Category: Data Science ENG

Evaluating the shape of a distribution in statistics is crucial for selecting appropriate models, ensuring the validity of inferences, and identifying anomalous behavior. With measures such as skewness and kurtosis, the skewness, tail and concentration of the distribution are evaluated. This analysis guides the choice of descriptive statistics, regression models and hypothesis tests, ensuring correct interpretation of the data. Understanding the shape of the distribution is essential for preparing data, comparing groups, and making reliable predictions.

Regression analysis is a statistical technique that is used to explore the relationship between a dependent variable and one or more independent variables. While classic regression analysis focuses on the …

Statistical regression is a powerful tool in the data analyst’s arsenal, allowing you to explore relationships between variables and make predictions based on these relationships. In this article, we will delve deeper into regression, exploring its fundamental concepts, different types and practical applications.

Marginal probability is a probability measure that is obtained by adding (or integrating, in the case of continuous variables) the joint probability over one or more events. In other words, it involves obtaining the probability of an individual event while ignoring information about the other events involved. This operation can be performed on both discrete variables and continuous variables.

Descriptive Statistics is an essential branch of statistics that focuses on summarizing and organizing data in order to provide a clear and concise understanding of their fundamental characteristics. While Inferential Statistics seeks to make statements about the population based on a sample, Descriptive Statistics is concerned with examining and communicating the intrinsic characteristics of the data itself.

Regularization is a technique used in regression analysis to prevent overfitting and improve the generalization ability of the model. Overfitting occurs when the model overfits the training data, also capturing the noise in the data rather than just the underlying patterns. This can lead to a poor generalization ability of the model on new data

Hypothesis Testing is a fundamental tool in inferential statistics that allows you to make informed decisions based on the data collected. This methodology is widely used in various fields, from scientific research to economics, from medicine to engineering

Statistics is a fundamental discipline for the analysis and interpretation of data. One of the most powerful conceptual tools in statistics is the Central Limit Theorem (CLT). This theorem is crucial to inferential statistics and provides the basis for many statistical analyzes applied in a wide range of fields.

In this article, we will explore together the differences between descriptive statistics and inferential statistics, highlighting their specific roles in statistical analysis and illustrating how they can be used in a complementary way to obtain a complete understanding of the data.