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 Concepts Accessible 6: Introduction to Multiple Linear Regression – Advancing Beyond the Simple Linear Model

here.

Although the interpretation and emphasis differ between these goals, the methodology for fitting the model is essentially the same.

This article offers a step-by-step guide to operationalizing Multiple Linear Regression (MLR), covering data preparation, assumption checks, and model estimation using commonly used software like R, Python, and SAS.

While the focus is not on extensive programming, practical examples will be shared in our weekly newsletter, Learning Stat by Example . Subscribe

here.

Each of these steps will be detailed in future editions; the goal here is to provide an overview.

Step 1: Data Preparation

Data Collection

The starting point is to define your analysis goal clearly. Ensure data quality and relevance, collecting all necessary predictors and clearly identifying your dependent variable. If no predefined list of predictors exists, methods like stepwise regression (forward or backward selection) can help identify relevant variables. However, this topic falls under model evaluation and will be addressed in the upcoming edition Making Statistical Concepts Accessible – Edition 9.

Most analyses begin with data in a format such as CSV or Excel. Selecting software that you are familiar with, such as R, Python, SAS, or SPSS, is critical for efficient analysis.

Subscribe to our weekly newsletter, Learning Stat by Example, where we provide practical examples with step-by-step software implementation, requiring no prior programming experience.

Now, let’s continue with the data preparation step.

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