There are different types of regression depending on one’s research objectives and variable format, with linear regression being one of the most frequently used.
Linear regression analyzes continuous outcomes (i.e., those that can be meaningfully added, subtracted, multiplied, and divided, like weight) and assumes that the relationship between the outcome and independent variables follows a straight line (e.g., as calories consumed increases, weight gain increases).
To assess the effect of a single independent variable on a continuous outcome (e.g., the contribution of calories consumed to weight gain), one would conduct simple linear regression.
However, it is usually more desirable to determine the influence of multiple factors at the same time (e.g., the contribution of number of calories consumed, days exercised per week, and age to weight gain), since one can then see the unique contributions of each variable after controlling for the effects of the others. In this case, multivariate linear regression is the proper choice.
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Stoltzfus, J.C. (2011), Logistic Regression: A Brief Primer. Academic Emergency Medicine, 18: 1099-1104.