Residual diagnostics often trigger debates, especially when tests like Shapiro-Wilk suggest non-normality. But should it be the final verdict on your model? Let’s dive deeper into residual analysis, focusing on its impact in GLS, mixed models, and robust alternatives.
Most diagrams for choosing statistical tests miss the bigger picture. Here’s a bold, practical approach that emphasizes interpretation over mechanistic rules, and cuts through statistical misconceptions like the N>30 rule.
Grace Hopper revolutionized computer science by developing the first compiler and contributing to COBOL. Discover her groundbreaking work and her legacy in the field of programming.
Time series analysis is a vital tool in epidemiology, allowing researchers to model the spread of diseases, detect outbreaks, and predict future trends in infection rates.
The Log-Rank test is a vital statistical method used to compare survival curves in clinical studies. This article explores its significance in medical research, including applications in clinical trials and epidemiology.
Before applying the Box-Cox transformation, it is crucial to consider its implications on model assumptions, interpretation, and hypothesis testing. This article explores 12 critical questions you should ask yourself before using the transformation.
This article delves into the Chi-Square test, a fundamental tool for analyzing categorical data, with a focus on its applications in goodness-of-fit and tests of independence.
Machine learning is transforming climate science, offering powerful predictive tools for forecasting extreme weather, rising sea levels, and biodiversity shifts.
Heteroscedasticity can affect regression models, leading to biased or inefficient estimates. Here’s how to detect it and what to do when it’s present.
Explore the role of data science in predictive maintenance, from forecasting equipment failure to optimizing maintenance schedules using techniques like regression and anomaly detection.
