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 In modern times, when it’s difficult to directly investigate vast amounts of overall data (populations), “sample surveys” and “estimation” play crucial roles in statistics. This article explains what sample surveys and estimation are, how they are used, and the benefits of learning these techniques. 1. What are Sample Surveys and Estimation? A sample survey is a method of investigating a portion of the overall data (a sample) and, based on the results, understanding the characteristics and trends of the entire population. - Why are Sample Surveys Necessary? Investigating the entire population takes a lot of time and resources. Therefore, it is practical to infer the overall trends using a randomly selected subset of data. - Ensuring Representativeness The sample must be obtained using methods such as random sampling or stratified sampling to faithfully reflect the characteristics of the population. Estimation is the process of “guessing” the characteristics (mean, variance, ratio,...

 In modern society, we are surrounded by various uncertainties and random phenomena. From the weather and stock prices to the outcomes of sports and even small daily choices, the concepts of probability and probability distributions are powerful tools for understanding these uncertainties quantitatively. This article explains what probability and probability distributions are, where they are used, and the benefits of learning these concepts. 1. What are Probability and Probability Distributions? Probability is a way of expressing the likelihood of an event occurring as a number between 0 and 1. 0 means the event will not occur, and 1 means the event will definitely occur. The mathematical thinking behind probability is often subtly present when we talk about the “likelihood” of something happening in everyday life. A probability distribution systematically represents all possible outcomes and the probability of each outcome. - Discrete Probability Distribution This applies to distr...

 In modern times, when faced with vast amounts of data and wondering “what can we say about it?”, the first step is often descriptive statistics. Descriptive statistics are basic techniques used to grasp the overall picture of data and express its characteristics through numbers and graphs – they can be considered the starting point for data analysis. Here, we will explain what descriptive statistics are, the situations in which they are used, and the benefits of learning them. 1. What are Descriptive Statistics? Descriptive statistics are methods for summarizing data sets numerically and visually, revealing their characteristics and patterns. Specifically, they include the following elements. - Measures of Central Tendency Such as the mean, median, and mode, which indicate the central position of the data and are used to understand the overall trend. - Measures of Dispersion Calculating indicators like variance, standard deviation, range, and interquartile range to show the spread...

 In today's information society, we are overwhelmed with vast amounts of data, and the ‘essence’ is often hidden behind numbers and tables, making it difficult to convey. This is where data visualization comes in. Data visualization is a technique that transforms complex data into visual representations such as graphs, charts, and infographics, facilitating intuitive understanding and supporting decision-making. This article explains what data visualization is, how it is used, and the benefits of learning it. 1. What is Data Visualization? Data visualization plays the following roles: - Concretizing Abstract Information: It converts rows of numbers and vast statistical data into visuals such as graphs, charts, heatmaps, and network diagrams. This allows you to instantly see patterns, trends, outliers, and relationships, and communicate the ‘story’ of the data. - Promoting Decision-Making: Visualized data serves as a basis for making important business decisions. Even in situations ...

 In today’s world, overflowing with vast amounts of data, there is a growing need for methods to extract essential patterns. Principal Component Analysis (PCA) is a powerful technique that summarizes complex, high-dimensional data into fewer dimensions, contributing to data organization, visualization, and even noise reduction. Here, we will explain what principal component analysis is, the situations in which it is used, and the benefits of learning this technique. 1. What is Principal Component Analysis? Principal Component Analysis is one method of dimensionality reduction. It efficiently represents data by replacing multiple correlated variables with a smaller number of new variables (principal components).  - Visualizing Data Structure By projecting high-dimensional data into lower-dimensional spaces (such as 2D or 3D), patterns and correlations hidden within the data become visually easier to understand. - Feature Extraction and Noise Reduction By focusing on the major f...

