Chauvenet’s Criterion is a statistical method used to determine whether a data point is an outlier. This article explains how the criterion works, its assumptions, and its application in real-world data analysis.
Kernel Density Estimation (KDE) is a non-parametric technique offering flexibility in modeling complex data distributions, aiding in visualization, density estimation, and model selection.
Dive into the Chi-Square Test, a statistical method for evaluating categorical data. Understand its applications in survey analysis, contingency tables, and genetics.
Peirce’s Criterion is a robust statistical method devised by Benjamin Peirce for detecting and eliminating outliers from data. This article explains how Peirce’s Criterion works, its assumptions, and its application.
Dixon’s Q test is a statistical method used to detect and reject outliers in small datasets, assuming normal distribution. This article explains its mechanics, assumptions, and application.
State Space Models (SSMs) offer a versatile framework for time series analysis, especially in dynamic systems. This article explores discretization, the Kalman filter, and Bayesian approaches, including their use in econometrics.
Explore how machine learning can be leveraged to forecast commodity prices, such as oil and gold, using advanced predictive models and economic indicators.
The integration of IoT and big data is revolutionizing elderly care by enabling remote monitoring systems that track vital signs, detect emergencies, and ensure quick responses to health risks.
Outliers, or extreme observations in datasets, can have a significant impact on statistical analysis. Learn how to detect, analyze, and manage outliers effectively to ensure robust data analysis.
The rich are getting richer while the poor remain poor. This article dives into the physics-based models that explain the inherent inequality in wealth distribution.