確率・統計学

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

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.

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.

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.

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