Learn how data science revolutionizes predictive maintenance through key techniques like regression, anomaly detection, and clustering to forecast machine failures and optimize maintenance schedules.
A comparison between machine learning models and univariate time series models for predicting emergency department visit volumes, focusing on predictive accuracy.
Leveraging customer behavior through predictive modeling, the BG/NBD model offers a more accurate approach to demand forecasting in the supply chain compared to traditional time-series models.
The log-rank test is a key tool in survival analysis, commonly used to compare survival curves between groups in medical research. Learn how it works and how to interpret its results.
This article explores the use of stationary distributions in time series models to define thresholds in zero-inflated data, improving classification accuracy.
This article delves into the fundamentals of Markov Chain Monte Carlo (MCMC), its applications, and its significance in solving complex, high-dimensional probability distributions.
A guide to solving DSGE models numerically, focusing on perturbation techniques and finite difference methods used in economic modeling.
Explore the different types of observational errors, their causes, and their impact on accuracy and precision in various fields, such as data science and engineering.
The Mann-Whitney U test and independent t-test are used for comparing two independent groups, but the choice between them depends on data distribution. Learn when to use each and explore real-world applications.
Understand Cochran’s Q test, a non-parametric test for comparing proportions across related groups, and its applications in binary data and its connection to McNemar’s test.