Exam 5: Forecasting

Tim gave his new intern the task of computing the tracking signal for a brand-new forecasting technique.The first month's data was available so the intern pounded away at his computer keyboard for the better part of an hour before finally running up to Tim brandishing a piece of paper with the answer.Which of these numbers should Tim not see for the first month's tracking signal result?

The errors in a particular forecast are as follows: 4, -3, 2, 5, -1.What is the tracking signal of the forecast?

As one increases the number of periods used in the calculation of a moving average

The following table represents the new members that have been acquired by a fitness center. Assuming α = 0.3, β = 0.4, an initial forecast of 40 for January, and an initial trend adjustment of 0 for January, use exponential smoothing with trend adjustment to come up with a forecast for May on new members.

The decomposition approach to forecasting (using trend and seasonal components)may be helpful when attempting to forecast a TIME SERIES.Could an analogous approach be used in multiple regression analysis? Explain briefly.

Enrollment in a particular class for the last four semesters has been 120, 126, 110, and 130.Suppose a one-semester moving average was used to forecast enrollment (this is sometimes referred to as a naïve forecast).Thus, the forecast for the second semester would be 120, for the third semester it would be 126, and for the last semester it would be 110.What would the MSE be for this situation?

For the data below: (a)Develop a scatter diagram. (b)Develop an exponential smoothing forecast using an alpha of 0.2 and a separate exponential smoothing forecast using an alpha of 0.9. (c)Compute the MSE for both forecasts from part b.Which is more accurate?

Which of the following is not classified as a qualitative forecasting model?

The most common quantitative causal model is regression analysis.

When is the exponential smoothing model equivalent to the naïve forecasting model?

Consider the actual and forecast values contained in the table. What is the MAPE?

Given the following data and seasonal index: (a)Compute the seasonal index using only year 1 data. (b)Determine the deseasonalized demand values using year 2 data and year 1's seasonal indices. (c)Determine the trend line on year 2's deseasonalized data. (d)Forecast the sales for the first 3 months of year 3, adjusting for seasonality.

Use simple exponential smoothing with α = 0.4 to forecast donut sales for March.Assume that the forecast for January was for 28 donuts.

In a second order exponential smoothing, a low β gives less weight to more recent trends.

Daily demand for newspapers for the last 10 days has been as follows: 12, 13, 16, 15, 12, 18, 14, 12, 13, 15 (listed from oldest to most recent).Forecast sales for the next day using a three-day weighted moving average where the weights are 3, 1, and 1 (the highest weight is for the most recent number).

Multiple regression models use dummy variables to adjust for seasonal variations in an additive TIME SERIES model.

Consider the actual and forecast values contained in the table. What is the MAD?

Sales for boxes of Girl Scout cookies over a 4-month period were forecasted as follows: 100, 120, 115, and 123.The actual results over the 4-month period were as follows: 110, 114, 119, 115.What was the MAD of the 4-month forecast?

Consider the actual and forecast values contained in the table. What is the MSE of the forecast?

Daily humidity in the city of Houston for the last week have been: 93, 94, 93, 95, 92, 86, 98 (yesterday).Calculate the MAD based on a two-day moving average, covering all days in which you can have a forecast and an actual humidity level.