We have examined multiple analytic models throughout this course. Compare two models that you found most beneficial and explain why.

The two models that I find useful are casual and time series.

The casual model is more detailed in the number of variables evaluated for the outcome of the forecast. This approach is useful if many components are to be used for the study to produce a detailed forecast. The model time series uses time to determine the result of the forecast. To determine the outcome of the prediction, the time series approach uses historical data for a certain era. This is beneficial in a smaller, simpler situation.

The casual trend will help to predict a bonus scheme forecast in which several variables contribute to the final distributed benefit. The model time series are useful for calculating the level of circulation in a retail stored store daily only and without any other elements such as seasonal elements or other detail. The time points, but also different considerations than time, should be included in casual models. Casual models also focus on quantitative expertise. Winter clothing sales, for instance, rely upon season-related factors, such as weather, product quality, discount, etc.

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I like your discussion of the causal and time series models and how they are beneficial in many ways. Great examples of their use. I also like using a time series as it gives us a good comparison. Did you find the simulations useful? Why or why not?