1.
Forecasts
Correct Answer
C. Are rarely perfect
Explanation
The answer "are rarely perfect" is correct because forecasting is a complex process that involves making predictions about future events based on historical data and various assumptions. However, there are many factors that can affect the accuracy of forecasts, such as unforeseen events, changes in market conditions, and limitations in data availability or quality. Therefore, it is rare for forecasts to be completely accurate, and there is always some degree of uncertainty associated with them.
2.
Exponential smoothing is a form of weighted averaging.
Correct Answer
A. True
Explanation
Exponential smoothing is a technique used in time series forecasting to assign different weights to past observations, with more recent observations receiving higher weights. This allows for a more accurate representation of the underlying trend and helps in making predictions. Therefore, it can be said that exponential smoothing is a form of weighted averaging, making the given answer true.
3.
The following equation is used to predict quarterly demand: Yt = 300 - 2t where = 1 in the second quarter of last year. quarterly seasonal indices are Q1 = 1.5; Q = .8; Q3 = 1.1; and Q4 = .6, What is the seasonally adjusted forecast for the third quarter of this year? (show all your work)
Correct Answer
C. 316.8
Explanation
Y6 = [300-2(6)](1.1) = 316.8
4.
In order to increase the responsiveness of a forecast made using the simple moving average technique, the number of data points in the average (n) should be
Correct Answer
A. Decreased
Explanation
To increase the responsiveness of a forecast made using the simple moving average technique, the number of data points in the average (n) should be decreased. This is because a smaller number of data points in the average will give more weight to recent data, making the forecast more sensitive to recent changes. Conversely, increasing the number of data points in the average would smooth out the forecast and make it less responsive to short-term fluctuations. Multiplying by a larger or smaller alpha is not relevant to the simple moving average technique, so the correct answer is to decrease the number of data points.
5.
Which of the following is not a type of judgmental forecasting?
Correct Answer
E. Time series analysis
Explanation
Time series analysis is not a type of judgmental forecasting because it is a statistical method that uses historical data to predict future values based on patterns and trends. It does not rely on subjective opinions or input from individuals like executive opinions, sales force opinions, and consumer surveys. Instead, it focuses on analyzing and forecasting based on the historical patterns in the data.
6.
Forecasts for groups of items tend to be less accurate than forecasts for individual items because forecasts for individual items don't include as many influencing factors.
Correct Answer
B. False
Explanation
The statement suggests that forecasts for groups of items are less accurate than forecasts for individual items because individual item forecasts do not consider as many influencing factors. However, this statement is false. Forecasts for groups of items tend to be more accurate because they can account for variations and patterns within the group, whereas individual item forecasts may be influenced by random fluctuations. Additionally, group forecasts can benefit from statistical techniques like aggregation and smoothing, which can improve accuracy.
7.
The mean absolute deviation (mad) is used to
Correct Answer
C. Measure forecast accuracy
Explanation
The mean absolute deviation (MAD) is a statistical measure that calculates the average difference between each forecasted value and the actual value. It quantifies the accuracy of a forecast by measuring how close the forecasted values are to the actual values. Therefore, the correct answer is "measure forecast accuracy."
8.
St month's actual demand is the same as a forecast for this month if the forecast is based on
Correct Answer
B. Naive forecast
Explanation
The naive forecast assumes that the demand for this month will be the same as the demand for the previous month. Therefore, if the forecast is based on the naive forecast method, which uses the previous month's demand as the forecast for this month, the actual demand for this month will be the same as the forecast.
9.
Forecast is a statement about the future value of a variable of interest.
Correct Answer
A. True
Explanation
A forecast is a statement that predicts or estimates the future value of a variable of interest. It is based on available data and analysis, and it helps in making informed decisions or plans. Therefore, the given statement that a forecast is a statement about the future value of a variable of interest is correct.
10.
Error-difference between the actual value and the value that was predicted for a given period.
Correct Answer
A. True
Explanation
The given statement is true. Error refers to the difference between the actual value and the value that was predicted for a given period. This means that when making predictions or estimations, there is often a discrepancy between the expected outcome and the actual outcome. The error helps measure the accuracy or precision of the prediction, indicating how far off the prediction was from the actual value.
11.
