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The Relationship Between the Dependent Variable and the Independent Variables in Linear Regression


Linear regression is a popular machine learning technique used in healthcare data analysis to identify the relationship between a dependent variable and one or more independent variables. The concepts of dependent and independent variables are crucial for building accurate and interpretable linear regression models.


The objective of this article is to explain the concepts of dependent and independent variables in linear regression analysis, using a healthcare data example to demonstrate their application.

Dependent and Independent Variables:

In linear regression analysis, a dependent variable is the outcome variable that is predicted or explained by the model. In healthcare data analysis, the dependent variable can be a health outcome, such as mortality or hospital readmission, or a clinical measurement, such as blood pressure or glucose levels. The dependent variable is usually denoted by Y in linear regression models.

On the other hand, an independent variable is a predictor variable that is used to explain the variation in the dependent variable. In healthcare data analysis, independent variables can include demographic information, medical history, clinical measurements, and environmental factors. Independent variables are usually denoted by X in linear regression models.

Linear regression models aim to estimate the relationship between the dependent variable and the independent variables. The model can be represented by the equation:

Y = β0 + β1X1 + β2X2 + ... + βp*Xp + ε

where X1, X2, ..., Xp are the independent variables, β0 is the intercept, β1, β2, ..., βp are the regression coefficients, and ε is the error term. The regression coefficients represent the change in the dependent variable associated with a one-unit change in the corresponding independent variable.


Let's consider an example of using linear regression analysis in healthcare data analysis. Suppose we have a dataset of patients with heart disease, including demographic information, medical history, clinical measurements, and medication data. Our goal is to develop a linear regression model that can predict the risk of mortality for patients based on their demographic and clinical characteristics.

We start by exploring the data and identifying the dependent variable (mortality risk) and the independent variables (age, gender, BMI, blood pressure, medication use, etc.). We then split the dataset into a training set and a validation set, using the training set to build the linear regression model and the validation set to evaluate its performance.

We first apply simple linear regression to predict mortality risk based on a single independent variable, such as age. We estimate the regression coefficient and evaluate the model's performance on the validation set using metrics such as mean squared error or R-squared. We repeat this process for other independent variables and select the one that produces the best performance.

We then apply multiple linear regression to predict mortality risk based on multiple independent variables. We estimate the regression coefficients and evaluate the model's performance on the validation set. We may also perform feature selection to identify the most informative independent variables and improve the model's performance.

We can also use linear regression models to estimate the effect of an intervention on the dependent variable. For example, we can use linear regression to evaluate the effect of a new medication on blood pressure levels or the effect of a lifestyle intervention on mortality risk.


Dependent and independent variables are essential concepts in linear regression analysis and are used to build accurate and interpretable linear regression models in healthcare data analysis. By understanding the relationship between the dependent variable and the independent variables, we can identify risk factors, predict health outcomes, and evaluate the effect of interventions. Linear regression analysis is a powerful tool that can help healthcare professionals to improve patient care and develop new interventions.


Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer.

Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2), 301-320.

Peck, K., & Wears, R. L. (2013). Introduction to the US healthcare system. John Wiley & Sons.

Ghassemi, M., Naumann, T., Schulam, P., Beam, A. L., Chen, I. Y., Ranganath, R., & Ossorio, P. N. (2020). A review of challenges and opportunities in machine learning for health. CoRR, abs/2006.16236.

Sauerbrei, W., & Schumacher, M. (2018). A tutorial on building clinical prediction models: when and how?. Statistics in Medicine, 37(22), 3501-3513

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