 In the field of data analysis, a major theme is how to group vast amounts of data and extract useful patterns. Among various methods, the k-means method is a simple yet powerful clustering technique widely used in many fields.  This article will explain what the k-means method is, where it is used, and the benefits of learning this technique. 1. What is the K-Means Method? The k-means method is one type of clustering technique, an algorithm for dividing data with similar properties into “clusters” (groups). It divides individual data points within a dataset into a predetermined “k” number of clusters, aiming to increase the similarity of data within the same cluster and clarify the differences between different clusters. - Simple and Intuitive: The algorithm is very easy to understand and implement, making it accessible to both beginners and professionals. - High Computational Efficiency: It operates relatively quickly even with large datasets, making it practical in various ...

 In the world of machine learning, there are many approaches to prediction tasks.  Among these, Support Vector Regression (SVR) is a powerful tool that applies the concepts of Support Vector Machines (SVM) to regression problems, allowing it to flexibly capture non-linear relationships. This article explains the basic concepts of Support Vector Regression, the situations in which it is used, and the benefits of learning SVR. 1. What is Support Vector Regression? Support Vector Regression is a regression technique that uses the SVM framework to predict continuous values. - Basic Idea First, deriving from the mechanism by which SVM finds the “optimal hyperplane” in binary classification problems, SVR introduces a certain tolerance (ε) to prediction errors. It constructs a regression function that keeps data points within this range. By applying a penalty to data points outside the range, the model becomes a “smoother” function, avoiding excessive complexity and overfitting. - Ut...

 In today’s data science landscape, algorithms capable of effectively handling non-linear relationships and complex interactions are in high demand. Among these, Random Forest Regression stands out as a flexible and powerful technique, achieving high prediction accuracy by combining numerous decision tree regression models. This article explains the basic concepts of Random Forest Regression, the scenarios where its strengths are best utilised, and the benefits of learning this technique. 1. What is Random Forest Regression? Random Forest Regression is a regression technique that integrates multiple decision tree regression models in the form of “ensemble learning.” – Basic Principles Each decision tree is constructed using bootstrap samples (resampling of the data) from the training data. Furthermore, the features used for splitting at each node are randomly selected, reducing the correlation between the trees. During prediction, the final predicted value is obtained by averaging ...

 In today’s data analysis landscape, while increasingly complex modeling techniques emerge, k-Nearest Neighbor Regression (k-NN Regression) remains popular due to its simplicity and intuitive understandability. This article explains the fundamental concepts of k-NN Regression, its practical applications, and the benefits of learning this technique. 1. What is k-Nearest Neighbor Regression? k-NN Regression is a non-parametric technique that operates as a “lazy learner,” meaning it doesn’t estimate model parameters during the learning phase. - Prediction Process When presented with a new data point, the algorithm identifies the ‘k’ closest samples from the training data. The predicted value is then calculated as the average (or weighted average) of the target variable (numerical data) of these identified samples. - Distance Measurement Euclidean distance is commonly used to calculate distances, but other distance metrics (e.g., Manhattan distance) can be employed depending on the cha...

 In modern machine learning, there’s growing interest in techniques that combine weak classifiers – classifiers with limited individual power – to create surprisingly accurate predictive models. Among these, “AdaBoost” stands out as a simple yet highly effective ensemble learning method, utilized in numerous applications. This article explains the basic concepts of AdaBoost, the fields where it's actually used, and the benefits of learning this technique. 1. What is AdaBoost? AdaBoost is a technique that combines multiple weak learners to create a strong, final classifier. It begins by creating a simple classifier. Based on its results, the weights of misclassified samples are increased, and the next learner focuses on these difficult samples. This process is repeated, significantly reducing overall error. Each iteration adjusts the importance of samples based on the results of the previous learner, hence the name "Adaptive". This allows even very simple weak learners to ...

 In today's world of machine learning, there are diverse algorithms for making decisions and predictions based on data. Among these, the Support Vector Machine (SVM) is a widely supported method due to its simple concept and powerful classification performance. This article provides a clear explanation of what SVM is, how it is used, and the benefits of learning it, starting from scratch. 1. What is a Support Vector Machine? The Support Vector Machine is a method for separating different classes by finding a boundary (or hyperplane) based on given data.  In its most basic form, SVM assumes that data is linearly separable and seeks the optimal straight line (or hyperplane) to divide two classes. This hyperplane is designed to maximize the margin (clearance) between classes, minimizing the risk of misclassification. The data points closest to the boundary are called “support vectors,” and these points play a crucial role in determining the position of the optimal hyperplane. Bec...

