59 Statistics Questions Completely Answered
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Description
Book Introduction
Statistics are always confusing in practice. This time, let's understand them properly!
Statistics is more than just an analytical technique; it's an essential tool for interpreting the meaning of data and deriving reliable results.
However, when many people encounter statistics in practice, they often become confused about how to apply them.
This book starts from such practical concerns.
It systematically explores key statistical concepts centered on four key situations, focusing on topics that have not been covered much in the past but are actually important and have a decisive impact on statistical decision-making.
Additionally, rather than simply listing formulas, it explains their meaning and context one by one, leading to a deeper understanding of the concepts.
I recommend this book to practitioners who want to go beyond simple data analysis and derive deep statistical insights.
Statistics is more than just an analytical technique; it's an essential tool for interpreting the meaning of data and deriving reliable results.
However, when many people encounter statistics in practice, they often become confused about how to apply them.
This book starts from such practical concerns.
It systematically explores key statistical concepts centered on four key situations, focusing on topics that have not been covered much in the past but are actually important and have a decisive impact on statistical decision-making.
Additionally, rather than simply listing formulas, it explains their meaning and context one by one, leading to a deeper understanding of the concepts.
I recommend this book to practitioners who want to go beyond simple data analysis and derive deep statistical insights.
- You can preview some of the book's contents.
Preview
index
CHAPTER 1 Basic Questions That Occur While Learning Statistics
Curiosity 01 What kind of discipline is statistics?
Curiosity 02 Why should we learn probability theory?
Curiosity 03 Statistics vs. Probability Theory
Curiosity 04: Why does it appear when learning machine learning?
Curiosity 05 What is the statistical meaning of the statement that correlation does not necessarily mean causation?
Question 06 How do we evaluate causality?
Curiosity 07 What is the intuitive meaning of adding an interaction effect?
Curiosity 08 What challenges are currently facing statistics?
Curiosity 09 What is dimension in statistical analysis?
Question 10: How to reduce the dimensionality (number of features in input variables)?
Question 11: When can I miss forward selection and backward removal?
Curiosity 12: Lasso vs. Ridge Regularization
Curiosity 13: Central Limit Theorem vs. Law of Large Numbers
Question 14: What is the difference between the various terms that represent averages (expectation, mean, average)?
Question 15: If the variance of an input variable is too small, does that mean it has no explanatory power?
Curiosity 16: Standardization vs. Normalization
Question 17 How do I transform non-normally distributed data into a normal distribution?
Question 18: Normality Transformation for Right-Skewed Distributions
Question 19: What happens if we simply log-transform sample data that already follows a normal distribution?
Curiosity 20 Normality Transformation for Left-Skewed Distributions
Curiosity 21: What does it mean to stabilize the distribution?
Question 22: What are the covariance matrix and correlation matrix?
CHAPTER 2 Questions that arise while learning inferential statistics
Curiosity 23 Likelihood vs. Probability
Question 24: How do you calculate likelihood for multiple samples?
Question 25: What is the intuitive meaning of maximizing likelihood in discrete data?
Curiosity 26 How does a computer calculate maximum likelihood estimates?
Curiosity 27 How can we calculate the maximum likelihood estimate for discrete data using a formula? (feat.
differential)
Curiosity 28 Why do we take logs in Udo?
Question 29: How can we mathematically calculate the maximum likelihood estimate for continuous data?
Question 30: What does it intuitively mean to maximize likelihood in continuous data?
Curiosity 31 What is the intuitive meaning of maximizing the posterior probability? (feat.
Bayesian)
Curiosity 32: Sampling vs. Resampling
Question 33: What are the main types of probability sampling?
Question 34: What are the main types of non-probability sampling?
Question 35: What are the main types of resampling?
CHAPTER 3 Questions that arise while learning about hypothesis testing
Question 36: Hypothesis Testing vs. Maximum Likelihood Estimation
Curiosity 37: Null Hypothesis vs. Alternative Hypothesis
Curiosity 38 What is the intuition behind what the number p-value actually means?
Question 39: What is a one-sample t-test?
Question 40: What is a two-sample t-test?
Question 41: One-Sample t-Test vs. Two-Sample t-Test vs. ANOVA
Question 42: Where does the paired t-test fall?
