Cochran’s Q Test: Comparing Three or More Related Proportions
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.
Recent posts
A Comprehensive Guide to ARIMA Time Series Modeling
Learn the fundamentals of ARIMA modeling for time series analysis. This guide covers the AR, I, and MA components, model identification, validation, and its comparison with other models.
Ordinary Least Squares (OLS) Regression: Properties and Applications
Discover the foundations of Ordinary Least Squares (OLS) regression, its key properties such as consistency, efficiency, and maximum likelihood estimation, and its applications in linear modeling.
Analysis of the False Positive Rate (FPR) in Machine Learning
Learn what the False Positive Rate (FPR) is, how it impacts machine learning models, and when to use it for better evaluation.
Shapiro-Wilk Test vs. Anderson-Darling Test: Checking Normality in Data
Learn about the Shapiro-Wilk and Anderson-Darling tests for normality, their differences, and how they guide decisions between parametric and non-parametric statistical methods.
Understanding Prediction Error: Bias, Variance, and Model Evaluation Techniques
Learn about different methods for estimating prediction error, addressing the bias-variance tradeoff, and how cross-validation, bootstrap methods, and Efron & Tibshirani’s .632 estimator help improve model evaluation.
The Friedman Test: Non-Parametric Alternative to Repeated Measures ANOVA
The Friedman test is a non-parametric alternative to repeated measures ANOVA, designed for use with ordinal data or non-normal distributions. Learn how and when to use it in your analyses.
Sustainability Analytics: How Data Science Drives Green Innovation
Data science is a key driver of sustainability, offering insights that help optimize resources, reduce waste, and improve the energy efficiency of supply chains.
Real-Time Data Processing and Epidemiological Surveillance
Real-time data processing platforms like Apache Flink are revolutionizing epidemiological surveillance by providing timely, accurate insights that enable rapid response to disease outbreaks and public health threats.
Understanding Type I and Type II Errors in Hypothesis Testing
Explore Type I and Type II errors in hypothesis testing. Learn how to balance error rates, interpret significance levels, and understand the implications of statistical errors in real-world scenarios.