Category: Making Statistics Accessible
This category is dedicated to breaking down complex statistical ideas into clear, practical insights that anyone can understand and apply. Whether you are a researcher, business professional, student, or decision-maker, the goal is to make statistics approachable without sacrificing accuracy or depth.
Here you will find explanations of key statistical concepts, real-world case studies, and hands-on examples designed to bridge the gap between theory and practice. From foundational principles to advanced methods, every article focuses on clarity, transparency, and practical relevance, ensuring that statistical knowledge becomes an accessible tool for learning, problem-solving, and informed decision-making.

Aug 23,2025

Making Statistical Concepts Accessible – Edition 7: Assumptions and Coefficient Estimates in Multiple Linear Regression

In the previous edition of Making Statistical Concepts Accessible, we introduced the Multiple Linear Model, focusing on its goal and conceptual aspects. You can review it via the following link: Making Statistical Concepts Accessible –…

3 D Statistical Learning

Aug 23,2025

Making Statistical Concepts Accessible – Edition 8: Operationalizing Multiple Linear Regression – From Data to Model Parameter Estimation

Multiple linear regression (MLR) is a foundational statistical method used for both prediction and inference. For a deeper understanding of the structure, goals, and conception of multiple linear regression, refer to the article Making Statistical…

3 D Statistical Learning

Aug 23,2025

Making Statistical Concepts Accessible – Edition 9: Evaluating the Quality of Linear Regression Models

After fitting a linear regression model, whether for inference or prediction, it is crucial to assess the quality of the fit. Key questions include: Have we selected the best set of predictor variables? and Can…

3 D Statistical Learning

Aug 23,2025

Making Statistical Concepts Accessible – Edition 10: Interpreting the Results of Multiple Linear Regression from an R Output

Before performing a linear regression, certain preparatory steps are essential to ensure valid results. These include data preprocessing, checking assumptions, and defining the analysis objectives. For detailed guidance, refer to our article: Operationalizing Multiple Linear…

3 D Statistical Learning

Aug 23,2025

Making Statistical Concepts Accessible – Edition 11: An Introduction to Generalized Linear Models

In our previous editions, we referred to the variable of interest that we aim to explain or predict as the response, outcome, or dependent variable. In classical linear regression analysis, this dependent variable is typically…

3 D Statistical Learning

Aug 23,2025

Making Statistical Concepts Accessible – Edition 12: Understanding Logistic Regression

Logistic regression is a statistical method used when the response variable is binary. It is widely applied for both prediction and inference, making it a versatile tool across industries. Key Applications of Logistic Regression 1.…