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E-raamat: Introduction to Statistics with Python: With Applications in the Life Sciences

  • Formaat: EPUB+DRM
  • Sari: Statistics and Computing
  • Ilmumisaeg: 15-Nov-2022
  • Kirjastus: Springer Nature Switzerland AG
  • Keel: eng
  • ISBN-13: 9783030973711
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Introduction to Statistics with Python: With Applications in the Life SciencesSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Statistics and Computing
  • Ilmumisaeg: 15-Nov-2022
  • Kirjastus: Springer Nature Switzerland AG
  • Keel: eng
  • ISBN-13: 9783030973711
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Now in its second edition, this textbook provides an introduction to Python and its use for statistical data analysis. It covers common statistical tests for continuous, discrete and categorical data, as well as linear regression analysis and topics from survival analysis and Bayesian statistics.

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. 


Part I Python and Statistics
1 Introduction
3(4)
1.1 Why Statistics?
3(2)
1.2 Conventions
5(1)
1.3 Accompanying Material
5(2)
2 Python
7(42)
2.1 Getting Started
8(7)
2.2 Elements of Scientific Python Programming
15(13)
2.3 Interactive Programming---IPython/Jupyter
28(11)
2.4 Statistics Packages for Python
39(4)
2.5 Programming Tips
43(2)
2.6 Exercises
45(4)
3 Data Input
49(10)
3.1 Text
49(5)
3.2 Excel
54(1)
3.3 Matlab
54(1)
3.4 Binary Data: NPZ Format
55(1)
3.5 Other Formats
56(1)
3.6 Exercises
56(3)
4 Data Display
59(28)
4.1 Introductory Example
59(3)
4.2 Plotting in Python
62(4)
4.3 Saving a Figure
66(1)
4.4 Preparing Figures for Presentation
67(3)
4.5 Display of Statistical Data Sets
70(12)
4.6 Exercises
82(5)
Part II Distributions and Hypothesis Tests
5 Basic Statistical Concepts
87(18)
5.1 Populations and Samples
87(2)
5.2 Data Types
89(1)
5.3 Probability Distributions
90(4)
5.4 Degrees of Freedom
94(1)
5.5 Study Design
94(11)
6 Distributions of One Variable
105(34)
6.1 Characterizing a Distribution
105(10)
6.2 Discrete Distributions
115(5)
6.3 Normal Distribution
120(5)
6.4 Continuous Distributions Derived from the Normal Distribution
125(7)
6.5 Other Continuous Distributions
132(3)
6.6 Confidence Intervals of Selected Statistical Parameters
135(1)
6.7 Exercises
136(3)
7 Hypothesis Tests
139(20)
7.1 Typical Analysis Procedure
139(5)
7.2 Hypothesis Tests and Power Analyses
144(8)
7.3 Sensitivity and Specificity
152(3)
7.4 Receiver-Operating-Characteristic (ROC) Curve
155(2)
7.5 Exercises
157(2)
8 Tests of Means of Numerical Data
159(22)
8.1 Distribution of a Sample Mean
159(5)
8.2 Comparison of Two Groups
164(4)
8.3 Comparison of Multiple Groups
168(8)
8.4 Summary: Selecting the Right Test for Comparing Groups
176(2)
8.5 Exercises
178(3)
9 Tests on Categorical Data
181(16)
9.1 Proportions and Confidence Intervals
182(1)
9.2 Tests Using Frequency Tables
183(11)
9.3 Exercises
194(3)
10 Analysis of Survival Times
197(8)
10.1 Survival Distributions
197(1)
10.2 Survival Probabilities
198(4)
10.3 Comparing Survival Curves in Two Groups
202(3)
Part III Statistical Modeling
11 Finding Patterns in Signals
205(24)
11.1 Cross Correlation
205(3)
11.2 Correlation Coefficient
208(3)
11.3 Coefficient of Determination
211(3)
11.4 Scatterplot Matrix
214(1)
11.5 Correlation Matrix
214(3)
11.6 Autocorrelation
217(1)
11.7 Time-Series Analysis
218(11)
12 Linear Regression Models
229(36)
12.1 Simple Fits
230(2)
12.2 Design Matrix and Formulas
232(5)
12.3 Linear Regression Analysis with Python
237(4)
12.4 Model Results of Linear Regression Models
241(16)
12.5 Assumptions and Interpretations of Linear Regression
257(5)
12.6 Bootstrapping
262(1)
12.7 Exercises
262(3)
13 Generalized Linear Models
265(10)
13.1 Comparing and Modeling Ranked Data
265(1)
13.2 Elements of GLMs
266(1)
13.3 GLM 1: Logistic Regression
267(3)
13.4 GLM 2: Ordinal Logistic Regression
270(4)
13.5 Exercises
274(1)
14 Bayesian Statistics
275(8)
14.1 Bayesian Versus Frequentist Interpretation
275(2)
14.2 The Bayesian Approach in the Age of Computers
277(1)
14.3 Example: Markov-Chain-Monte-Carlo Simulation
278(2)
14.4 Summing Up
280(3)
Appendix A Useful Programming Tools 283(10)
Appendix B Solutions 293(28)
Appendix C Equations for Confidence Intervals 321(2)
Appendix D Web Ressources 323(2)
Glossary 325(6)
Bibliography 331(2)
Index 333
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.

 
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