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Aug 23,2025
Making Statistical Concepts Accessible – Edition 5: Exploring Correlation and Regression Analysis in Healthcare
In the dynamic world of healthcare research and analysis, correlation and regression analyses stand out as vital tools, offering profound insights into the intricate relationships between variables. While both methods shed light on how changes…
Aug 23,2025
Making Statistical Concepts Accessible – Edition 6: Introduction to Multiple Linear Regression – Advancing Beyond the Simple Linear Model
To grasp the concept of multiple linear regression, it’s essential to first understand the simple linear model, which we covered in our third edition of the “Making Statistical Concepts Accessible” series. You can review it…
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 –…
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…
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…
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…
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…
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.…
Aug 23,2025
Making Statistical Concepts Accessible – Edition 13: Logistic Regression in R, Interpreting the Output
When conducting analysis with a binary outcome, it is essential to align the interpretation of the results with the goal of the analysis, whether it’s inference or prediction. Unlike linear regression, the output of logistic…
Aug 23,2025
Making Statistical Concepts Accessible – Edition 14: Cloglog and Probit as Alternatives to Logit Link Function
Introduction In social sciences, health sciences, and fields like banking and insurance, logistic regression remains a popular method for prediction and inference, especially when the outcome variable is binary. Logistic regression, a specific case of…
