Juan was asked by his boss to develop a forecast model for the WTI Spot crude oil price. Juan was eager to show off his regression skills and was confident he could build a model that would accurately predict the WTI spot price. After receiving a file of the Cushing OK WTI Spot Price, he got to work. Juan started by first plotting the time series data in Excel and determining if there were any trends or seasonality. He decided to start by building several models. QUESTIONS: 1. Plot time series data (10 pts) 2. Generate linear trend model and forecast WTI for the next period. (12 pts) 3. Generate linear trend with seasonality model and forecast WTI for the next period. (12 pts) 4. Comment on the quality of these models and calculate MAD (10 pts) 5. Show the linear trend and trend with seasonality model equations (6 pts) As Juan was reviewing the modeling results, his colleague, Mary, commented that he might want to try other forecasting methods that are simpler and may provide better forecasts. Mary suggested to try Moving Average and Exponential Smoothing models to try and generate a more accurate forecast. QUESTIONS: 1. Generate Moving Averages of 2, 3, 5, and 10 weeks and forecast WTI for the next period. (12 pts) 2. Calculate the MAD for each Moving Average model and comment on which is the best model among these. (10 pts) 3. Generate Exponential Smoothing models with alpha of 0.2, 0.4, 0.6 and 0.8. Forecast WTI for the next period. (12 pts) 4. Calculate the MAD for each Exponential Smoothing model and comment on which is the best model among these. (10 pts) 5. Plot the Moving Average models with the actual WTI on one chart AND on a separate chart plot the Exponential Smoothing models with actual WTI. (6 pts) EXTRA CREDIT (5 pts): 1. Compare the forecasts from the all the models (Linear trend, trend with seasonality, moving average and exponential smoothing) generated using the MAD. Determine which model Juan should present to his boss.