Mean absolute deviation (MAD)
Correct Answer
B. The average absolute forecast error
Explanation
The mean absolute deviation (MAD) is a measure of the average absolute forecast error. It calculates the average difference between the forecasted values and the actual values, disregarding the direction of the errors. By taking the absolute value of each forecast error and then averaging them, MAD provides a measure of the overall accuracy of the forecasts. It is particularly useful in comparing the performance of different forecasting models or techniques. The squared forecast errors, on the other hand, are used in calculating the mean squared error (MSE), which emphasizes larger errors due to the squaring operation.
12.
Mean squared error (MSE)
Correct Answer
A. The average of squared forecast errors
Explanation
The mean squared error (MSE) is a metric used to measure the average of squared forecast errors. It calculates the average of the squared differences between the predicted and actual values. This metric is commonly used in regression analysis to assess the accuracy of a model's predictions. By squaring the errors, the MSE penalizes larger errors more heavily, giving a more comprehensive view of the model's performance. Therefore, the correct answer is "the average of squared forecast errors."
13.
Mean absolute percent error (MAPE) is the average absolute percent error.
Correct Answer
A. True
Explanation
The explanation for the given correct answer is that the mean absolute percent error (MAPE) is indeed the average absolute percent error. MAPE is a measure used to assess the accuracy of a forecasting method by calculating the average percentage difference between the forecasted and actual values. It provides a standardized way to compare the accuracy of different forecasting models. Therefore, the statement "mean absolute percent error (MAPE) is the average absolute percent error" is true.
14.
Judgmental forecasts
Correct Answer
C. Forecasts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts
Explanation
Judgmental forecasts are forecasts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts. This forecasting technique relies on the expertise and judgment of individuals to make predictions about future demand. It does not involve the use of explanatory variables or the projection of patterns identified in recent time series observations. Instead, it takes into account the opinions and insights of individuals who have knowledge and experience in the relevant industry or market.
15.
Times series forecasts
Correct Answer
B. Forecasts that project patterns identified in recent time series observations
Explanation
This answer correctly describes time series forecasts as forecasts that project patterns identified in recent time series observations. Time series forecasting involves analyzing historical data to identify trends, patterns, and seasonality in order to make predictions about future values. By projecting these patterns into the future, time series forecasts can provide estimates of future demand or other variables. This explanation accurately captures the essence of time series forecasting and how it utilizes patterns identified in recent observations to make predictions.
16.
Associative model
Correct Answer
A. Forecasting technique that uses explanatory variables to predict future demand
Explanation
The correct answer is "forecasting technique that uses explanatory variables to predict future demand." This means that the associative model is a method of forecasting that takes into account various factors or variables that can explain or influence future demand. It uses these explanatory variables to make predictions about future demand patterns. This approach is based on the idea that past patterns and relationships between variables can help predict future outcomes.
17.
Delphi method is an iterative process in which managers and staff complete a series of questionnaires, each developed from the previous one, to achieve a consensus forecast.
Correct Answer
A. True
Explanation
The Delphi method is indeed an iterative process where managers and staff participate in completing a series of questionnaires. These questionnaires are developed based on the previous ones, allowing for the refinement and evolution of ideas. The ultimate goal of this method is to reach a consensus forecast, where all participants contribute their opinions and insights to arrive at a collective decision. Therefore, the statement that the Delphi method is an iterative process to achieve a consensus forecast is true.
18.
Trend
Correct Answer
E. A long term upward or downward movement in data
Explanation
The correct answer is "a long term upward or downward movement in data." This explanation suggests that a trend refers to a consistent and prolonged pattern in data that either shows an increase or decrease over time. It implies that the data is not subject to short-term or temporary fluctuations, but rather represents a sustained movement in a specific direction.
19.
Seasonality
Correct Answer
D. Short term regular variations related to the calendar or time of day
Explanation
The term "seasonality" refers to short-term regular variations in data that are related to the calendar or time of day. This means that the data fluctuates in a predictable pattern based on the time of year or the time of day. These variations are not caused by unusual circumstances and are reflective of typical behavior. They can be observed as repetitive patterns that occur within a year or within a day.
20.
