Consider the following time series data. Week 2 3 5 Value ST 14 16 13 (a) Construct a time series plot. What type of pattern exists in the data? O 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.) Week Time Series Value Forecast 1 19 2 12 3 14 4 10 14 X 5 16 10 X 6 13 16 X Compute MSE. (Round your answer to two decimal places.) MSE = What is the forecast for week 7?(c) Use a = 0.2 to compute the exponential smoothing values for the time series. Week Time Series Value Forecast 1 19 2 12 3 14 4 10 5 16 6 13 Compute MSE. (Round your answer to two decimal places.) MSE = What is the forecast for week 7? (Round your answer to two decimal places.) (d) Compare the three-week moving average forecast with the exponential smoothing forecast using @ = 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. O The three-week moving average provides a better forecast since it has a smaller MSE than the smoothing approach using a = 0.2. O The three-week moving average provides a better forecast since it has a larger MSE than the smoothing approach using a = 0.2. (e) Use trial and error to find a value of the exponential smoothing coefficient of that results in a smaller MSE than what you calculated for a = 0.2. a =