonly used in quality control have been introduced. The book also features a new chapter on finding patterns in data, including time series. A new appendix describes useful programming tools, such as testing tools, code repositories, and GUIs.The provided working code for Python solutions, together with easy-to-follow examples, will reinforce the reader¿s immediate understanding of the topic. Accompanying data sets and Python programs are also available online. With recent advances in the Python ecosystem, Python has become a popular language for scientific computing, offering a powerful environment for statistical data analysis.
With examples drawn mainly from the life and medical sciences, this book is intended primarily for masters and PhD students. As it provides the required statistics background, the book can also be used by anyone who wants to perform a statistical data analysis.
Thomas Haslwanter is a Professor at the School of Medical Engineering and Applied Social Sciences at the University of Applied Sciences Upper Austria in Linz, and lecturer at the ETH Zurich in Switzerland. He also worked as a researcher at the University of Sydney, Australia and the University of Tübingen, Germany. He has extensive experience in medical research, with a focus on the diagnosis and treatment of vertigo and dizziness and on rehabilitation. After 15 years of extensive use of Matlab, he discovered Python, which he now uses for statistical data analysis, sound and image processing, and for biological simulation applications. He has been teaching in an academic environment for more than 15 years.
Provides an introduction to Python for statistical data analysis
Covers common statistical tests and various applications, including their implementation and working solutions in Python
Features a new chapter on finding patterns in data, also in time series, and useful programming tools
I Python and Statistics.- 1 Introduction.- 2 Python.- 3 Data Input.- 4 Data Display.- II Distributions and Hypothesis Tests.- 5 Basic Statistical Concepts.- 6 Distributions of One Variable.- 7 Hypothesis Tests.- 8 Tests of Means of Numerical Data.- 9 Tests on Categorical Data.- 10 Analysis of Survival Times.- III Statistical Modelling.- 11 Finding Patterns in Signals.- 12 Linear Regression Models.- 13 Generalized Linear Models.- 14 Bayesian Statistics.- Appendices.- A Useful Programming Tools.- B Solutions.- C Equations for Confidence Intervals.- D Web Ressources.- Glossary.- Bibliography.- Index.
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 356 |
| Reihe: | Statistics and Computing |
| Inhalt: |
xvi
336 S. 25 s/w Illustr. 131 farbige Illustr. 336 p. 156 illus. 131 illus. in color. |
| ISBN-13: | 9783030973704 |
| ISBN-10: | 3030973700 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Haslwanter, Thomas |
| Auflage: | 2nd ed. 2022 |
| Hersteller: |
Springer International Publishing
Statistics and Computing |
| Maße: | 241 x 160 x 25 mm |
| Von/Mit: | Thomas Haslwanter |
| Erscheinungsdatum: | 16.11.2022 |
| Gewicht: | 0,699 kg |
Thomas Haslwanter is a Professor at the School of Medical Engineering and Applied Social Sciences at the University of Applied Sciences Upper Austria in Linz, and lecturer at the ETH Zurich in Switzerland. He also worked as a researcher at the University of Sydney, Australia and the University of Tübingen, Germany. He has extensive experience in medical research, with a focus on the diagnosis and treatment of vertigo and dizziness and on rehabilitation. After 15 years of extensive use of Matlab, he discovered Python, which he now uses for statistical data analysis, sound and image processing, and for biological simulation applications. He has been teaching in an academic environment for more than 15 years.
Provides an introduction to Python for statistical data analysis
Covers common statistical tests and various applications, including their implementation and working solutions in Python
Features a new chapter on finding patterns in data, also in time series, and useful programming tools
I Python and Statistics.- 1 Introduction.- 2 Python.- 3 Data Input.- 4 Data Display.- II Distributions and Hypothesis Tests.- 5 Basic Statistical Concepts.- 6 Distributions of One Variable.- 7 Hypothesis Tests.- 8 Tests of Means of Numerical Data.- 9 Tests on Categorical Data.- 10 Analysis of Survival Times.- III Statistical Modelling.- 11 Finding Patterns in Signals.- 12 Linear Regression Models.- 13 Generalized Linear Models.- 14 Bayesian Statistics.- Appendices.- A Useful Programming Tools.- B Solutions.- C Equations for Confidence Intervals.- D Web Ressources.- Glossary.- Bibliography.- Index.
| Erscheinungsjahr: | 2022 |
|---|---|
| Fachbereich: | Wahrscheinlichkeitstheorie |
| Genre: | Mathematik |
| Rubrik: | Naturwissenschaften & Technik |
| Medium: | Buch |
| Seiten: | 356 |
| Reihe: | Statistics and Computing |
| Inhalt: |
xvi
336 S. 25 s/w Illustr. 131 farbige Illustr. 336 p. 156 illus. 131 illus. in color. |
| ISBN-13: | 9783030973704 |
| ISBN-10: | 3030973700 |
| Sprache: | Englisch |
| Ausstattung / Beilage: | HC runder Rücken kaschiert |
| Einband: | Gebunden |
| Autor: | Haslwanter, Thomas |
| Auflage: | 2nd ed. 2022 |
| Hersteller: |
Springer International Publishing
Statistics and Computing |
| Maße: | 241 x 160 x 25 mm |
| Von/Mit: | Thomas Haslwanter |
| Erscheinungsdatum: | 16.11.2022 |
| Gewicht: | 0,699 kg |
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