ARIMAX Time Series: Comprehensive Guide
The ARIMAX model extends ARIMA by integrating exogenous variables into time series forecasting, offering more accurate predictions for complex systems.
Recent posts
Understanding Statistical Testing: The Null Hypothesis and Beyond
A detailed look at hypothesis testing, the misconceptions around the null hypothesis, and the diverse methods for detecting data deviations.
ANOVA vs Kruskal-Wallis: Understanding the Differences and Applications
Learn the key differences between ANOVA and Kruskal-Wallis tests, and understand when to use each method based on your data’s assumptions and characteristics.
Cox Proportional Hazards Model: A Guide to Survival Analysis in Medical Studies
The Cox Proportional Hazards Model is a vital tool for analyzing time-to-event data in medical studies. Learn how it works and its applications in survival analysis.
Don’t Get MAD About Shapiro-Wilk: Real Issues in Residual Diagnostics and Model Fitting
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.
Rethinking Statistical Test Selection: Why the Diagrams Are Failing Us
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: Pioneer of Computer Science and Programming Languages
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.
Applications of Time Series Analysis in Epidemiological Research
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.
Log-Rank Test: Comparing Survival Curves in Clinical Studies
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.
Critical Considerations Before Using the Box-Cox Transformation for Hypothesis Testing
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.