Does one thing predict another thing?
Linear regression is a basic form of multiple regression analysis. Unlike multiple regression, which handles multiple variables influencing one outcome, linear regression focuses on situations where there’s just one independent variable.
For instance, if we want to understand how specific factors like the number of vendors considered or the purchase cost impact the duration of buying teams’ purchasing processes, we would use linear regression. By entering one variable at a time into the model, we gain insights into the unique contribution of each factor to the overall variation in the length of the buying cycle.
Moreover, linear regression allows us to assess the strength and direction of the relationship between variables. This understanding is crucial for making informed decisions and predictions based on the available data. While linear regression may not handle multiple variables simultaneously like multiple regression does, its simplicity makes it a powerful tool for understanding and predicting outcomes based on individual factors.
