Summary

Congratulations, you truly deserve recognition for getting through a very tough topic. You now have more awareness about time series analysis than some people with formal statistical training. You learned first-hand why linear regression is inappropriate for data that violates the assumption of independence. For time series (dependent) data, there is an assumption that the data is stationary. When the data does not meet this assumption, you can apply techniques such as differencing to help make it stationary. The R software includes specialized tools to create, inspect, and even decompose time-based data. This provided you with clues as you learned about the ARIMA model, which is a combination of three different modeling techniques applied simultaneously. These techniques, similar to other models, require both art and science in their use. Additionally, you can apply the ARIMA model to non-seasonal and seasonal components, when both exist. Finally, you learned that you must have...