# Forecast that reflect very little happenstance fluctuation in the

Question 1

1. Forecast that reflect very little happenstance fluctuation in the past data are said to exhibit
 [removed] 1 Seasonal effects [removed] 2 noise dampening response [removed] 3 impulse response [removed] 4 all of the above [removed] 5 none of the above

5 points

Question 2

1. A Winter’s forecasting model that has zero values for the beta and gamma constants exhibit what type of behavior
 [removed] 1 A simple exponential smoothing model [removed] 2 Impulse response [removed] 3 Noise dampening [removed] 4 all of the above [removed] 5 none of the above

5 points

Question 3

1. In measuring forecast accuracy, the average of the absolute difference between the forecast and the actual demand is called
 [removed] 1 alpha [removed] 2 E-bar [removed] 3 MAD [removed] 4 all of the above [removed] 5 none of the above

5 points

Question 4

1. Choice the best type of forecasting methods for the type of data indicated
 trend data that fits in a straight line

 short range forecast with no trends or seasonal effects

 random data with no seasonal effects or trends

 random data that illustrates a trend or seasonal pattern
1.
2.
 random data with a trend or no seasonal effect
 A. Exponential Smoothing B. Winter’s Method C. Holt’s Method D. Linear Regression E. Moving Average

20 points

Question 5

1. In order to establish a forecast method that exhibits impulse response;
 [removed] 1 an exponential smoothing forecast method should be used [removed] 2 the data must be linear [removed] 3 The alpha coefficient should be set close to 1 for exponential smoothing [removed] 4 The alpha coefficient should be set close to 0 for exponential smoothing [removed] 5 None of the above

5 points

Question 6

1. Refer to the data in table 1 posted in the discussion folder. Using the data, what is the forecast for November if a three month moving average model is used?
 [removed] 1 49.25 [removed] 2 50.67 [removed] 3 53.00 [removed] 4 none of the above

5 points

Question 7

1. Refer to the data in table 1 posted in the discussion folder. Using the data, which month has a demand forecast equal to 55 for a 3 month moving average approach
 [removed] 1 April [removed] 2 June [removed] 3 August [removed] 4 October [removed] 5 None of the above

5 points

Question 8

1. Refer to the data in table 1 posted in the discussion folder. Using the data, what is the November forecast if exponential smoothing is used with an alpha value = .1
 [removed] 1 47.9 [removed] 2 53.2 [removed] 3 40.8 [removed] 4 51.6

5 points

Question 9

1. Refer to the data, table 1, from the discussion folder. Using this data, what is the forecast error % for an exponential smoothing model with a alpha of .6
 [removed] 1 10% [removed] 2 12% [removed] 3 14% [removed] 4 16% [removed] 5 18%

5 points

Question 10

1. Forecasting models are an integral part of business planning that requies input from
 [removed] 1 marketing [removed] 2 demand estimates [removed] 3 sales forecast [removed] 4 all of the above [removed] 5 none of the above

5 points

Question 11

1. The alpha coefficient in exponential smothing
 [removed] 1 is set equal to the actual value in period 1 [removed] 2 varies over a time series of data [removed] 3 is a value between 0 and 1 [removed] 4 all of the above [removed] 5 none of the above

5 points

Question 12

1. Quarterly data which reflect an increase every fourth quarter followed by a decrease every first quarter are said to be
 [removed] 1 seasonal [removed] 2 cyclical [removed] 3 periodical [removed] 4 abnormal [removed] 5 following a trend

5 points

Question 13

1. To deseasonalize time series data
 [removed] 1 divide each actual value by the trend line intercept [removed] 2 divide each actual value by its seasonal index factor [removed] 3 divide each actual value by total forecast error [removed] 4 divide each actual value by the alpha coefficient

5 points

Question 14

1. A linear trend for 12 months of data is y = 339.02 + 23.96x. What is the forecast for the next quarter (January, Feruary and March)?
 [removed] 1 1160.82 [removed] 2 1807.74 [removed] 3 2023.38 [removed] 4 3641.59

5 points

Question 15

1. Refer to the data in table 1 posted in the discussion folder. Using the data, what is the MAD for an exponential smoothing model with alpha = .1
 [removed] 1 6.2 [removed] 2 7.7 [removed] 3 8.3 [removed] 4 8.8

5 points

Question 16

1. The delphi method of forecasting is
 [removed] 1 time series method for detecting seasonality [removed] 2 variation of exponential smoothing method [removed] 3 multiple regression method [removed] 4 qualitative method which solicits from experts [removed] 5 qualitative method for researching similar to data

5 points

Question 17

1. The ideal value of MAD is
 [removed] 1 0 [removed] 2 100 [removed] 3 10 [removed] 4 none of the above

5 points

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