CHAPTER 4 Questions that arise while learning regression analysis
Curiosity 43 What does it mean to train a linear model?
Question 44 Observed value (y) VS Fitted value ( )
Question 45: Maximum Likelihood Estimation vs. Least Squares in Linear Models
Question 46: What is the basis for linearity in linear models?
Question 47: What is the null hypothesis in linear regression analysis?
Curiosity 48: Deviation vs. Error vs. Residual
Question 49: Standard Deviation vs. Standard Error
Question 50: Multiple Linear Regression vs. Multivariate Linear Regression
Question 51: What are the assumptions made in linear regression?
Question 52: Quantitative Data vs. Qualitative Data
Question 53: How to model qualitative output variables?
Question 54: What does a model look like in logistic regression analysis?
Question 55: What are some metrics used to evaluate the performance of classification problems?
Question 56: What does the x-axis value of the ROC curve mean?
Question 57: What does the y-axis value of the ROC curve mean?
Curiosity 58 Precision vs. Recall
Question 59: How should we evaluate data with an imbalance between positive and negative values?
Curiosity 01 What kind of discipline is statistics?
Curiosity 02 Why should we learn probability theory?
Curiosity 03 Statistics vs. Probability Theory
Curiosity 04: Why does it appear when learning machine learning?
Curiosity 05 What is the statistical meaning of the statement that correlation does not necessarily mean causation?
Question 06 How do we evaluate causality?
Curiosity 07 What is the intuitive meaning of adding an interaction effect?
Curiosity 08 What challenges are currently facing statistics?
Curiosity 09 What is dimension in statistical analysis?
Question 10: How to reduce the dimensionality (number of features in input variables)?
Question 11: When can I miss forward selection and backward removal?
Curiosity 12: Lasso vs. Ridge Regularization
Curiosity 13: Central Limit Theorem vs. Law of Large Numbers
Question 14: What is the difference between the various terms that represent averages (expectation, mean, average)?
Question 15: If the variance of an input variable is too small, does that mean it has no explanatory power?
Curiosity 16: Standardization vs. Normalization
Question 17 How do I transform non-normally distributed data into a normal distribution?
Question 18: Normality Transformation for Right-Skewed Distributions
Question 19: What happens if we simply log-transform sample data that already follows a normal distribution?
Curiosity 20 Normality Transformation for Left-Skewed Distributions
Curiosity 21: What does it mean to stabilize the distribution?
Question 22: What are the covariance matrix and correlation matrix?
CHAPTER 2 Questions that arise while learning inferential statistics
Curiosity 23 Likelihood vs. Probability
Question 24: How do you calculate likelihood for multiple samples?
Question 25: What is the intuitive meaning of maximizing likelihood in discrete data?
Curiosity 26 How does a computer calculate maximum likelihood estimates?
Curiosity 27 How can we calculate the maximum likelihood estimate for discrete data using a formula? (feat.
differential)
Curiosity 28 Why do we take logs in Udo?
Question 29: How can we mathematically calculate the maximum likelihood estimate for continuous data?
Question 30: What does it intuitively mean to maximize likelihood in continuous data?
Curiosity 31 What is the intuitive meaning of maximizing the posterior probability? (feat.
Bayesian)
Curiosity 32: Sampling vs. Resampling
Question 33: What are the main types of probability sampling?
Question 34: What are the main types of non-probability sampling?
Question 35: What are the main types of resampling?
CHAPTER 3 Questions that arise while learning about hypothesis testing
Question 36: Hypothesis Testing vs. Maximum Likelihood Estimation
Curiosity 37: Null Hypothesis vs. Alternative Hypothesis
Curiosity 38 What is the intuition behind what the number p-value actually means?
Question 39: What is a one-sample t-test?
Question 40: What is a two-sample t-test?
Question 41: One-Sample t-Test vs. Two-Sample t-Test vs. ANOVA
Question 42: Where does the paired t-test fall?
CHAPTER 4 Questions that arise while learning regression analysis
Curiosity 43 What does it mean to train a linear model?
Question 44 Observed value (y) VS Fitted value ( )
Question 45: Maximum Likelihood Estimation vs. Least Squares in Linear Models
Question 46: What is the basis for linearity in linear models?
Question 47: What is the null hypothesis in linear regression analysis?
