Statistics Textbook for Learning with Python
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Description
Book Introduction
A friendly introduction to statistics, explained in Python code.
Important decisions in modern society, such as content recommendations and data analysis, are based on statistics.
However, there are many times when the concept is not easily understood with just statistical formulas and theories.
This book aims to alleviate such difficulties by providing an intuitive path to understanding through Python code and practice along with theory.
Analyze data with NumPy and Pandas libraries, and visualize data using Matplotlib and Seaborn.
We will cover the fundamentals of statistics, including descriptive statistics, probability and distribution, statistical estimation, and hypothesis testing, and explore the intersection with machine learning.
Learning statistics by analyzing actual data rather than just focusing on theory will be of great help.
If statistics felt difficult, start with this book.
Important decisions in modern society, such as content recommendations and data analysis, are based on statistics.
However, there are many times when the concept is not easily understood with just statistical formulas and theories.
This book aims to alleviate such difficulties by providing an intuitive path to understanding through Python code and practice along with theory.
Analyze data with NumPy and Pandas libraries, and visualize data using Matplotlib and Seaborn.
We will cover the fundamentals of statistics, including descriptive statistics, probability and distribution, statistical estimation, and hypothesis testing, and explore the intersection with machine learning.
Learning statistics by analyzing actual data rather than just focusing on theory will be of great help.
If statistics felt difficult, start with this book.
- You can preview some of the book's contents.
Preview
index
CHAPTER 1 Basic Statistics
_1.1 Statistics
_1.2 Why are descriptive statistics needed?
_1.3 Why Inferential Statistics Are Needed
CHAPTER 2: Python and Jupyter Notebooks
_2.1 Setting up the environment
_2.2 Jupyter Notebook
_2.3 Python Programming
_2.4 NumPy and Pandas
CHAPTER 3 Descriptive Statistics
_3.1 Data Classification
_3.2 How to read formulas
_3.3 Frequency distribution
_3.4 1-variate data statistics
_3.5 Multivariate Data Statistics
_3.6 Stratified Analysis
_3.7 Using Graphs
CHAPTER 4 PROBABILITY AND PROBABILITY DISTRIBUTIONS
_4.1 Probability Theory
_4.2 Probability distribution
_4.3 Binomial distribution
_4.4 Normal distribution
CHAPTER 5 Statistical Estimation
_5.1 Concept of statistical inference
_5.2 Sampling Simulation from a Population
_5.3 Estimation of population mean
_5.4 Estimation of population variance
_5.5 Probability distributions derived from normal populations
_5.6 Interval estimation
CHAPTER 6 Statistical Hypothesis Testing
_6.1 One-Sample t-Test for Population Mean
_6.2 Mean difference test
_6.3 Test of contingency table
_6.4 Interpreting the test results
CHAPTER 7 STATISTICAL MODELS
_7.1 Statistical Model Basics
_7.2 How to create a linear model
_7.3 Data Representation and Model Names
_7.4 Parameter Estimation: Likelihood Maximization
_7.5 Parameter Estimation: Loss Minimization
_7.6 Prediction Accuracy Evaluation and Variable Selection
CHAPTER 8 Regular Linear Models
_8.1 Model with one continuous independent variable: Simple regression
_8.2 Regular Linear Model Evaluation
_8.3 Analysis of variance
_8.4 Models with multiple independent variables
CHAPTER 9 Generalized Linear Models
_9.1 Generalized Linear Model Basics
_9.2 Logistic Regression
_9.3 Generalized Linear Model Evaluation
_9.4 Poisson regression
CHAPTER 10: STATISTICS AND MACHINE LEARNING
_10.1 Machine Learning Basics
_10.2 Regularization and Ridge Regression, Lasso Regression
_10.3 Ridge Regression and Lasso Regression Using Python
_10.4 Linear Models and Neural Networks
_1.1 Statistics
_1.2 Why are descriptive statistics needed?
