スキップしてメイン コンテンツに移動

Understanding Descriptive Statistics from Scratch

 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 or variability of the data, allowing you to assess its distribution.


- Shape Characteristics


Statistical measures like skewness and kurtosis, which indicate the asymmetry and peakedness of the data distribution, are also important for clarifying the data's properties.


- Visualization


Using graphs such as histograms, box plots, and scatter plots to visually represent numerical data, revealing patterns, outliers, and clusters that may not be apparent from the numbers alone.


Descriptive statistics can be considered “summary mathematics” for intuitively understanding the overall picture of data. Taking this fundamental step prepares you for more advanced analysis and prediction.


2. In What Situations are They Used?


Descriptive statistics play an important foundational role in data analysis across all fields. Here are some specific examples.


- Business & Marketing


Summarizing vast amounts of information like sales data, customer purchase history, and survey results with descriptive statistics allows you to understand market trends and customer characteristics, providing a basis for strategic planning. Visualizations through dashboards are also effective for reporting to management.


- Healthcare & Medical Field


Organizing patient data and test results using descriptive statistics allows for quantitative evaluation of health status, disease trends, and treatment effectiveness, supporting diagnosis and treatment plan development. They are also used to monitor the occurrence of infectious diseases in the field of public health.


- Public Policy & Administration


Organizing and visualizing statistical information such as population statistics, unemployment rates, and crime rates by region allows for policy evaluation and the development of new initiatives, and helps to communicate information clearly to residents.


- Education & Research


Organizing diverse survey data such as academic test results, experimental data, and surveys with descriptive statistics reveals basic trends and correlations, leading to academic research and policy recommendations.


As these examples show, descriptive statistics are a fundamental tool for understanding the “first impression” of data in each field, serving as the foundation for decision-making, strategic planning, and research.


3. What are the Benefits of Learning Them?


Learning descriptive statistics is not only a first step in data analysis but also provides significant benefits, directly applicable to a variety of fields.


- Grasping the Overall Picture of Data


Your ability to summarize and organize vast amounts of information improves, allowing you to acquire the ability to extract the essence from data. This allows you to quickly identify the core of a problem or emerging trends.


- Improving Decision-Making Accuracy


By presenting accurate information through numbers and graphs, you can make decisions based on quantitative evidence rather than speculation or intuition.


- Improving Communication Skills


By mastering techniques for visualizing and explaining complex data, you can deliver persuasive presentations based on data in the workplace, at academic conferences, and in business settings.


- From Fundamentals to Application


Descriptive statistics are a stepping stone to more advanced analytical techniques such as inferential statistics, machine learning, and big data analysis. A solid foundation will make it easier to learn these subsequent methods.


- High Versatility


It is a skill required in all fields – business, science, healthcare, and the public sector – and being proficient in descriptive statistics itself is a significant asset for career advancement.


In Conclusion


Descriptive statistics are a crucial technique as the first step in problem-solving and decision-making, extracting the essence of data.


By learning descriptive statistics, you can steadily develop the ability to unravel the “story” within data, becoming an even more valuable asset in today’s information society. We recommend starting by actually handling a dataset, calculating basic statistics, and creating graphs.


If you want to learn descriptive statistics, we recommend this book (access here).

コメント

コメントを投稿

このブログの人気の投稿

Understanding Probability and Probability Distributions from Scratch

 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...
コメントを投稿
続きを読む

Entendiendo la Regresión de Bosques Aleatorios desde Cero

En el panorama actual de la ciencia de datos, los algoritmos capaces de manejar eficazmente relaciones no lineales e interacciones complejas están muy demandados. Entre estos, la Regresión de Bosques Aleatorios destaca como una técnica flexible y potente, logrando una alta precisión predictiva al combinar numerosos modelos de regresión de árboles de decisión. Este artículo explica los conceptos básicos de la Regresión de Bosques Aleatorios, los escenarios donde sus fortalezas se utilizan mejor y los beneficios de aprender esta técnica. 1. ¿Qué es la Regresión de Bosques Aleatorios? La Regresión de Bosques Aleatorios es una técnica de regresión que integra múltiples modelos de regresión de árboles de decisión en forma de “aprendizaje conjunto” (ensemble learning). – Principios Básicos Cada árbol de decisión se construye utilizando muestras bootstrap (remuestreo de los datos) del conjunto de entrenamiento. Además, las características utilizadas para la división en cada nodo se selecciona...
コメントを投稿
続きを読む

Understanding Differential Equations Solved with Variation of Parameters

1. What are Differential Equations Solved with Variation of Parameters? Differential equations are a powerful tool for mathematically capturing changing phenomena. Among these, the “method of variation of parameters” is a particularly useful technique for solving non-homogeneous linear differential equations. The general solution to a homogeneous differential equation is known, expressed by a combination of constants (constant coefficients).  However, this cannot be directly solved when a non-homogeneous term (corresponding to an external influence or input) is added. Therefore, the method of variation of parameters takes an approach of replacing the original constant parts with (unknown) functions and determining the shape of those functions through differentiation. This method allows the construction of a complete solution including the non-homogeneous term.  Due to its flexibility in handling various systems – such as when the non-homogeneous term is an exponential function...
コメントを投稿
続きを読む