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.
Calculate Probability Mass Function (PMF) with Python
The Probability Mass Function (PMF) is a function that associates with each value of a discrete random variable the probability that the variable takes on that particular value. In other words, the PMF provides the probability distribution of a discrete random variable.
Tidyverse, an ideal tool for Descriptive Statistics with R
Descriptive statistics is a crucial step in data analysis, providing a detailed overview of the main characteristics of a dataset. R, with its vast ecosystem of packages, offers a powerful and coherent solution to address this phase. Among these, Tidyverse stands out, a set of packages designed to improve data manipulation, analysis and visualization in R.
Introduction to Statistics with R
Statistics is a discipline that deals with the collection, analysis and interpretation of data. Through the use of statistical methods, it is possible to extract meaningful information from data, draw …
From Uncertainty to Knowledge: Foundations of Bayesian Statistics
Bayesian statistics is an approach to statistics that relies on Bayes’ theorem to update the probabilities of hypotheses in light of new available data. Unlike the Frequentist approach, Bayesian statistics treats probabilities as expressions of knowledge or uncertainty rather than as frequencies of events.
Centrality Measurements of a Distribution with Python
Centrality measures, such as the mean, median, and mode, identify the typical value of a data set, providing a reference point for understanding the distribution. These measures work synergistically with measures of dispersion, such as standard deviation and IQR, to quantify the variability around the central value. Considering both of these aspects offers a comprehensive perspective of the distribution, essential for statistical modeling, informed decisions, and the accurate description of data.
Measurements of Dispersion of a Distribution in Python
Calculating measures of dispersion, such as standard deviation and IQR, is crucial for evaluating the variability of data around its central tendency. These measures provide critical information about the distribution, allowing you to identify outliers, compare distributions, and make informed decisions. Understanding variability is essential for process control, building accurate statistical models, and supporting predictions and decisions in different contexts.