Exam (elaborations) TEST BANK FOR Operations And Supply Chain Manageme

Exam (elaborations) TEST BANK FOR Operations And Supply Chain Manageme Time series forecasting models make predictions about the future based on analysis of past data. True False 6. In the weighted moving average forecasting model the weights must add up to one times the number of data points. True False 7. In a forecasting model using simple exponential smoothing the data pattern should remain stationary. True False 8. In a forecasting model using simple moving average the shorter the time span used for calculating the moving average, the closer the average follows volatile trends. True False 9. In the simple exponential smoothing forecasting model you need at least 30 observations to set the smoothing constant alpha. True False 10.Experience and trial and error are the simplest ways to choose weights for the weighted moving average forecasting model. True False 11.Bayesian analysis is the simplest way to choose weights for the weighted moving average forecasting model. True False 12.The weighted moving average forecasting model uses a weighting scheme to modify the effects of individual data points. This is its major advantage over the simple moving average model. True False 13.A central premise of exponential smoothing is that more recent data is less indicative of the future than data from the distant past. True False 14.The equation for exponential smoothing states that the new forecast is equal to the old forecast plus the error of the old forecast. True False 15.Exponential smoothing is always the best and most accurate of all forecasting models. True False 16.In exponential smoothing, it is desirable to use a higher smoothing constant when forecasting demand for a product experiencing high growth. True False 17.The value of the smoothing constant alpha in an exponential smoothing model is between 0 and 1. True False 18.Simple exponential smoothing lags changes in demand. True False 19.Exponential smoothing forecasts always lag behind the actual occurrence but can be corrected somewhat with a trend adjustment. True False 20.Because the factors governing demand for products are very complex, all forecasts of demand contain error. True False 21.Random errors can be defined as those that cannot be explained by the forecast model being used. True False 22.There are no differences in strategic and tactical forecasting. A forecast is a mathematical projection and its ultimate purpose should make no difference to the analyst. True False 23.Random errors in forecasting occur when an undetected secular trend is not included in a forecasting model. True False 24.When forecast errors occur in a normally distributed pattern, the ratio of the mean absolute deviation to the standard deviation is 2 to 1, or 2 x MAD = 1 standard deviation. True False 25.MAD statistics can be used to generate tracking signals. True False 26.RSFE in forecasting stands for "reliable safety function error." True False 27.In forecasting, RSFE stands for "running sum of forecast errors." True False 28.A tracking signal (TS) can be calculated using the arithmetic sum of forecast deviations divided by the MAD. True False 29.A restriction in using linear regression is that it assumes that past data and future projections fall on or near a straight line. True False 30.Regression is a functional relationship between two or more correlated variables, where one variable is used to predict another. True False 31.Linear regression is not useful for aggregate planning. True False 32.The standard error of the estimate of a linear regression is not useful for judging the fit between the data and the regression line when doing forecasts. True False 33.Multiple regression analysis uses several regression models to generate a forecast. True False 34.For every forecasting problem there is one best forecasting technique. True False 35.A good forecaster is one who develops special skills and experience at one forecasting technique and is capable of applying it to widely diverse situations. True False 36.In causal relationship forecasting leading indicators are used to forecast occurrences. True False 37.Qualitative forecasting techniques generally take advantage of the knowledge of experts and therefore do not require much judgment. True False 38.Market research is a quantitative method of forecasting. True False 39.Decomposition of a time series means identifying and separating the time series data into its components. True False 40.A time series is defined in the text as chronologically ordered data that may contain one or more components of demand variation: trend, seasonal, cyclical, autocorrelation, and random. True False 41.It is difficult to identify the trend in time series data. True False 42.In decomposition of time series data it is relatively easy identify cycles and autocorrelation components. True False 43.We usually associate the word "seasonal" with recurrent periods of repetitive activity that happen on other than an annual cycle. True False Multiple Choice Questions 44.In time series data depicting demand which of the following is not considered a component of demand variation? A. Trend B. Seasonal C. Cyclical D. Variance E. Autocorrelation 45.Which of the following is not one of the basic types of forecasting? A. Qualitative B. Time series analysis C. Causal relationships D. Simulation E. Force field analysis 46.In most cases, demand for products or services can be broken down into several components. Which of the following is not considered a component of demand? A. Average demand for a period B. A trend C. Seasonal elements D. Past data E. Autocorrelation 47.In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand? A. Cyclical elements B. Future demand C. Past demand D. Inconsistent demand E. Level demand 48.In most cases, demand for products or services can be broken into several components. Which of the following is considered a component of demand? A. Forecast error B. Autocorrelation C. Previous demand D. Consistent demand E. Repeat demand 49.Which of the following forecasting methodologies is considered a qualitative forecasting technique? A. Simple moving average B. Market research C. Linear regression D. Exponential smoothing E. Multiple regression 50.Which of the following forecasting methodologies is considered a time series forecasting technique? A. Simple moving average B. Market research C. Leading indicators D. Historical analogy E. Simulation 51.Which of the following forecasting methodologies is considered a time series forecasting technique? A. Delphi method B. Exponential averaging C. Simple movement smoothing D. Weighted moving average E. Simulation 52.Which of the following forecasting methodologies is considered a causal forecasting technique? A. Exponential smoothing B. Weighted moving average C. Linear regression D. Historical analogy E. Market research 53.Which of the following forecasting methods uses executive judgment as its primary component for forecasting? A. Historical analogy B. Time series analysis C. Panel consensus D. Market research E. Linear regression 54.Which of the following forecasting methods is very dependent on selection of the right individuals who will judgmentally be used to actually generate the forecast? A. Time series analysis B. Simple moving average C. Weighted moving average D. Delphi method E. Panel consensus 55.In business forecasting, what is usually considered a short-term time period? A. Four weeks or less B. More than three months C. Six months or more D. Less than three months E. One year 56.In business forecasting, what is usually considered a medium-term time period? A. Six weeks to one year B. Three months to two years C. One to five years D. One to six months E. Six months to six years 57.In business forecasting, what is usually considered a long-term time period? A. Three months or longer B. Six months or longer C. One year or longer D. Two years or longer E. Ten years or longe