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Introduction to Statistics with Python: With Applications in the Life Sciences 1st ed. 2016 [Kõva köide]

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  • Formaat: Hardback, 278 pages, kõrgus x laius: 235x155 mm, kaal: 5679 g, 85 Illustrations, color; 28 Illustrations, black and white; XVII, 278 p. 113 illus., 85 illus. in color., 1 Hardback
  • Sari: Statistics and Computing
  • Ilmumisaeg: 02-Aug-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319283154
  • ISBN-13: 9783319283159
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Introduction to Statistics with Python: With Applications in the Life Sciences 1st ed. 2016Suurem pilt
  • Formaat: Hardback, 278 pages, kõrgus x laius: 235x155 mm, kaal: 5679 g, 85 Illustrations, color; 28 Illustrations, black and white; XVII, 278 p. 113 illus., 85 illus. in color., 1 Hardback
  • Sari: Statistics and Computing
  • Ilmumisaeg: 02-Aug-2016
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319283154
  • ISBN-13: 9783319283159
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319283159.html
Märksõnad:
This textbook provides anintroduction to the free software Python and its use for statistical dataanalysis. It covers common statistical tests for continuous, discrete andcategorical data, as well as linear regression analysis and topics from survivalanalysis and Bayesian statistics. Working code and data for Python solutionsfor each test, together with easy-to-follow Python examples, can be reproducedby the reader and reinforce their immediate understanding of the topic. Withrecent advances in the Python ecosystem, Python has become a popular languagefor scientific computing, offering a powerful environment for statistical dataanalysis and an interesting alternative to R. The book is intended for masterand PhD students, mainly from the life and medical sciences, with a basicknowledge of statistics. As it also provides some statistics background, thebook can be used by anyone who wants to perform a statistical dataanalysis. 

Part I: Python and Statistics.- Why Statistics .- Python.- Data Input.- Display of Statistical Data.- Part II: Distributions and Hypothesis Tests.- Background.- Distributions of One Variable.- Hypothesis Tests.- Tests of Means of Numerical Data.- Tests on Categorical Data.- Analysis of Survival Times.- Part III: Statistical Modelling.- Linear Regression Models.- Multivariate Data Analysis.- Tests on Discrete Data.- Bayesian Statistics.- Solutions.- Glossary.- Index.

Arvustused

This book is a timely addition designed to bridge the gap between statisticians/computer scientists and experimentalists (biologists, physicists, medical doctors) by focussing on solutions to practical problems . the book also provides hands-on examples and exercises for a better understanding (for which the solutions are included at the end of the book). This approach makes the book appealing to a wide audience ranging from undergraduates in various subjects to established researchers looking for a focused set of answers. (Irina Ioana Mohorianu, zbMATH 1357.92001, 2017)

