Differential Equations in Growth Models
Differential equations are essential in modeling economic growth, providing insight into long-term trends and the impact of policy changes on macroeconomic variables.
Recent posts
Bayesian State Space Models in Macroeconometrics
Explore the critical role of Bayesian state space models in macroeconometric analysis, with a focus on linear Gaussian models, dimension reduction, and non-linear or non-Gaussian extensions.
Understanding Statistical Significance in Data Analysis
Learn the essential concepts of statistical significance and how it applies to data analysis and business decision-making.
Predicting Hospital Readmissions for Elderly Patients Using Machine Learning
Machine learning models are revolutionizing post-hospitalization care by predicting hospital readmissions in elderly patients, helping healthcare providers optimize treatment and reduce complications.
Linear Optimization: Efficient Resource Allocation for Business Success
Learn how decision-makers in industries like logistics, finance, and manufacturing use linear optimization to allocate scarce resources effectively, maximizing profits and minimizing costs.
Chauvenet’s Criterion: A Statistical Approach to Detecting Outliers
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.
Exploring Kernel Density Estimation: A Powerful Tool for 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.
Chi-Square Test: Exploring Categorical Data and Goodness-of-Fit
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: A Robust Method for Detecting Outliers
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: A Guide for Detecting Outliers
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.