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.
Learn what the False Positive Rate (FPR) is, how it impacts machine learning models, and when to use it for better evaluation.
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.
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 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.
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 platforms like Apache Flink are revolutionizing epidemiological surveillance by providing timely, accurate insights that enable rapid response to disease outbreaks and public health threats.
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.
The ARIMAX model extends ARIMA by integrating exogenous variables into time series forecasting, offering more accurate predictions for complex systems.
A detailed look at hypothesis testing, the misconceptions around the null hypothesis, and the diverse methods for detecting data deviations.