this is all one question

Exponential Smoothing Conceptual Overview: Explore how changing the weight for exponential smoothing effects forecast and accuracy statistics. Exponential smoothing is a weighted average of the last observation with the last forecast. The weight a determines the proportion of the weight given to the last observation. As the weight increases, the forecast becomes more determined by the prior observation and less by the prior forecast. In the graph, the red dots with solid lines represent the time series for the number of gallons of milk sold by a convenience store. The blue dots with dotted lines represent the exponential smoothing forecast. Use the slider below the graph to change the value of a between 0 and 1. Here are some things to try: 1. Move the slider to a = 0. What determines the forecast in this case? 2. Move the slider to a = 1. What determines the forecast in this case? 3. Move the slider until you find the value of a that produces the smallest mean squared error (MSE). (suojeo sojes et Exponential Smoothing: a = 0.2 MSE - 1285.28 1 2 3 4 5 7 8 9 10 11 12 6 Month 1. For what value of the smoothing constant (alpha) does the exponential smoothing method look like the Naive Forecasting Method? a. 0.0 b. 0.3 C. 0.5 d. 1.0 -Select- 2. What value of the smoothing constant results in the smallest Mean Squared Error? a. 0.0 b. 0.18 C. 0.36 d. 0.5 e. 1.0 1-Select- 3. Which of the following is least accurate? a. The smaller the value of the smoothing constant, the smoother the forecast series. b. When the smoothing constant is 0.0, the mean squared error is minimized c. The larger the smoothing constant, the less weight the forecast places on the prior forecast value. d. The smoothing constant must always be between zero and one. Select