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목차 (Table of Contents)

CONTENTS
Part Ⅰ Python and Statistics
1 Why Statistics? = 3
2 Python = 5
2.1 Getting Started = 5

CONTENTS
Part Ⅰ Python and Statistics
1 Why Statistics? = 3
2 Python = 5
2.1 Getting Started = 5
2.2 Python Data Structures = 17
2.3 IPython／Jupyrer : An Interactive Programming Environment = 21
2.4 Developing Python Programs = 27
2.5 Pandas : Data Structures for Statistics = 35
2.6 Statsmodels : Tools for Statistical Modeling = 39
2.7 Seaborn : Data Visualization = 40
2.8 General Routines = 41
2.9 Exercises = 42
3 Data Input = 43
3.1 Input from Text Files = 43
3.2 Input from MS Excel = 47
3.3 Input from Other Formats = 49
4 Display of Statistical Data = 51
4.1 Datatypes = 51
4.2 Plotting in Python = 52
4.3 Displaying Statistical Datasets = 59
4.4 Exercises = 71
Part Ⅱ Distributions and Hypothesis Tests
5 Background = 75
5.1 Populations and Samples = 75
5.2 Probability Distributions = 76
5.3 Degrees of Freedom = 79
5.4 Study Design = 79
6 Distributions of One Variable = 89
6.1 Characterizing a Distribution = 89
6.2 Discrete Distributions = 99
6.3 Normal Distribution = 104
6.4 Continuous Distributions Derived from the Normal Distribution = 109
6.5 Other Continuous Distributions = 115
6.6 Exercises = 119
7 Hypothesis Tests = 121
7.1 Typical Analysis Procedure = 121
7.2 Hypothesis Concept, Errors, p-Value, and Sample Size = 126
7.3 Sensitivity and Specificity = 134
7.4 Receiver-Operating-Characteristic(ROC) Curve = 136
8 Tests of Means of Numerical Data = 139
8.1 Distribution of a Sample Mean = 139
8.2 Comparison of Two Groups = 142
8.3 Comparison of Multiple Groups = 146
8.4 Summary : Selecting the Right Test for Comparing Groups = 155
8.5 Exercises = 157
9 Tests on Categorical Data = 159
9.1 One Proportion = 160
9.2 Frequency Tables = 162
9.3 Exercises = 171
10 Analysis of Survival Times = 175
10.1 Survival Distributions = 175
10.2 Survival Probabilities = 176
10.3 Comparing Survival Curves in Two Groups = 180
Part Ⅲ Statistical Modeling
11 Linear Regression Models = 183
11.1 Linear Correlation = 184
11.2 General Linear Regression Model = 185
11.3 Patsy : The Formula Language = 190
11.4 Linear Regression Analysis with Python = 193
11.5 Model Results of Linear Regression Models = 198
11.6 Assumptions of Linear Regression Models = 214
11.7 Interpreting the Results of Linear Regression Models = 218
11.8 Bootstrapping = 219
11.9 Exercises = 220
12 Multivariate Data Analysis = 221
12.1 Visualizing Multivariate Correlations = 221
12.2 Multilinear Regression = 223
13 Tests on Discrete Data = 227
13.1 Comparing Groups of Ranked Data = 227
13.2 Logistic Regression = 228
13.3 Generalized Linear Models = 231
13.4 Ordinal Logistic Regression = 232
14 Bayesian Statistics = 237
14.1 Bayesian vs. Frequentist Interpretation = 237
14.2 The Bayesian Approach in the Age of Computers = 239
14.3 Example : Analysis of the Challenger Disaster with a Markov-Chain-Monte-Carlo Simulation = 240
14.4 Summing Up = 243
Solutions = 245
Glossary = 267
References = 273
Index = 275

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An Introduction to Statistics With Python (With Applications in the Life Sciences)

This textbook provides an introduction to the free software 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.