Consider some of the examples you have brought up in earlier discussion forums a

Consider some of the examples you have brought up in earlier discussion forums about applying models to real-world problems. Choose one of the models covered earlier in the course and describe the key differences in solving a problem with that model versus with a simulation model. In your opinion, which is more effective? How does the problem at hand determine which type of model to use?
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Nov 23, 2023, 8:44 AM
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Justin MitchellNov 23, 2023, 9:03 AM
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Good Morning,
In earlier discussions I have brought up different models for different real world problems.
To examine the relationship between a dependent variable and one or more independent variables, linear regression models are frequently utilized. They demand that the data be regularly distributed and presuppose a linear relationship between the dependent and independent variables. However, simulation models, which frequently have numerous variables and intricate interactions, are made to mimic the behavior of a system that actually exists in the real world. 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 models might be helpful. When an issue is more complicated and the interactions between variables are unclear or impossible to model using 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 be 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..
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