The Beta (ß) Coefficient in Multivariate Linear Regression

Modified on Wed, 01 Feb 2023 at 10:30 PM

What is the beta coefficient?

In a multivariate linear regression model, the beta coefficient represents the estimated change in the dependent variable (y) for a one-unit change in a predictor variable (x), while holding all other predictors constant. It represents the strength of the relationship between each predictor variable and the dependent variable and the direction of the relationship (positive or negative).


Let's say we are conducting a medical study to examine the relationship between age and systolic blood pressure (SBP). We collect data on age and SBP for a sample of individuals and fit a simple linear regression model with age as the predictor variable and SBP as the dependent variable. The beta coefficient for age in the regression model represents the average change in SBP for a one-year increase in age, while holding all other variables constant.


If the beta coefficient for age is 2, this means that for each year increase in age, SBP increases by an average of 2 units, all else being equal. This can be interpreted as a positive relationship between age and SBP: older individuals tend to have higher SBP. The magnitude of the beta coefficient indicates the strength of the relationship, so a larger beta coefficient would suggest a stronger association between age and SBP.


Beware of the units!


The interpretation of beta coefficients in a linear regression model is dependent on the units of the predictor variables. For example, if the predictor variable is age and the beta coefficient is 2, this means that for a one-year increase in age, the dependent variable is expected to change by 2 units, on average. If age is measured in months, then the interpretation would be that for a one-month increase in age, the dependent variable is expected to change by a fraction of 2 units (i.e., 2/12 = 0.17). It's important to keep in mind the units of measurement of the predictor variables when interpreting the beta coefficients in a linear regression model.


Keep a critical eye


Here are some important things to keep in mind when interpreting beta coefficients in a linear regression model:


  1. The beta coefficients represent the expected change in the dependent variable for a one-unit change in a predictor variable, holding all other predictors constant. It's important to keep in mind that this is just an estimate and that in the real world, other variables may also be affecting the outcome.

  2. The magnitude of the beta coefficients can be interpreted as the strength of the relationship between the predictor variable and the dependent variable. However, it is important to keep in mind that the magnitude of a beta coefficient does not provide information on the importance of the predictor variable in explaining the variation in the dependent variable.

  3. The direction of the beta coefficients (positive or negative) indicates the direction of the relationship between the predictor variable and the dependent variable. A positive beta coefficient means that an increase in the predictor variable is associated with an increase in the dependent variable, while a negative beta coefficient means that an increase in the predictor variable is associated with a decrease in the dependent variable.

  4. The beta coefficients are affected by the units of measurement of the predictor variables, as discussed in the previous answer.

  5. The beta coefficients are only meaningful in the context of the model as a whole and in relation to the other predictor variables in the model. It's important to examine the full model, including the coefficients for all the predictors, to get a comprehensive understanding of the relationship between the predictor variables and the dependent variable.

  6. The beta coefficients should not be used to make causal inferences without additional evidence, such as from experimental or observational studies that control for confounding variables.

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