確率・統計学

確率・統計学

About events, probabilities and random variables.

In this article, we will discuss events, probabilities, and random variables. It also describes conditional probabilities, Bayes' theorem, and expected values and variances.
確率・統計学

Derivation of Spearman’s rank correlation coefficient and example calculation using python

Correlation coefficients are often used as a method of summarizing relationships between data. There are different types of correlation coefficients, and one is called a rank correlation coefficient, which is used in cases where only the order of the data is known. In this article, I will focus on Spearman's rank correlation coefficient in particular, and describe its derivation and examples of calculations using python.
確率・統計学

【Multivariate Data】 Scatter Plots and Correlation Coefficients

In this article, I will discuss scatter plots and scatter plot matrices as a basic way to handle multivariate data, and correlation coefficients, rank correlation coefficients, and variance-covariance matrices as a method of summarization.
確率・統計学

How To Handle Univariate Data Histograms and Box Plots

This article describes how to create summary statistics such as mean and variance, histograms and box-and-whisker plots to visually capture characteristics of univariate data. The program used is described in python, which you can try in Google Colab below.
確率・統計学

Transformation Of Random Variables

Consider the transformation of a random variable X into Y = g(X) to derive the form of the transformed probability density function.