The goal of linear regression analysis is to understand the influence of independent variables on dependent variables. The result of linear regression analysis is the regression coefficient, which indicates the size and magnitude of the influence of independent variables on dependent variables.

Based on the analysis results, this regression coefficient can be either positive or negative, depending on the nature of the relationship between the variables. This article by Kanda Data is written to discuss how to correctly interpret a negative regression coefficient.

**The Meaning of the Regression Coefficient**

The regression coefficient is a number that shows the size and direction of the change that occurs in the dependent variable when the independent variable changes by one unit, assuming that other variables in the model remain constant. If the regression coefficient is positive, it indicates a positive relationship between the independent and dependent variables; that is, when the independent variable increases, the dependent variable also increases.

Conversely, if the regression coefficient is negative, it indicates a negative relationship. This can be interpreted as the observed variables having an opposite relationship. When the independent variable increases, the dependent variable decreases. Likewise, when the independent variable decreases, the dependent variable will increase.

**Is a Negative Regression Coefficient Acceptable?**

A negative regression estimation coefficient is common and acceptable in linear regression analysis. This negative value indicates an inverse relationship between the independent and dependent variables.

In some contexts, this negative relationship is logical and aligns with theories or other phenomena. For example, in economics, an increase in interest rates is often associated with a decrease in investment, which is an example of a negative regression coefficient.

**How to Interpret a Negative Regression Coefficient**

To interpret a negative regression coefficient, it is important for researchers to understand the context of the variables being analyzed. A negative coefficient indicates that each unit increase in the independent variable will cause a decrease in the dependent variable by the magnitude of the coefficient.

For example, if the regression coefficient is -0.5, it can be interpreted that an increase of one unit in the independent variable will cause a decrease of 0.5 units in the dependent variable. This interpretation can be adjusted to the magnitude of the coefficient and the unit of measurement of the variable.

**What Researchers Should Do When Encountering a Negative Regression Coefficient**

When encountering a negative regression coefficient, researchers need to ensure that this negative relationship is theoretically and empirically reasonable. Additionally, it is important for researchers to evaluate whether the model used is appropriate and whether there are other variables that may not have been included in the model that could influence the results.

Researchers should also pay attention to the results of statistical significance tests to ensure that the negative coefficient is significant. Furthermore, to ensure that the best linear unbiased estimator is obtained, researchers need to ensure that the required assumption tests have been met.

**Case Study Example**

For example, in agricultural economics research, a researcher wants to understand the effect of fertilizer prices on land productivity. From the regression analysis results, a negative regression coefficient of -0.3 was obtained.

This coefficient value can be interpreted as meaning that every increase in fertilizer price by one unit (e.g., 1 USD) will cause a decrease in land productivity by 0.3 units (e.g., the variable is measured in quintals per hectare).

Based on the analysis results, a logical explanation can be obtained that higher fertilizer prices may cause farmers to reduce fertilizer use, which in turn decreases land productivity.

**Conclusion**

A negative regression coefficient is common and acceptable in linear regression analysis. A negative regression coefficient indicates an inverse relationship between the independent and dependent variables.

It is important for researchers to carefully interpret this negative coefficient, ensuring that the relationship found is reasonable and statistically significant. That concludes this article from Kanda Data. Hopefully, it is useful and adds knowledge for all of us. Stay tuned for updates in future articles.