The primary purpose of the mean absolute deviation (MAD) in forecasting is to:
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Measure forecast accuracy
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For a given product demand, the time-series trend equation is 53 - 4X. The negative sign on the slope of the equation:
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is an indication that product demand is declining
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Demand for a certain product is forecast to be 800 units per month, averaged over all 12 months of the year. The product follows a seasonal pattern, for which the January monthly index is 1.25. What is the seasonally-adjusted sales forecast for January?
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The seasonally-adjusted sales forecast for January is 1000 units. To calculate a seasonally-adjusted sales forecast you take the product forecast (in this case 800) and multiply that by the monthly index (in this case 1.25). Thus, 800 * 1.25 = 1000.
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The last four weekly values of sales were 80, 100, 105, and 90 units. The last four forecasts were 60, 80, 95, and 75 units. These forecasts illustrate:
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Bias
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The degree or strength of a relationship between two variables is shown by the__________
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correlation coefficient
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A forecast that projects a company's sales is a(n):
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Demand forecast
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Given an actual demand of 103, a previous forecast value of 99, and an alpha of .4, the exponential smoothing forecast for the next period would be:
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The forecast for the next period would be 100.6. The simple exponential smoothing forecast model uses the following equation:
Last period's forecast + α(Last period's demand - last period's forecast), where α = the smoothing constant. Therefore, in this case:
Last period's forecast = 99
α = .4
Last period's demand = 103
99 + .4 (103 - 99) = 100.6
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Given an actual demand of 61, a previous forecast value of 58, and an alpha of .3, the exponential smoothing forecast for the next period would be:
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The forecast for the next period would be 58.9. The simple exponential smoothing forecast model uses the following equation:
Last period's forecast + α(Last period's demand - last period's forecast), where α = the smoothing constant. Therefore, in this case:
Last period's forecast = 58
α = .3
Last period's demand = 61
58 + .3(61 - 58) = 58.9
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Given last periods forecast of 65, and last periods demand of 62, what is the simple exponential smoothing forecast with an alpha of .4 for the next period?
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The forecast for the next period would be 63.8. The simple exponential smoothing forecast model uses the following equation:
Last periods forecast + α(Last periods demand - last periods forecast), where α = the smoothing constant. Therefore, in this case:
Last periods forecast = 65
α = .4
Last periods demand = 62
65 + .4 (62 - 65) = 63.8
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Given forecast errors of -1, 4, 8, and -3, what is the mean absolute deviation?
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The mean absolute deviation is 4. The mean absolute deviation is designed to provide a measure of overall forecast error for the model. It does this by taking the sum of the absolute values of the individual forecast errors and dividing by the number of data periods. In this case,
1+4+8+3 = 16
16/4 = 4
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Forecasts used for new product planning, capital expenditures, facility location or expansion, and R&D typically utilize a__________
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long-range time horizon
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Forecasts are usually classified into three categories including:
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Short-range, medium-range, and long-range
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A time-series trend equation is 25.3 + 2.1X. What is your forecast for period 7?
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The forecast for period 7 is 40. This is determined by solving the equation 25.3 + 2.1X, where X = time period. In this case we are interested in period 7. Therefore:
25.3 + 2.1(7) =40
25.3 + 14.7 = 40
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Time-series patterns that repeat themselves after a period of days or weeks are called __________
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seasonality
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The last four months of sales were 8, 10, 15, and 9 units. The last four forecasts were 5, 6, 11, and 12 units. The Mean Absolute Deviation (MAD) is:
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The Mean Absolute Deviation (MAD) is 3.5. The mean absolute deviation is designed to provide a measure of overall forecast error for the model. It does this by taking the sum of the absolute values of the individual forecast errors and dividing by the number of data periods.
The last four months sales were 8, 10, 15, and 9 units. The forecasts for these same months were 5, 6, 11, and 12 units. Forecast errors are calculated using the equation demand - forecast. In this case, that would be 8 - 5 = 3; 10 - 6 = 4; 15 - 11 = 4; 9 - 12 = -3. Therefore:
3+4+4+3 = 14
14/4 = 3.5
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Which of the following statements about time-series forecasting is true?
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It is based on the assumption that the analysis of past demand helps predict future demand.
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A seasonal index for a monthly series is about to be calculated on the basis of three years' accumulation of data. The three previous July values were 110, 150, and 130. The average over all months is 190. The approximate seasonal index for July is:
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The approximate seasonal index for July is 0.684. The seasonal index is calculated by dividing a month's actual average demand by the average demand over all months. Thus, in this case:
Step 1 - Calculate average historical demand. To do this, we must first obtain the actual demand during July (in this case 110, 150, 130) and divide by the number of months on record (in this case 3). Thus, average July demand is calculated as 110 + 150 + 130 = 390/3 = 130
Step 2 - Calculate seasonal index by taking monthly average (130) and dividing by average demand over all months (190).
Seasonal index for July is 130/190 = 0.684
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Which of the following smoothing constants would make an exponential smoothing forecast equivalent to a naïve forecast?
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1.0
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Which of the following uses three types of participants: decision makers, staff personnel, and respondents?
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The Delphi method
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Which time-series model assumes that demand in the next period will be equal to the most recent period's demand?
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Naïve approach
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Given the following data about monthly demand, what is the approximate forecast for May using a four month moving average?
November = 39
December = 36
January = 40
February = 42
March = 48
April = 46
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The four-month moving average is 44. The moving average is calculated by summing the relevant monthly demand reports and dividing by the months included in the model. In this case, we are calculating a four month moving average for May so we will use the months of January (40), February (42), March (48), and April (46) in our calculation. Therefore:
40+42+48+46 = 176
176/4 = 44
Moving Average = 44
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The tracking signal is the__________
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ratio of cumulative error/MAD
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A regression model is used to forecast sales based on advertising dollars spent. The regression line is y=500+35x and the coefficient of determination is .90. Which is the best statement about this forecasting model?
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The correlation between sales and advertising is positive.
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If demand is 106 during January, 120 in February, 134 in March, and 142 in April, what is the 3-month simple moving average for May?
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The 3-month moving average for May is 132. The moving average is calculated by summing the relevant monthly demand reports and dividing by the months included in the model. In this case, we are calculating a three month moving average for May so we will use the months of February (120), March (134), and April (142) in our calculation. Therefore:
120+134+142 = 396
396/3 = 132
Moving Average = 132
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Quantitative methods of forecasting include
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Exponential smoothing
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