General Statistics
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Description
Book Introduction
The Era of Big Data and Artificial Intelligence: The Essentials of Statistics
Now that data has become central to industry and research, statistics is an essential discipline for effectively analyzing and utilizing it.
"General Statistics (3rd Edition)" is an introductory book designed to systematically organize basic statistical concepts and develop data analysis skills through R and Python practice.
This third edition, revised for the first time in 25 years, adds practical exercises using R and Python instead of SAS, allowing readers to implement the theory through programming, and reinforces examples reflecting real-life data.
This book is designed to be systematically studied by anyone, from non-majors new to statistics to beginners learning data analysis, making it suitable for both lectures and self-study.
Let's develop statistical thinking skills by learning how to properly understand and analyze data.
Now that data has become central to industry and research, statistics is an essential discipline for effectively analyzing and utilizing it.
"General Statistics (3rd Edition)" is an introductory book designed to systematically organize basic statistical concepts and develop data analysis skills through R and Python practice.
This third edition, revised for the first time in 25 years, adds practical exercises using R and Python instead of SAS, allowing readers to implement the theory through programming, and reinforces examples reflecting real-life data.
This book is designed to be systematically studied by anyone, from non-majors new to statistics to beginners learning data analysis, making it suitable for both lectures and self-study.
Let's develop statistical thinking skills by learning how to properly understand and analyze data.
- You can preview some of the book's contents.
