## Statsmodels – the Python library for statistics

Statsmodels is an open-source library that offers a wide range of tools for estimating statistical models, running statistical tests, and visualizing data.

## The Sign Test, a nonparametric method with Python

The Sign Test is a nonparametric method used to compare two related samples or to test whether the median of a population differs from a specific value. It is especially useful when the data does not meet the assumptions necessary for parametric tests, such as normality of the distribution.

## Calculating Measures of Dispersion in Statistics with Python

Measures of dispersion in statistics provide an indication of the variability or spread of data within a set. In other words, they show how much the data deviates from the mean or central value. These measures are critical because they provide valuable information about the distribution and consistency of data, allowing analysts to better understand the nature and characteristics of a data set.

## Non-Parametric Statistics

Non-parametric statistics is a branch of statistics that focuses on the analysis of data without making rigid assumptions about their distribution.

## Calculating centrality measures with Python: Mean, Median and Mode

Centrality measures, such as mean, median, and mode, are fundamental in descriptive statistics.

## The Cumulative Distribution Function (CDF) in Python

The Cumulative Distribution Function (CDF) is a mathematical function that provides the probability that a random variable is less than or equal to a certain value. In other words, the CDF provides an overview of the probability distribution of a random variable. In Python, you can use CDF through libraries like NumPy, SciPy or Statmodels. These libraries provide methods to calculate the CDF for different probability distributions, such as normal distribution, binomial distribution, Poisson distribution, etc.

## Joint probability and Union probability

Joint Probability and Union Probability are fundamental concepts in probability theory, and represent different ways of describing relationships between events.

## Sampling Methods in Python

Sampling is a fundamental process in research and statistics, allowing meaningful conclusions to be drawn from a representative subset of a larger population. In this article, we will review the concept of sampling and the main methods used to select representative samples. Through practical examples in Python code and theoretical considerations, we will illustrate the importance of careful sample selection and the applications of different sampling methods.

## Mutually exclusive events in statistics with Python

In this article we will give a quick overview of the definition of mutually exclusive events, using some examples that can best elucidate these concepts, such as the launch d. In Python, as in other programming languages, it is easy to make simple programs useful for checking the exclusivity of events.

## Longitudinal data in statistics and study techniques with Python

Longitudinal data in statistics refers to observations collected on the same study unit (for example, an individual, a family, a company) repeatedly over time. In other words, instead of collecting data from different study units at one point in time, you follow the same units over time to analyze the variations and changes that occur within each unit. In this article we will discover what they are and which study techniques to apply using Python as an analysis tool.