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Which of the following is not a good measure of forecast accuracy

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Question;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 = 0 + 1LHR + 2QUAL + 3T. If 3 = 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 = 0 + 1Price + 2Adv + 3Sept + 4Feb. If 3 = 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

Paper#20127 | Written in 18-Jul-2015

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