FiltersDone

Question type

Essay

Multiple Choice

Short Answer

True False

Matching

Exam 4: Forecasting

Show Answer

Share

---

All types

Filters

Study FlashcardsPractice ExamLearn

Question 61

Multiple Choice

Review LaterAdd Note

Review Later

Gradual movement in time-series data over time is called

A) seasonal variation.
B) a cycle.
C) a trend.
D) exponential variation.
E) random variation.

F) A) and B)
G) D) and E)

Correct Answer

verified

Show Answer

Question 62

Essay

Review LaterAdd Note

Review Later

Explain, in your own words, the meaning of the coefficient of determination.

Correct Answer

verified

The coefficient of determinati...

View Answer

Show Answer

Question 63

Multiple Choice

Review LaterAdd Note

If two variables were perfectly correlated, the correlation coefficient r would equal

What is the key difference between weighted moving average and simple moving average approaches to forecasting?

A small family-owned restaurant uses a seven-day moving average model to determine manpower requirements. These forecasts need to be seasonalized because each day of the week has its own demand pattern. The seasonal indices for each day of the week are: Monday, 0.445; Tuesday, 0.791; Wednesday, 0.927; Thursday, 1.033; Friday, 1.422; Saturday, 1.478; and Sunday 0.903. Average daily demand based on the most recent moving average is 194 patrons. What is the seasonalized forecast for each day of next week?

Which of the following is not present in a time series?

Forecasts of individual products tend to be more accurate than forecasts of product families.

________ forecasting tries a variety of computer models and selects the best one for a particular application.

The forecasting time horizon and the forecasting techniques used tend to vary over the life cycle of a product.

Jim's department at a local department store has tracked the sales of a product over the last ten weeks. Forecast demand using exponential smoothing with an alpha of 0.4, and an initial forecast of 28.0 for period 1. Calculate the MAD. What do you recommend? $\begin{array} { | c | c | } \hline \text { Period } & \text { Demand } \\\hline 1 & 24 \\\hline 2 & 23 \\\hline 3 & 26 \\\hline 4 & 36 \\\hline 5 & 26 \\\hline 6 & 30 \\\hline 7 & 32 \\\hline 8 & 26 \\\hline 9 & 25 \\\hline 10 & 28 \\\hline\end{array}$

What is a time-series forecasting model?

________ forecasts use a series of past data points to make a forecast.

Use exponential smoothing with trend adjustment to forecast deliveries for period 10. Let alpha = 0.4, beta = 0.2, and let the initial trend value be 4 and the initial forecast be 200. $\begin{array} { | c | c | } \hline \underline { \text { Period } } & \underline { \text { Actual } } \\ & \underline { \text { Demand } } \\\hline 1 & 200 \\\hline 2 & 212 \\\hline 3 & 214 \\\hline 4 & 222 \\\hline 5 & 236 \\\hline 6 & 221 \\\hline 7 & 240 \\\hline 8 & 244 \\\hline 9 & 250 \\\hline\end{array}$

The sales force composite forecasting method relies on salespersons' estimates of expected sales.

Which of the following uses three types of participants: decision makers, staff personnel, and respondents?

The last four weekly values of sales were 80, 100, 105, and 90 units. The last four forecasts (for the same four weeks) were 60, 80, 95, and 75 units. Calculate MAD, MSE, and MAPE for these four weeks. $\begin{array} { | c | c | c | c | c | } \hline \text { Sales } & \text { Forecast } & \text { Error } & \text { Error squared } & \text { Pct. error } \\\hline 80 & 60 & 20 & 400 & .25 \\\hline 100 & 80 & 20 & 400 & .20 \\\hline 105 & 95 & 10 & 100 & .095 \\\hline 90 & 75 & 15 & 225 & .167 \\\hline\end{array}$

Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naive forecast?

Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation?

Which of the following most requires long-range forecasting (as opposed to short-range or medium-range forecasting) for its planning purposes?

A fundamental distinction between trend projection and linear regression is that