Leveraging Data Science Techniques for Predictive Maintenance
Explore the role of data science in predictive maintenance, from forecasting equipment failure to optimizing maintenance schedules using techniques like regression and anomaly detection.
Recent posts
One-Way ANOVA vs. Two-Way ANOVA: When to Use Which
One-way and two-way ANOVA are essential tools for comparing means across groups, but each test serves different purposes. Learn when to use one-way versus two-way ANOVA and how to interpret their results.
Multiple Comparisons Problem: Bonferroni Correction and Other Solutions
The multiple comparisons problem arises in hypothesis testing when performing multiple tests increases the likelihood of false positives. Learn about the Bonferroni correction and other solutions to control error rates.
Kolmogorov-Smirnov Test: Assessing Goodness-of-Fit in Non-Parametric Data
The Kolmogorov-Smirnov test is a powerful tool for assessing goodness-of-fit in non-parametric data. Learn how it works, how it compares to the Shapiro-Wilk test, and explore real-world applications.
Maximum Likelihood Estimation (MLE): Statistical Modeling in Data Science
Discover the fundamentals of Maximum Likelihood Estimation (MLE), its role in data science, and how it impacts businesses through predictive analytics and risk modeling.
Causality Beyond Correlation: Simpson’s and Berkson’s Paradoxes
Understand how causal reasoning helps us move beyond correlation, resolving paradoxes and leading to more accurate insights from data analysis.
Mathematical Models of Inequality: Understanding Lorenz Curves and Gini Coefficients
This article delves into mathematical models of inequality, focusing on the Lorenz curve and Gini coefficient to measure and interpret economic disparities.
Machine Learning and Statistics: Bridging the Gap
Machine learning is often seen as a new frontier, but its roots lie firmly in traditional statistical methods. This article explores how statistical techniques underpin key machine learning algorithms, highlighting their interconnectedness.
A Deep Dive into Why Multiple Imputation is Indefensible
Let’s examine why multiple imputation, despite being popular, may not be as robust or interpretable as it’s often considered. Is there a better approach?
Evaluating Binary Classifiers on Imbalanced Datasets: Why AUC-PR Beats AUC-ROC and Gini
AUC-ROC and Gini are popular metrics for evaluating binary classifiers, but they can be misleading on imbalanced datasets. Discover why AUC-PR, with its focus on Precision and Recall, offers a better evaluation for handling rare events.