The decision between simulation models and linear regression in terms of effectiveness is based on the type of issue that needs to be resolved. When the dependent and independent variables have a distinct relationship to one another and the model's presumptions are true, linear regression might be helpful. When an issue is more complicated and the interactions conventional statistical techniques, simulation models are a better option. For instance, assuming the data is normally distributed, and that there is a linear relationship between house size and price, a linear regression model would the best option if we were to forecast the price of a house based on its size and location. However, a simulation model would be more appropriate if we wanted to model the spread of a disease in a population because there are numerous variables that can affect the spread of the disease and it is challenging to model these interactions using conventional statistical methods