Statistics and Applications for Data Science with R and Python
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Description
Book Introduction
At the statistical level, using data to compare one group, two groups, or multiple groups plays a fundamental and very important role.
"Statistics and Applications for Data Science with R and Python" provides the fundamental knowledge of statistics required for data science.
In addition, R, which is well known to statistical users, and Python, which has recently been used more frequently by software developers, are being utilized, and these are establishing themselves as software actively used in data science.
In each chapter, R performs statistical processing on data from various views, and corresponding exercises for Python are provided in the last section of each chapter.
I hope this book will be helpful to students in data science who want to understand statistics and analyze real-world data at an applied level using R and Python, as well as students from other majors.
"Statistics and Applications for Data Science with R and Python" provides the fundamental knowledge of statistics required for data science.
In addition, R, which is well known to statistical users, and Python, which has recently been used more frequently by software developers, are being utilized, and these are establishing themselves as software actively used in data science.
In each chapter, R performs statistical processing on data from various views, and corresponding exercises for Python are provided in the last section of each chapter.
I hope this book will be helpful to students in data science who want to understand statistics and analyze real-world data at an applied level using R and Python, as well as students from other majors.
- You can preview some of the book's contents.
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index
Chapter 1 Data and Statistics
1.1 Statistical Data ······ 2
1.2 Statistics ······ 2
1.3 Data Collection ······ 4
1.4 Types of data ······ 7
1.5 Summary of Data ······ 12
1.6 Normality Evaluation ······ 22
1.7 Summary of Text Materials ······ 28
1.8 Python Code Practice ······ 36
1.9 Practice Problems ······ 50
Chapter 2 Comparative Testing of a Group
2.1 Problems with Statistical Hypothesis Testing... 54
2.2 Population mean test ······ 55
2.3 One-tailed and two-tailed tests... 59
2.4 Mobiproportionality Test ······ 66
2.5 Python Code Practice ······ 71
2.6 Practice Problems ······ 77
Chapter 3 Comparative Test of Two Groups
3.1 Comparison of the Two Groups... 80
3.2 Comparison of Population Means of Independent Samples ······ 82
3.3 Comparison of population means of paired samples ······ 95
3.4 Comparison of Population Proportions of Independent Samples... 101
3.5 Comparison of Population Variances of Independent Samples... 108
3.6 Python Code Practice ······ 113
3.7 Practice Problems ······ 119
Chapter 4 Comparative Testing of Multiple Groups
4.1 Comparison of Multiple Groups... 124
4.2 One-Way ANOVA ······ 125
4.3 Multiple comparisons... 128
4.4 Probabilistic Full-Block Design... 134
4.5 Python Code Practice ······ 141
4.6 Practice Problems ······ 148
Chapter 5: Correlation Analysis and Regression Analysis
5.1 Correlation Analysis ······ 152
5.2 Simple Regression Analysis... 157
5.3 Least Squares and Residuals... 160
5.4 Significance of the Fitted Regression Equation... 167
5.5 Multiple Regression Analysis... 176
5.6 Python Code Practice ······ 184
5.7 Practice Problems ······ 192
Chapter 6 Comparative Testing of Categorical Data
6.1 Contingency Table for Categorical Data... 198
6.2 Chi-square test ······ 200
6.3 Fisher's exact test... 208
6.4 Python Code Practice ······ 212
6.5 Practice Problems ······ 218
Chapter 7 Generalized Linear Model Analysis
7.1 Logistic Regression and Discriminant Analysis... 224
7.2 Repeated Measures Analysis of Variance ······ 234
7.3 Covariance Analysis ······ 240
7.4 Python Code Practice ······ 251
7.5 Practice Problems ······ 265