Preview
index
CHAPTER 01 Data Creation
1.1 What is Statistics?
1.2 Simple random sampling
1.3 Things to keep in mind when conducting a sample survey
1.4 Statistical Experiments
1.5 Comparative Experiments and Randomization
1.6 Blocking
Practice problems
CHAPTER 02 Population and Sample
2.1 Distribution of the population
2.2 Representative value of the population
2.3 Plotting the sample
2.4 Representative value of the sample
Practice problems
CHAPTER 03 Probability and Probability Distributions
3.1 Definition of probability
3.2 Conditional probability and independent events
3.3 Random variables and probability distributions
3.4 Expected values and their properties
3.5 Joint distribution of two random variables
3.6 Covariance and Correlation Coefficient
Practice problems
CHAPTER 04 Sampling Distribution
4.1 Bernoulli distribution and normal distribution as population distributions
4.2 Sampling distribution
4.3 Binomial distribution
4.4 Distribution of sample means
4.5 Chi-square, t, and F distributions
4.6 Normal distribution quantile contrast plot
Practice problems
CHAPTER 05 Statistical Inference
5.1 Estimate
5.2 Significance testing and hypothesis testing
Practice problems
CHAPTER 06 Inference about Distributions
6.1 Inference about the population mean
6.2 Comparison of population means by corresponding comparison
6.3 Comparison of population means by this sample
6.4 Inference about the population variance
Practice problems
CHAPTER 07 Analysis of Discrete Data
7.1 Estimation and testing of the population ratio
7.2 Comparison of two ratios
7.3 Comparison of multiple populations using categorical data
7.4 Testing independence using categorical data
Practice problems
CHAPTER 08 Correlation Analysis and Regression Analysis
8.1 Introduction
8.2 Correlation Analysis
8.3 Model and fit of simple regression analysis
8.4 Inference in Simple Regression Analysis
8.5 Residual Analysis in Simple Regression Analysis
8.6 Multiple Regression Analysis
Practice problems
CHAPTER 09 Analysis of Variance
9.1 What is ANOVA?
9.2 One-Way Arrangement Method
9.3 Two-way arrangement without repetition
9.4 Two-way arrangement with repetition
Practice problems
CHAPTER 10 Nonparametric Inference
10.1 Introduction
10.2 Wilcoxon rank-sum test
10.3 Wilcoxon signed-rank test
Practice problems
CHAPTER 11 TIME SERIES ANALYSIS
11.1 Various types of time series data
11.2 Analysis Methods for Time Series Data
11.3 Trend Analysis Method
11.4 Moving Average Method
11.5 Exponential Smoothing
Practice problems
CHAPTER 12 SAMPLE SURVEY
12.1 Basic Concepts of Sampling
12.2 Sampling Methods
12.3 Estimation by stratified random sampling
12.4 Problems in Actual Research
Practice problems
APPENDIX A General Statistics Practice with R
A.1 R Basics
A.2 Chapter 2 Population and Sample Practice
A.3 Chapter 3 Probability and Probability Distribution Practice
A.4 Chapter 4 Sampling Distribution Practice
A.5 Chapter 5 Statistical Inference Practice
A.6 Chapter 6 Distribution Inference Practice
A.7 Chapter 7 Discrete Data Analysis Practice
A.8 Chapter 8 Correlation Analysis and Regression Analysis Practice
A.9 Chapter 9 Analysis of Variance Practice
A.10 Chapter 10 Nonparametric Inference Practice
A.11 Chapter 11 Time Series Analysis Practice
A.12 Chapter 12 Sampling Practice
APPENDIX B General Statistics Practice with Python
B.1 Python Basics
B.2 Chapter 2 Population and Sample Practice
B.3 Chapter 3 Probability and Probability Distributions Practice
B.4 Chapter 4 Sampling Distribution Practice
B.5 Chapter 5 Statistical Inference Practice
B.6 Chapter 6 Distribution Inference Practice
B.7 Chapter 7 Discrete Data Analysis Practice
B.8 Chapter 8 Correlation Analysis and Regression Analysis Practice
B.9 Chapter 9 Analysis of Variance Practice
B.10 Chapter 10 Nonparametric Inference Practice
B.11 Chapter 11 Time Series Analysis Practice
B.12 Chapter 12 Sample Survey Practice
APPENDIX C Various distribution tables
C.1 Standard Normal Distribution Table
C.2 t distribution table
C.3 Chi-square distribution table
C.4 F distribution table
C.5 Wilcoxon rank sum statistic cumulative probability distribution table
C.6 Wilcoxon Signed Rank Statistic Cumulative Probability Distribution Table
1.1 What is Statistics?
1.2 Simple random sampling
1.3 Things to keep in mind when conducting a sample survey
1.4 Statistical Experiments
1.5 Comparative Experiments and Randomization
1.6 Blocking
Practice problems
CHAPTER 02 Population and Sample
2.1 Distribution of the population
2.2 Representative value of the population
2.3 Plotting the sample
2.4 Representative value of the sample
Practice problems
CHAPTER 03 Probability and Probability Distributions
3.1 Definition of probability
3.2 Conditional probability and independent events
3.3 Random variables and probability distributions
3.4 Expected values and their properties
3.5 Joint distribution of two random variables
3.6 Covariance and Correlation Coefficient
Practice problems
CHAPTER 04 Sampling Distribution
4.1 Bernoulli distribution and normal distribution as population distributions
4.2 Sampling distribution
4.3 Binomial distribution
4.4 Distribution of sample means
4.5 Chi-square, t, and F distributions
4.6 Normal distribution quantile contrast plot
Practice problems
CHAPTER 05 Statistical Inference
5.1 Estimate
5.2 Significance testing and hypothesis testing
Practice problems
CHAPTER 06 Inference about Distributions
6.1 Inference about the population mean
6.2 Comparison of population means by corresponding comparison
6.3 Comparison of population means by this sample
6.4 Inference about the population variance
Practice problems
CHAPTER 07 Analysis of Discrete Data
7.1 Estimation and testing of the population ratio
7.2 Comparison of two ratios
7.3 Comparison of multiple populations using categorical data
7.4 Testing independence using categorical data
Practice problems
CHAPTER 08 Correlation Analysis and Regression Analysis
8.1 Introduction
8.2 Correlation Analysis
8.3 Model and fit of simple regression analysis
8.4 Inference in Simple Regression Analysis
8.5 Residual Analysis in Simple Regression Analysis
8.6 Multiple Regression Analysis
Practice problems
CHAPTER 09 Analysis of Variance
9.1 What is ANOVA?
9.2 One-Way Arrangement Method
9.3 Two-way arrangement without repetition
9.4 Two-way arrangement with repetition
Practice problems
CHAPTER 10 Nonparametric Inference
10.1 Introduction
10.2 Wilcoxon rank-sum test
10.3 Wilcoxon signed-rank test
Practice problems
CHAPTER 11 TIME SERIES ANALYSIS
11.1 Various types of time series data
11.2 Analysis Methods for Time Series Data
11.3 Trend Analysis Method
11.4 Moving Average Method
11.5 Exponential Smoothing
Practice problems
CHAPTER 12 SAMPLE SURVEY
12.1 Basic Concepts of Sampling
12.2 Sampling Methods
12.3 Estimation by stratified random sampling
12.4 Problems in Actual Research
Practice problems
APPENDIX A General Statistics Practice with R
A.1 R Basics
A.2 Chapter 2 Population and Sample Practice
A.3 Chapter 3 Probability and Probability Distribution Practice
A.4 Chapter 4 Sampling Distribution Practice
A.5 Chapter 5 Statistical Inference Practice
A.6 Chapter 6 Distribution Inference Practice
A.7 Chapter 7 Discrete Data Analysis Practice
A.8 Chapter 8 Correlation Analysis and Regression Analysis Practice
A.9 Chapter 9 Analysis of Variance Practice
A.10 Chapter 10 Nonparametric Inference Practice
A.11 Chapter 11 Time Series Analysis Practice
A.12 Chapter 12 Sampling Practice
APPENDIX B General Statistics Practice with Python
B.1 Python Basics
B.2 Chapter 2 Population and Sample Practice
B.3 Chapter 3 Probability and Probability Distributions Practice
B.4 Chapter 4 Sampling Distribution Practice
B.5 Chapter 5 Statistical Inference Practice
B.6 Chapter 6 Distribution Inference Practice
B.7 Chapter 7 Discrete Data Analysis Practice
B.8 Chapter 8 Correlation Analysis and Regression Analysis Practice
B.9 Chapter 9 Analysis of Variance Practice
B.10 Chapter 10 Nonparametric Inference Practice
B.11 Chapter 11 Time Series Analysis Practice
B.12 Chapter 12 Sample Survey Practice
APPENDIX C Various distribution tables
C.1 Standard Normal Distribution Table
C.2 t distribution table
C.3 Chi-square distribution table
C.4 F distribution table
C.5 Wilcoxon rank sum statistic cumulative probability distribution table
C.6 Wilcoxon Signed Rank Statistic Cumulative Probability Distribution Table
Detailed image
Publisher's Review
A statistics textbook essential for today's times
"General Statistics (3rd Edition)" is a textbook that systematically organizes the basic concepts of statistics and can be used in statistics classes through practical exercises using R and Python.
It also covers advanced topics such as nonparametric inference, time series analysis, and sample surveys, providing a broad understanding of statistics.
For readers who want to systematically study statistics and practice programming, this book will be an essential guide.
"General Statistics (3rd Edition)" is a textbook that systematically organizes the basic concepts of statistics and can be used in statistics classes through practical exercises using R and Python.
It also covers advanced topics such as nonparametric inference, time series analysis, and sample surveys, providing a broad understanding of statistics.
For readers who want to systematically study statistics and practice programming, this book will be an essential guide.
GOODS SPECIFICS
- Date of issue: March 2, 2025
- Page count, weight, size: 572 pages | 1,001g | 188*257*20mm
- ISBN13: 9791173400254
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