Part I Python and Statistics
1 Why Statistics?
3(2)
2 Python
5(38)
2.1 Getting Started
5(12)
2.1.1 Conventions
5(1)
2.1.2 Distributions and Packages
6(2)
2.1.3 Installation of Python
8(2)
2.1.4 Installation of R and rpy2
10(1)
2.1.5 Personalizing IPython/Jupyter
11(3)
2.1.6 Python Resources
14(1)
2.1.7 First Python Programs
15(2)
2.2 Python Data Structures
17(4)
2.2.1 Python Datatypes
17(2)
2.2.2 Indexing and Slicing
19(1)
2.2.3 Vectors and Arrays
19(2)
2.3 IPython/Jupyrer: An Interactive Programming Environment
21(6)
2.3.1 First Session with the Qt Console
22(2)
2.3.2 Notebook and rpy2
24(2)
2.3.3 IPython Tips
26(1)
2.4 Developing Python Programs
27(8)
2.4.1 Converting Interactive Commands into a Python Program
27(3)
2.4.2 Functions, Modules, and Packages
30(4)
2.4.3 Python Tips
34(1)
2.4.4 Code Versioning
34(1)
2.5 Pandas: Data Structures for Statistics
35(4)
2.5.1 Data Handling
35(2)
2.5.2 Grouping
37(2)
2.6 Statsmodels: Tools for Statistical Modeling
39(1)
2.7 Seaborn: Data Visualization
40(1)
2.8 General Routines
41(1)
2.9 Exercises
42(1)
3 Data Input
43(8)
3.1 Input from Text Files
43(4)
3.1.1 Visual Inspection
43(1)
3.1.2 Reading ASCII-Data into Python
44(3)
3.2 Input from MS Excel
47(2)
3.3 Input from Other Formats
49(2)
3.3.1 Matlab
49(2)
4 Display of Statistical Data
51(24)
4.1 Datatypes
51(1)
4.1.1 Categorical
51(1)
4.1.2 Numerical
52(1)
4.2 Plotting in Python
52(7)
4.2.1 Functional and Object-Oriented Approaches to Plotting
54(1)
4.2.2 Interactive Plots
55(4)
4.3 Displaying Statistical Datasets
59(12)
4.3.1 Univariate Data
59(10)
4.3.2 Bivariate and Multivariate Plots
69(2)
4.4 Exercises
71(4)
Part II Distributions and Hypothesis Tests
5 Background
75(14)
5.1 Populations and Samples
75(1)
5.2 Probability Distributions
76(3)
5.2.1 Discrete Distributions
77(1)
5.2.2 Continuous Distributions
77(1)
5.2.3 Expected Value and Variance
78(1)
5.3 Degrees of Freedom
79(1)
5.4 Study Design
79(10)
5.4.1 Terminology
79(1)
5.4.2 Overview
80(1)
5.4.3 Types of Studies
81(1)
5.4.4 Design of Experiments
82(4)
5.4.5 Personal Advice
86(1)
5.4.6 Clinical Investigation Plan
87(2)
6 Distributions of One Variable
89(32)
6.1 Characterizing a Distribution
89(10)
6.1.1 Distribution Center
89(2)
6.1.2 Quantifying Variability
91(5)
6.1.3 Parameters Describing the Form of a Distribution
96(2)
6.1.4 Important Presentations of Probability Densities
98(1)
6.2 Discrete Distributions
99(5)
6.2.1 Bernoulli Distribution
100(1)
6.2.2 Binomial Distribution
100(3)
6.2.3 Poisson Distribution
103(1)
6.3 Normal Distribution
104(5)
6.3.1 Examples of Normal Distributions
107(1)
6.3.2 Central Limit Theorem
107(1)
6.3.3 Distributions and Hypothesis Tests
108(1)
6.4 Continuous Distributions Derived from the Normal Distribution
109(6)
6.4.1 t-Distribution
110(1)
6.4.2 Chi-Square Distribution
111(2)
6.4.3 F-Distribution
113(2)
6.5 Other Continuous Distributions
115(4)
6.5.1 Lognormal Distribution
116(1)
6.5.2 Weibull Distribution
116(2)
6.5.3 Exponential Distribution
118(1)
6.5.4 Uniform Distribution
118(1)
6.6 Exercises
119(2)
7 Hypothesis Tests
121(18)
7.1 Typical Analysis Procedure
121(5)
7.1.1 Data Screening and Outliers
122(1)
7.1.2 Normality Check
122(4)
7.1.3 Transformation
126(1)
7.2 Hypothesis Concept, Errors, p-Value, and Sample Size
126(8)
7.2.1 An Example
126(1)
7.2.2 Generalization and Applications
127(1)
7.2.3 The Interpretation of the p-Value
128(1)
7.2.4 Types of Error
129(2)
7.2.5 Sample Size
131(3)
7.3 Sensitivity and Specificity
134(2)
7.3.1 Related Calculations
136(1)
7.4 Receiver-Operating-Characteristic (ROC) Curve
136(3)
8 Tests of Means of Numerical Data
139(20)
8.1 Distribution of a Sample Mean
