Consider the following time series data. Week 1 2 3 4 5 6 Value 18 12 15 10 16 13 (a) Construct a time series plot. What type of pattern exists in the data? The data appear to follow a horizontal pattern. The data appear to follow a seasonal pattern. The data appear to follow a cyclical pattern. The data appear to follow a trend pattern. (b) Develop the three-week moving average for this time series. (Round your answers to two decimal places.) Time Series Forecast Week Value 1 18 2 12 3 15 4 10 5 16 6 13 Compute MSE. (Round your answer to two decimal places.) MSE = What is the forecast for week 7? (e) Use 0.2 to compute the exponential smoothing values for the time series Week Time Series Value Forecast 1 115 2 12 E 3 15 4 10 5 16 6 13 Compute MSE. (Round your answer to two decimal places.) MSE What is the forecast for week 77 (Round your answer to two decimal places.) (d) Compare the three-week moving average forecast with the exponential smoothing forecast using a = 0.2. Which appears to provide the better forecast based on MSE? Explain. The exponential smoothing using a 0.2 provides a better forecast since it has a smaller MSE than the three-week moving average approach The exponential smoothing using a = 0.2 provides a better forecast since it has a larger MSE than the three week moving average approach The three week moving average provides a better forecast since it has a larger MSE than the smoothing approach using - 0.2. The three week moving average provides a better forecast since it has a smaller MSE than the smoothing approach using 0.2 (e) Use trial and error to find a value of the exponential smoothing coefficient a that results in a smaller MSE than what you calculated for a 0.2