Chapter 8 Nonparametric Comparative Tests
8.1 Nonparametric Methods ······ 272
8.2 Comparing Groups: Signed Test and Wilcoxon Signed-Rank Test... 273
8.3 Comparing Two Groups: Rank Sum Test... 280
8.4 Comparing Multiple Groups: The Kruskal-Wallis Test... 289
8.5 Rank correlation analysis... 296
8.6 Python Code Practice ······ 310
8.7 Practice Problems ······ 322
Chapter 9 Machine Learning
9.1 Support Vector Machines... 326
9.2 Classification Tree ······ 335
9.3 Random Forest ······ 345
9.4 Artificial Neural Networks... 352
9.5 Python Code Practice ······ 360
9.6 Practice Problems ······ 367
Practice Problem Solutions … … 371
Appendix I: List of Codes and Materials … … 392
Appendix II: Installing and Using R and RStudio… … 398
Appendix III: Python Installation and Usage … … 402
References … … 404
Search … … 405
Author's Note… … 411
About the Author … … 413
1.1 Statistical Data ······ 2
1.2 Statistics ······ 2
1.3 Data Collection ······ 4
1.4 Types of data ······ 7
1.5 Summary of Data ······ 12
1.6 Normality Evaluation ······ 22
1.7 Summary of Text Materials ······ 28
1.8 Python Code Practice ······ 36
1.9 Practice Problems ······ 50
Chapter 2 Comparative Testing of a Group
2.1 Problems with Statistical Hypothesis Testing... 54
2.2 Population mean test ······ 55
2.3 One-tailed and two-tailed tests... 59
2.4 Mobiproportionality Test ······ 66
2.5 Python Code Practice ······ 71
2.6 Practice Problems ······ 77
Chapter 3 Comparative Test of Two Groups
3.1 Comparison of the Two Groups... 80
3.2 Comparison of Population Means of Independent Samples ······ 82
3.3 Comparison of population means of paired samples ······ 95
3.4 Comparison of Population Proportions of Independent Samples... 101
3.5 Comparison of Population Variances of Independent Samples... 108
3.6 Python Code Practice ······ 113
3.7 Practice Problems ······ 119
Chapter 4 Comparative Testing of Multiple Groups
4.1 Comparison of Multiple Groups... 124
4.2 One-Way ANOVA ······ 125
4.3 Multiple comparisons... 128
4.4 Probabilistic Full-Block Design... 134
4.5 Python Code Practice ······ 141
4.6 Practice Problems ······ 148
Chapter 5: Correlation Analysis and Regression Analysis
5.1 Correlation Analysis ······ 152
5.2 Simple Regression Analysis... 157
5.3 Least Squares and Residuals... 160
5.4 Significance of the Fitted Regression Equation... 167
5.5 Multiple Regression Analysis... 176
5.6 Python Code Practice ······ 184
5.7 Practice Problems ······ 192
Chapter 6 Comparative Testing of Categorical Data
6.1 Contingency Table for Categorical Data... 198
6.2 Chi-square test ······ 200
6.3 Fisher's exact test... 208
6.4 Python Code Practice ······ 212
6.5 Practice Problems ······ 218
Chapter 7 Generalized Linear Model Analysis
7.1 Logistic Regression and Discriminant Analysis... 224
7.2 Repeated Measures Analysis of Variance ······ 234
7.3 Covariance Analysis ······ 240
7.4 Python Code Practice ······ 251
7.5 Practice Problems ······ 265
Chapter 8 Nonparametric Comparative Tests
8.1 Nonparametric Methods ······ 272
8.2 Comparing Groups: Signed Test and Wilcoxon Signed-Rank Test... 273
8.3 Comparing Two Groups: Rank Sum Test... 280
8.4 Comparing Multiple Groups: The Kruskal-Wallis Test... 289
8.5 Rank correlation analysis... 296
8.6 Python Code Practice ······ 310
8.7 Practice Problems ······ 322
Chapter 9 Machine Learning
9.1 Support Vector Machines... 326
9.2 Classification Tree ······ 335
9.3 Random Forest ······ 345
9.4 Artificial Neural Networks... 352
9.5 Python Code Practice ······ 360
9.6 Practice Problems ······ 367
Practice Problem Solutions … … 371
Appendix I: List of Codes and Materials … … 392
Appendix II: Installing and Using R and RStudio… … 398
Appendix III: Python Installation and Usage … … 402
References … … 404
Search … … 405
Author's Note… … 411
About the Author … … 413
GOODS SPECIFICS
- Date of issue: September 5, 2025
- Page count, weight, size: 424 pages | 188*257*30mm
- ISBN13: 9791160737776
- ISBN10: 1160737770
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