Chapter 1 Data
1.1 Data size
1.2 Types of variables
1.2.1 Qualitative and quantitative variables
1.2.2 Scale Level
1.2.3 Discrete and continuous variables

Chapter 2: Organizing One-Dimensional Data
2.1 Data-driven metrics
2.1.1 Average
2.1.2 Median
2.1.3 Mode
2.2 Data variance indicator
2.2.1 Variance and standard deviation
2.2.2 Range and Interquartile Range
2.2.3 Data Indicator Organization
2.3 Normalizing data
2.3.1 Standardization
2.3.2 Deviation value
2.4 Visualizing one-dimensional data
2.4.1 Frequency distribution table
2.4.2 Histogram
2.4.3 Boxplot

Chapter 3: Organizing Two-Dimensional Data
3.1 Indicators indicating the relationship between two data
3.1.1 Covariance
3.1.2 Correlation coefficient
3.2 Visualizing two-dimensional data
3.2.1 Scatterplot
3.2.2 Regression line
3.2.3 Heatmap
3.3 Anscombe's example

Chapter 4: Fundamentals of Inferential Statistics
4.1 Population and Sample
4.1.1 Sampling method
4.2 Probability Model
4.2.1 Basics of Probability
4.2.2 Probability distribution
4.3 Probability of inferential statistics
4.4 What you will learn from now on

Chapter 5 Discrete Random Variables
5.1 One-dimensional discrete random variables
5.1.1 Definition of one-dimensional discrete random variables
5.1.2 Indicators of one-dimensional discrete random variables
5.2 Two-dimensional discrete random variables
5.2.1 Definition of two-dimensional discrete random variables
5.2.2 Indicators of two-dimensional discrete random variables

Chapter 6: Representative Discrete Probability Distributions
6.1 Bernoulli distribution
6.2 Binomial distribution
6.3 Geometric distribution
6.4 Poisson distribution

Chapter 7 Continuous Random Variables
7.1 One-dimensional continuous random variables
7.1.1 Definition of one-dimensional continuous random variables
7.1.2 Indicators of one-dimensional continuous random variables
7.2 Two-dimensional continuous random variables
7.2.1 Definition of two-dimensional continuous random variables
7.2.2 Indicators of two-dimensional continuous random variables

Chapter 8: Representative Continuous Probability Distributions
8.1 Normal distribution
8.2 Exponential distribution
8.3 Chi-square distribution
8.4 t distribution
8.5 F distribution

Chapter 9 Independent and Identical Distributions
9.1 Independence
9.1.1 Definition of Independence
9.1.2 Independence and uncorrelatedness
9.2 Consensus distribution
9.2.1 Distribution of the sum of normal distributions
9.2.2 Distribution of the sum of the Poisson distribution
9.2.3 Distribution of the sum of Bernoulli distributions
9.3 Distribution of sample means
9.3.1 Distribution of sample means of normal distribution
9.3.2 Distribution of sample means of the Poisson distribution
9.3.3 Central Limit Theorem
9.3.4 Law of Large Numbers

Chapter 10 Statistical Estimation
10.1 Point Estimation
10.1.1 Point Estimation of the Population Mean
10.1.2 Point Estimation of Population Variance
10.1.3 Summary
10.2 Interval estimation
10.2.1 Estimating the population mean interval of a normal distribution: When the population variance is known
10.2.2 Estimating the population variance interval of a normal distribution
10.2.3 Estimating the population mean interval of a normal distribution: When the population variance is unknown
10.2.4 Interval estimation of the population mean of the Bernoulli distribution
10.2.5 Confidence interval for the population mean of the Poisson distribution

Chapter 11 Statistical Hypothesis Testing
11.1 Statistical Hypothesis Testing
11.1.1 Basics of Statistical Hypothesis Testing
11.1.2 One-tailed and two-tailed tests
11.1.3 Two Errors in Hypothesis Testing
11.2 Basic Hypothesis Testing
11.2.1 Test for the population mean of a normal distribution: When the population variance is known
11.2.2 Test for population variance of normal distribution
11.2.3 Test for the population mean of a normal distribution: When the population variance is unknown
11.3 Hypothesis Testing for Two-Sample Problems
11.3.1 Paired comparison t-test
11.3.2 Independent comparison t-test
11.3.3 Wilcoxon's signed-rank test
11.3.4 Only? Whitney's U Test
11.3.5 Chi-square test

Chapter 12 Regression Analysis
12.1 Simple Regression Model
12.1.1 Hypotheses in Regression Analysis
12.1.2 Regression analysis using statsmodels
12.1.3 Regression coefficients
12.2 Multiple regression model
12.2.1 Regression coefficients
12.2.2 Variables
12.3 Model Selection
12.3.1 Coefficient of Determination
12.3.2 Coefficient of adjustment
12.3.3 F black
12.3.4 Maximum Log-Likelihood and AIC
12.4 Validity of the Model
12.4.1 Normality Test
12.4.2 Durbin-Watson-Bee
12.4.3 Multicollinearity

Anyone can do it!
Statistics is difficult because there are many formulas.
Python lets you perform statistical analysis without complex formulas!
As you practice following the book, you will soon find yourself performing statistical analysis.

It's better when people like this see it.
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