139(3)
8.1.1 One Sample t-Test for a Mean Value
139(2)
8.1.2 Wilcoxon Signed Rank Sum Test
141(1)
8.2 Comparison of Two Groups
142(4)
8.2.1 Paired t-Test
142(1)
8.2.2 t-Test between Independent Groups
143(1)
8.2.3 Nonparametric Comparison of Two Groups: Mann-Whitney Test
144(1)
8.2.4 Statistical Hypothesis Tests vs Statistical Modeling
144(2)
8.3 Comparison of Multiple Groups
146(9)
8.3.1 Analysis of Variance (ANOVA)
146(4)
8.3.2 Multiple Comparisons
150(2)
8.3.3 Kruskal-Wallis Test
152(1)
8.3.4 Two-Way ANOVA
152(2)
8.3.5 Three-Way ANOVA
154(1)
8.4 Summary: Selecting the Right Test for Comparing Groups
155(2)
8.4.1 Typical Tests
155(1)
8.4.2 Hypothetical Examples
156(1)
8.5 Exercises
157(2)
9 Tests on Categorical Data
159(16)
9.1 One Proportion
160(2)
9.1.1 Confidence Intervals
160(1)
9.1.2 Explanation
160(1)
9.1.3 Example
161(1)
9.2 Frequency Tables
162(9)
9.2.1 One-Way Chi-Square Test
162(1)
9.2.2 Chi-Square Contingency Test
163(2)
9.2.3 Fisher's Exact Test
165(4)
9.2.4 McNemar's Test
169(1)
9.2.5 Cochran's Q Test
170(1)
9.3 Exercises
171(4)
10 Analysis of Survival Times
175(8)
10.1 Survival Distributions
175(1)
10.2 Survival Probabilities
176(4)
10.2.1 Censorship
176(1)
10.2.2 Kaplan--Meier Survival Curve
177(3)
10.3 Comparing Survival Curves in Two Groups
180(3)
Part III Statistical Modeling
11 Linear Regression Models
183(38)
11.1 Linear Correlation
184(1)
11.1.1 Correlation Coefficient
184(1)
11.1.2 Rank Correlation
184(1)
11.2 General Linear Regression Model
185(5)
11.2.1 Example 1: Simple Linear Regression
187(1)
11.2.2 Example 2: Quadratic Fit
187(1)
11.2.3 Coefficient of Determination
188(2)
11.3 Patsy: The Formula Language
190(3)
11.3.1 Design Matrix
190(3)
11.4 Linear Regression Analysis with Python
193(5)
11.4.1 Example 1: Line Fit with Confidence Intervals
193(1)
11.4.2 Example 2: Noisy Quadratic Polynomial
194(4)
11.5 Model Results of Linear Regression Models
198(16)
11.5.1 Example: Tobacco and Alcohol in the UK
198(2)
11.5.2 Definitions for Regression with Intercept
200(1)
11.5.3 The R2 Value
201(1)
11.5.4 R2: The Adjusted R2 Value
201(4)
11.5.5 Model Coefficients and Their Interpretation
205(4)
11.5.6 Analysis of Residuals
209(3)
11.5.7 Outliers
212(1)
11.5.8 Regression Using Sklearn
212(2)
11.5.9 Conclusion
214(1)
11.6 Assumptions of Linear Regression Models
214(4)
11.7 Interpreting the Results of Linear Regression Models
218(1)
11.8 Bootstrapping
219(1)
11.9 Exercises
220(1)
12 Multivariate Data Analysis
221(6)
12.1 Visualizing Multivariate Correlations
221(2)
12.1.1 Scatterplot Matrix
221(1)
12.1.2 Correlation Matrix
222(1)
12.2 Multilinear Regression
223(4)
13 Tests on Discrete Data
227(10)
13.1 Comparing Groups of Ranked Data
227(1)
13.2 Logistic Regression
228(3)
13.2.1 Example: The Challenger Disaster
228(3)
13.3 Generalized Linear Models
231(1)
13.3.1 Exponential Family of Distributions
231(1)
13.3.2 Linear Predictor and Link Function
232(1)
13.4 Ordinal Logistic Regression
232(5)
13.4.1 Problem Definition
232(2)
13.4.2 Optimization
234(1)
13.4.3 Code
235(1)
13.4.4 Performance
235(2)
14 Bayesian Statistics
237(8)
14.1 Bayesian vs. Frequentist Interpretation
237(2)
14.1.1 Bayesian Example
238(1)
14.2 The Bayesian Approach in the Age of Computers
239(1)
14.3 Example: Analysis of the Challenger Disaster with a Markov-Chain--Monte-Carlo Simulation
240(3)
14.4 Summing Up
243(2)
Solutions 245(22)
Glossary 267(6)
References 273(2)
Index 275
Thomas Haslwanter is a Professor at the Department of Medical Engineering of 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 Tuebingen, 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 10 years.