Curiosity 48: Deviation vs. Error vs. Residual
Question 49: Standard Deviation vs. Standard Error
Question 50: Multiple Linear Regression vs. Multivariate Linear Regression
Question 51: What are the assumptions made in linear regression?
Question 52: Quantitative Data vs. Qualitative Data
Question 53: How to model qualitative output variables?
Question 54: What does a model look like in logistic regression analysis?
Question 55: What are some metrics used to evaluate the performance of classification problems?
Question 56: What does the x-axis value of the ROC curve mean?
Question 57: What does the y-axis value of the ROC curve mean?
Curiosity 58 Precision vs. Recall
Question 59: How should we evaluate data with an imbalance between positive and negative values?
Detailed image
Into the book
The most important thing I focused on while preparing the book was helping readers understand it intuitively.
We have made extensive use of pictures and diagrams to visually explain complex concepts as much as possible, enabling readers to grasp the flow of statistics at a glance.
Formulas are explained rigorously when absolutely necessary, but parts that can be sufficiently conveyed intuitively are boldly omitted.
Thanks to the feedback I received from a variety of statistical experts and non-experts throughout the process, I was able to achieve both academic depth and practicality.
It also includes concepts that are used in actual statistical work but are not often covered, focusing on more fundamental aspects to help readers understand the big picture of statistics.
I hope that it doesn't just end with the book, but that it communicates with readers and provides practical learning.
We have made extensive use of pictures and diagrams to visually explain complex concepts as much as possible, enabling readers to grasp the flow of statistics at a glance.
Formulas are explained rigorously when absolutely necessary, but parts that can be sufficiently conveyed intuitively are boldly omitted.
Thanks to the feedback I received from a variety of statistical experts and non-experts throughout the process, I was able to achieve both academic depth and practicality.
It also includes concepts that are used in actual statistical work but are not often covered, focusing on more fundamental aspects to help readers understand the big picture of statistics.
I hope that it doesn't just end with the book, but that it communicates with readers and provides practical learning.
--- From the author's note
Publisher's Review
If you are into data analysis, you will have questions about statistics.
Let's understand more precisely and gain statistical insights!
To find the probability distribution that best fits my sample data, should I use hypothesis testing or an approach based on maximum likelihood estimation? If familiar statistical terms appear at first glance, but you find it difficult to answer these questions directly, this book is for you.
Anyone who works with data knows how powerful a tool statistics can be.
However, even after learning various concepts, there are many cases where a gap between theory and reality is felt when trying to apply them to actual analysis.
This book unravels concepts that permeate the broad framework of statistics by raising questions that arise in four major situations.
It focuses on topics that have not been covered much in the past but are actually important and frequently used theories.
It clearly explains how similar terms and concepts actually differ, and unlike many statistics books that try to minimize formulas, it starts with the basic formulas and explains step-by-step what conclusions they lead to.
It helps you understand the concepts more deeply by breaking down the formulas one by one, and lays a solid foundation for the concepts you will learn later.
If you still find statistics confusing or difficult, this book will definitely help.
* Video lecture: https://www.youtube.com/@paintingscientist/featured
Let's understand more precisely and gain statistical insights!
To find the probability distribution that best fits my sample data, should I use hypothesis testing or an approach based on maximum likelihood estimation? If familiar statistical terms appear at first glance, but you find it difficult to answer these questions directly, this book is for you.
Anyone who works with data knows how powerful a tool statistics can be.
However, even after learning various concepts, there are many cases where a gap between theory and reality is felt when trying to apply them to actual analysis.
This book unravels concepts that permeate the broad framework of statistics by raising questions that arise in four major situations.
It focuses on topics that have not been covered much in the past but are actually important and frequently used theories.
It clearly explains how similar terms and concepts actually differ, and unlike many statistics books that try to minimize formulas, it starts with the basic formulas and explains step-by-step what conclusions they lead to.
It helps you understand the concepts more deeply by breaking down the formulas one by one, and lays a solid foundation for the concepts you will learn later.
If you still find statistics confusing or difficult, this book will definitely help.
* Video lecture: https://www.youtube.com/@paintingscientist/featured
GOODS SPECIFICS
- Date of issue: May 12, 2025
- Page count, weight, size: 264 pages | 396g | 152*225*12mm
- ISBN13: 9791140714568
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