_1.3 Why Inferential Statistics Are Needed
CHAPTER 2: Python and Jupyter Notebooks
_2.1 Setting up the environment
_2.2 Jupyter Notebook
_2.3 Python Programming
_2.4 NumPy and Pandas
CHAPTER 3 Descriptive Statistics
_3.1 Data Classification
_3.2 How to read formulas
_3.3 Frequency distribution
_3.4 1-variate data statistics
_3.5 Multivariate Data Statistics
_3.6 Stratified Analysis
_3.7 Using Graphs
CHAPTER 4 PROBABILITY AND PROBABILITY DISTRIBUTIONS
_4.1 Probability Theory
_4.2 Probability distribution
_4.3 Binomial distribution
_4.4 Normal distribution
CHAPTER 5 Statistical Estimation
_5.1 Concept of statistical inference
_5.2 Sampling Simulation from a Population
_5.3 Estimation of population mean
_5.4 Estimation of population variance
_5.5 Probability distributions derived from normal populations
_5.6 Interval estimation
CHAPTER 6 Statistical Hypothesis Testing
_6.1 One-Sample t-Test for Population Mean
_6.2 Mean difference test
_6.3 Test of contingency table
_6.4 Interpreting the test results
CHAPTER 7 STATISTICAL MODELS
_7.1 Statistical Model Basics
_7.2 How to create a linear model
_7.3 Data Representation and Model Names
_7.4 Parameter Estimation: Likelihood Maximization
_7.5 Parameter Estimation: Loss Minimization
_7.6 Prediction Accuracy Evaluation and Variable Selection
CHAPTER 8 Regular Linear Models
_8.1 Model with one continuous independent variable: Simple regression
_8.2 Regular Linear Model Evaluation
_8.3 Analysis of variance
_8.4 Models with multiple independent variables
CHAPTER 9 Generalized Linear Models
_9.1 Generalized Linear Model Basics
_9.2 Logistic Regression
_9.3 Generalized Linear Model Evaluation
_9.4 Poisson regression
CHAPTER 10: STATISTICS AND MACHINE LEARNING
_10.1 Machine Learning Basics
_10.2 Regularization and Ridge Regression, Lasso Regression
_10.3 Ridge Regression and Lasso Regression Using Python
_10.4 Linear Models and Neural Networks
Detailed image
Publisher's Review
Understanding the basics and practical application with formulas and Python code!
From data analysis to machine learning
Learning Statistics with Python
One of the reasons statistics can seem difficult is that there is so much to understand.
The important thing is to understand the connections between concepts.
This book is structured to help readers easily understand how concepts relate to one another.
Chapters 1 through 6 are introductory statistics books, covering descriptive statistics, the basics of probability and distribution, statistical estimation, and statistical hypothesis testing.
Chapters 7 through 10 cover analysis methods such as statistical models, regression analysis, and linear models.
Additionally, we have covered the intersection of machine learning and predictive technology, allowing you to understand the flow from the basics of statistics to machine learning.
Rather than delving into detailed statistical know-how and tips, we've done our best to lay the foundation for statistics by focusing on statistical terminology, basic formulas, and implementation using simple Python syntax.
The same content is explained three times using text, formulas, and Python code, so you will gradually be able to understand the content in depth.
I recommend this book to developers who are unfamiliar with statistics or readers who are unfamiliar with Python but want to study statistics to learn the statistics necessary for data analysis.
Changes from the first edition
● We have improved the structure by providing explanations along the implementation order so that beginners can learn easily.
● This book is for those who want to analyze data using Python.
I checked and corrected the Python practice code.
● The content on descriptive statistics, inferential statistics practice, etc. has increased.
There are more sections explaining techniques and ideas that are crucial for data processing, and more sections are devoted to practical techniques such as stratified analysis.
Key Contents
● Basic Statistics
● Python basics and Jupyter notebook setup
● Descriptive statistics
● Probability and probability distribution
● Statistical estimation and hypothesis testing
● Statistical model
● Regular linear model and generalized linear model
● Statistics and Machine Learning
From data analysis to machine learning
Learning Statistics with Python
One of the reasons statistics can seem difficult is that there is so much to understand.
The important thing is to understand the connections between concepts.
This book is structured to help readers easily understand how concepts relate to one another.
Chapters 1 through 6 are introductory statistics books, covering descriptive statistics, the basics of probability and distribution, statistical estimation, and statistical hypothesis testing.
Chapters 7 through 10 cover analysis methods such as statistical models, regression analysis, and linear models.
Additionally, we have covered the intersection of machine learning and predictive technology, allowing you to understand the flow from the basics of statistics to machine learning.
Rather than delving into detailed statistical know-how and tips, we've done our best to lay the foundation for statistics by focusing on statistical terminology, basic formulas, and implementation using simple Python syntax.
The same content is explained three times using text, formulas, and Python code, so you will gradually be able to understand the content in depth.
I recommend this book to developers who are unfamiliar with statistics or readers who are unfamiliar with Python but want to study statistics to learn the statistics necessary for data analysis.
Changes from the first edition
● We have improved the structure by providing explanations along the implementation order so that beginners can learn easily.
● This book is for those who want to analyze data using Python.
I checked and corrected the Python practice code.
● The content on descriptive statistics, inferential statistics practice, etc. has increased.
There are more sections explaining techniques and ideas that are crucial for data processing, and more sections are devoted to practical techniques such as stratified analysis.
Key Contents
● Basic Statistics
● Python basics and Jupyter notebook setup
● Descriptive statistics
● Probability and probability distribution
● Statistical estimation and hypothesis testing
● Statistical model
● Regular linear model and generalized linear model
● Statistics and Machine Learning
GOODS SPECIFICS
- Date of issue: November 29, 2024
- Page count, weight, size: 528 pages | 183*235*35mm
- ISBN13: 9791169213196
- ISBN10: 1169213197
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