 In modern data analysis and machine learning, various algorithms are emerging, among which decision tree regression stands out for its simplicity and intuitive splitting process, making it a flexible method applicable to a wide range of problems. Here, we will explain in detail what decision tree regression is, the situations in which it is used, and the benefits of learning this technique. 1. What is Decision Tree Regression? Decision tree regression is a technique that applies the mechanism of decision trees to problems aiming to predict continuous values. Decision tree regression divides given data based on multiple conditions, ultimately calculating continuous predicted values in each subdivided region (leaf node). Each split is determined according to a criterion that minimizes data variance (e.g., minimizing variance), resulting in increasingly homogeneous data groups with each split. Decision tree regression excels in its ability to capture non-linear relationships and natu...

 In today’s world of data science, tools are needed to make highly accurate predictions and classifications from vast amounts of data. Among these, “Random Forest” stands out as a simple yet powerful algorithm attracting attention in many fields.  This article explains the basic concepts of Random Forest, specific use cases, and the benefits of learning this technology. 1. What is a Random Forest? A Random Forest is an ensemble learning method that generates multiple decision trees and integrates their prediction results to achieve high accuracy. As the name suggests, a Random Forest constructs a “forest” of many decision trees. Each decision tree is created from a random sample of the original data (bootstrap sample), and the features used at each branching point are also randomly selected. This reduces the variance and risk of overfitting of individual trees, resulting in a very robust model overall. In classification problems, a majority vote (the most frequent class output...

 In modern data analysis, reliable statistical estimation is becoming increasingly important. Amidst this trend, the bootstrap method is gaining attention as an innovative technique for producing highly reliable estimation results while minimizing theoretical assumptions. This article provides a detailed explanation of the fundamentals of the bootstrap method, its specific applications, and the benefits of learning this technique. 1. What is the Bootstrap Method? The bootstrap method is a non-parametric technique used to estimate the distribution of statistics, confidence intervals, and errors by performing “resampling” from the original sample data. By repeatedly performing replacement sampling from the original data, statistics such as the mean and variance are calculated for each sample. This allows for a practical evaluation of the shape of the distribution and the variability of the statistics. Proposed by Bradley Efron in 1979, this technique is attractive because it doesn't ...

 In today's data-driven era, tools to support complex decision-making are constantly emerging. Among these, “Decision Trees” are a popular method due to their ease of understanding and intuitive visualization. Here, we will explain the basic concepts of decision trees, specific scenarios where they are used, and the benefits of learning them. 1. What are Decision Trees? Decision Trees are a type of model used for data classification and prediction. They use a tree structure to represent the decision-making process. Decision Trees consist of nodes (decision nodes) and edges (branches). Each node involves a conditional judgment based on a certain feature, and the branches diverge based on that result. Ultimately, the classification result or predicted value is indicated at the terminal parts called leaf nodes. The learning algorithm for decision trees aims to accurately capture the characteristics of the data by determining each branching point to minimise information impurity. This ...

Modern society is awash with data.  Extracting meaningful information from this data is extremely important for businesses and researchers alike. In particular, “multiple regression analysis” is gaining attention as a powerful tool for understanding phenomena involving multiple interacting factors and predicting the future. This article provides a detailed explanation of the basic concepts of multiple regression analysis, the situations in which it is used, and the benefits of learning it. 1. What is Multiple Regression Analysis? Multiple regression analysis is a statistical method that reveals the relationship between a single dependent variable (target variable) and multiple explanatory variables (independent variables). This method allows you to quantitatively assess how each explanatory variable influences the dependent variable. For example, when predicting the sales of a product, you can simultaneously consider multiple factors such as advertising expenditure, price, seasonal...