(Solved): Consider the following time series data. Month 1 2 3 Value 23 12 19 11 18 22 14 (a) Construct a t ...

Consider the following time series data. Month 1 2 3 Value 23 12 19 11 18 22 14 (a) Construct a time series plot. O 30 25 25 20 Im iwim 10 20 15 10- 5 0: 30 25- 20 15 0 10- 5 0- 0 1 1 2 + 2 3 4 4 Month 3. 4 Month 5 5 6 5 6 + 6 7 7 + 7 What type of pattern exists in the data? O The data appear to follow a seasonal pattern. O The data appear to follow a horizontal pattern. O The data appear to follow a trend pattern. O The data appear to follow a cyclical pattern. 8 8 DO ? 30- 25 20- 15 10 0 0 1 2 + 3 4 5 6 7 8 Month DO 30 0 0 1 2 3 4 Month 5 6 7 8 (b) Develop the three-month moving average forecasts for this time series. Time Series Value Month 1 2 3 4 6 7 Month 1 2 3 4 23 Compute MSE. (Round your answer to two decimal places.) MSE = 5 12 What is the forecast for month 8? 6 19 (c) Use ? = 0.2 to compute the exponential smoothing forecasts for the time series. (Round your answers to two decimal places.) Time Series Value 7 11 18 22 14 23 12 19 11 Forecast 18 22 14 Forecast Compute MSE. (Round your answer to two decimal places.) MSE = What is the forecast for month 8? (Round your answer to two decimal places.) (d) Compare the three-month moving average approach with the exponential smoothing approach using a = 0.2. Which appears to provide more accurate forecasts based on MSE? O The three-month moving average provides a better forecast since it has a smaller MSE than the exponential smoothing using a = 0.2. has a smaller MSE than the three-month moving average. O The exponential smoothing using a = 0.2 provides a better forecast since O The three-month moving average provides a better forecast since it has a larger MSE than the exponential smoothing using a = 0.2. O The exponential smoothing using a = 0.2 provides a better forecast since it has a larger MSE than the three-month moving average.