Reference no: EM131418083

1.Which of the following is not a good measure of forecast accuracy?

A) MSE - Mean sum of errors

B) MAE - Mean absolute value of errors

C) MAPE - Mean absolute percentage errors

D) RMSE - square root of mean squared errors

2.The most complicated forecasting model is always the best model for forecasting accuracy.

A) True

B) False

3.Forecast errors for regression models are the same as the fitted values of the regression equation.

A) True

B) False

4.Suppose you have the following regression equation where PROD = output produced per month, LHR = labor hours paid per month, and QUAL = quality of raw materials purchased within the month, and T=trend variable:Prod = ß0 + ß1LHR + ß2QUAL + ß3T. If ß3 = 3.5 and is significantly different from zero, this result indicates

A) an increase in labor efficiency.

B) an increase in the quality of raw materials used in the production process.

C) an increase in labor efficiency or the quality of raw materials or both.

D) an increase in production efficiency holding constant labor efficiency and material quality.

E) an increase in production efficiency due to an increase in labor efficiency or quality of materials or both.

5.Suppose you have the following regression equation where SALES = 100s of cars sold per month, PRICE = average actual price paid by customer for car purchases in month, ADV = monthly advertising expenses, SEPT = dummy variable with value 1 for Sept and 0 for all other months; and FEB = dummy variable with value of 1 for February and 0 for all other months:

Sales = ß0 + ß1Price + ß2Adv + ß3Sept + ß4Feb. If ß3 = 3.5 and is significantly different from zero, this result indicates that

A) sales are higher in September than in all other months.

B) sales are higher than sales in all other months excluding February.

6.What are the limitation of using regression analysis as a forecast tool?

A) Use of historical data may be limited in forecasting future.

B) Use of all available data may be good because one has much information to develop a good model, but may be bad because a number of changes may have occurred over the time period of the data.

C) Regression forecasts for future periods require information about future values of independent variables.

D) Variation in the independent variables only capture limited amount of variability in dependent variable.

E) All of the above

7.Moving average forecasts for period t is simply the average of the k previous observations in the time series.

A) True

B) False

8.If one uses moving averages to make forecasts when there is an upward trend in the data, moving averages will underestimate (under-forecast) future outcomes.

A) True

B) False

9.Exponential smoothing may be a better forecasting model than moving average models because it puts greater weight on more recent data than historic data.

A) True

B) False

10.If one uses simple exponential smoothing models to make forecasts when there is an upward trend in the data, simple exponential smoothing models will underestimate (under-forecast) future outcomes.

A) True

B) False