Cycle
Correct Answer
C. Wavelike variations lasting more than one year
Explanation
The term "cycle" refers to wavelike variations that occur over a period of more than one year. These variations can be observed in data and are characterized by repetitive patterns or fluctuations that extend beyond a yearly timeframe. Unlike short-term regular variations related to the calendar or time of day, cycles represent longer-term trends in the data. They are not caused by unusual circumstances and are not residual variations after accounting for other behaviors. Instead, cycles reflect inherent patterns or oscillations in the data that repeat over a period longer than a year.
21.
Random variations
Correct Answer
A. Residual variations after all other behaviors are accounted for
Explanation
The correct answer is residual variations after all other behaviors are accounted for. This means that the answer refers to the remaining variations in the data that cannot be explained or attributed to any other factors or behaviors. It suggests that once all other factors have been taken into consideration and accounted for, the residual variations represent the random and unpredictable fluctuations in the data.
22.
A forecast for any period that equals the previous period that equals the previous period's actual value.
Correct Answer
A. Naive forecast
Explanation
A naive forecast is a simple forecasting method where the forecast for any period is equal to the previous period's actual value. This method assumes that there will be no change or trend in the data, and that the future values will be the same as the past values. It is a basic and straightforward approach that can be useful when there are no clear patterns or trends in the data.
23.
Moving average is a technique that averages a number of recent actual values, updated as new values become available.
Correct Answer
A. True
Explanation
The statement correctly defines the concept of a moving average. It states that a moving average is a technique that calculates the average of a specific number of recent actual values. This average is updated as new values become available, providing a smoother representation of the data over time. The answer "true" confirms the accuracy of the statement.
24.
The weighted average is more recent values in a series are given more weight in computing a forecast.
Correct Answer
A. True
Explanation
The statement is true because in a weighted average, more recent values are given more weight in the calculation of a forecast. This means that the most recent data points have a greater influence on the overall average, reflecting the idea that more recent information is usually more relevant and accurate for predicting future trends.
25.
Exponential smoothing is a weighted averaging method based on the previous forecast plus a percentage of the forecast error.
Correct Answer
A. True
Explanation
Exponential smoothing is a forecasting technique that calculates future values by taking into account the previous forecast and adding a certain percentage of the forecast error. This method assigns more weight to recent observations, making it useful for capturing short-term trends and variations in data. Therefore, the given statement is true as it accurately describes the concept of exponential smoothing.
26.
Linear trend equation is Ft = a + bt, used to develop forecasts when the trend is present.
Correct Answer
A. True
Explanation
The given statement is true. The linear trend equation, Ft = a + bt, is indeed used to develop forecasts when there is a trend present. This equation allows for the estimation of future values based on the current trend observed in the data. By calculating the values of a and b, the equation can be used to predict future outcomes.
27.
Seasonal relative is percentage of average or trend.
Correct Answer
A. True
Explanation
The statement is true because a seasonal relative is indeed a percentage that represents the average or trend of a specific season. It is used to compare the value of a particular season to the average or trend of all seasons. This helps in identifying any seasonal patterns or trends in data and making appropriate adjustments or predictions based on those patterns.
28.
Centered moving average is a moving average positioned at the center of the data that were used to compute it.
Correct Answer
A. True
Explanation
A centered moving average is a type of moving average that is calculated by taking the average of a set of data points, with the average being positioned at the center of the data set. This means that the moving average will include an equal number of data points before and after the center point. Therefore, the statement "centered moving average is a moving average positioned at the center of the data that were used to compute it" is true.
29.
Tracking signal is the ratio of cumulative forecast error to the corresponding value of MAD, used to monitor a forecast.
Correct Answer
A. True
Explanation
The statement is true. Tracking signal is a measure used to monitor the accuracy of a forecast. It is calculated by dividing the cumulative forecast error by the corresponding value of the Mean Absolute Deviation (MAD). By comparing the tracking signal to a predetermined threshold, it can indicate whether the forecast is performing within acceptable limits or if adjustments need to be made.
30.
Bias is persistent tendency for forecasts to be greater or less than the actual values of a time series.
Correct Answer
A. True
Explanation
The statement is true because bias refers to a consistent tendency for forecasts to consistently overestimate or underestimate the actual values of a time series. This means that the forecasts consistently deviate from the true values in a particular direction, indicating a persistent bias.