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Q:
If a smoothing model is applied with a smoothing constant exceeding 0.50, the forecasting bias will tend to be positive in most cases.
Q:
The owners of Hal's Cookie Company have collected sales data for the past 8 months. These data are shown as follows:Using a starting forecast in period 1 of 100, the forecast bias over periods 2-8 is negative when a single exponential smoothing model is used with a smoothing constant of 0.20
Q:
Double exponential smoothing is used instead of single exponential smoothing when extra smooth forecasts are desired.
Q:
Because simple exponential smoothing models require a starting point for the first period forecast that will be arbitrary, it is important to have as much data as possible to dampen out the effect of the starting point.
Q:
In establishing a single exponential smoothing forecasting model, a starting point for the forecast value for period 1 is required. One method for arriving at this starting point is to use the first data point as the forecast for that period. If we do that, then the first data point should be ignored when computing measures of forecast error.
Q:
If a time series contains substantial irregular movement, the smoothing constant for a single exponential smoothing model that is close to 1.0 will result in forecasts that are not as smoothed out as those that would occur if a smaller smoothing constant was used.
Q:
In a single exponential smoothing model, a large value for the smoothing constant will result in greater smoothing of the data than will a smoothing constant close to zero.
Q:
In a single exponential smoothing model, one smoothing constant is used to weigh the historical data, and the model is of primary value when the data do not exhibit trend or seasonal components.
Q:
In a single exponential smoothing model, finding the forecast value for each period requires having the actual and forecast values from the proceeding period. This is not possible for the first period, so for the first period one should use the actual value as the forecast value.
Q:
An advantage of exponential smoothing techniques over a regression-based trend model is that the exponential smoothing model allows us to weigh each observation equally, thereby giving a fairer method of developing a forecast.
Q:
The Durbin-Watson test for autocorrelation can be reliably applied to any sample sizes.
Q:
If the observed value in a time series for period 3 is yt= 128, and the seasonal index that applies to period 3 is 1.20, then the deseasonalized value for period 3 is 153.6
Q:
The reason for testing for the presence of autocorrelation in a regression-based trend forecasting model is that one assumption of the regression analysis is that the residuals are not correlated.
Q:
If a forecasting model produces forecast errors (residuals) that are negatively correlated, then we expect a negative residual to be followed by another negative residual to be followed by another negative residual and so forth.
Q:
If the Durbin-Watson d statistic has a value close to 2, there is reason to believe that there is no autocorrelation between the forecast errors.
Q:
The purpose of deseasonalizing a time series is that a strong seasonal pattern may make it difficult to see a trend in the time series.
Q:
If you suspect that a nonlinear trend exists in your data, one way to deal with it in a trend-based forecasting application is to transform the independent variable, for example by squaring the time measure or maybe taking the square-root of the time measure.
Q:
Large values of the Durbin-Watson d statistic indicate that positive autocorrelation among the forecast errors exists.
Q:
It is possible to conduct a statistical test for autocorrelation using the Durbin-Watson test and not be able to make a definitive conclusion about whether there is autocorrelation or not based on the data.
Q:
If the Durbin-Watson test leads you to reject the null hypothesis, then you are concluding that the forecast errors are positively autocorrelated.
Q:
If the forecast errors are autocorrelated, this is a good indication that the model has been specified correctly.
Q:
To deseasonalize a time series, assuming a multiplicative model, the observed values are divided by the appropriate seasonal index.
Q:
Herb Criner, an analyst for the Folgerty Company, recently gave a report in which he stated that the annual sales forecast based on 20 years of annual sales data was done using a seasonally adjusted, trend-based forecasting technique. Given the information presented here, this statement has the potential to be credible.
Q:
When using the multiplicative time-series model to determine the seasonal indexes, the first step is to isolate the seasonal and random components from the cyclical and trend components.
Q:
Recently, a manager for a major retailer computed the following seasonal indexes:The manager then developed the following least squares trend model based on the past five years of quarterly data: . Based on this, the seasonally adjusted forecast for quarter 25, which is the winter quarter, is 489.11
Q:
A seasonally unadjusted forecast is one that is made from seasonal data without any adjustment for the seasonal component in the time series.
Q:
If a time series involves monthly data there will be a total of 12 seasonal indexes.
Q:
Recently, a manager for a major retailer computed the following seasonal indexes:Note that the index for Summer Qtr is missing. However, it can be determined that the index for that period is approximately 1.03
Q:
The Baker's Candy Company has been in business for three years. The quarterly sales data for the company are shown as follows:Based on these data, the seasonal index for quarters 3, 7, and 11 is approximately 1.61
Q:
In a time series with quarterly sales data, assume that the seasonal index for the summer quarter has been found to be 0.87, this can be interpreted to mean that sales tend to be 87 percent higher in the summer quarter when compared to the other quarters.
Q:
A seasonal index is a statistic that is computed from time-series data to indicate the effect of the seasonality in the time-series data.
Q:
A plot of the time series with time on the horizontal axis is an effective means of assessing whether the series is linear or nonlinear.
Q:
It is possible to use linear regression analysis to develop a forecasting model for nonlinear data if we can effectively transform the data.
Q:
The reason for using split samples in developing a forecasting model is to eliminate the potential for bias in the resulting model.
Q:
One of the disadvantages of a regression-based linear trend forecasting model is that the forecast errors are computed for time periods that were used in developing the forecasting model and thus do not truly measure the forecasting ability of the model.
Q:
Forecast bias measures the average amount of error per forecast, so a positive value means that forecasts tended to be too low.
Q:
Gibson, Inc. is a holding company that owns several businesses. One such business is a truck sales company. To help in managing this operation, managers at Gibson have collected sales data for the past 20 years showing the number of trucks sold each year. They have then developed the linear trend forecasting model shown as follows:Based on this information, the fitted value for year 1 is about 99.
Q:
In measuring forecast errors, the MAD and the square root of the MSE will provide similar (but not identical) values, in that both provide a measure of the "typical" amount of error in forecasts.
Q:
In comparing two or more forecasting models, the MAD value is useful in determining how successful the models were in fitting historical data.
Q:
In using simple linear regression to find the linear trend in an annual time series from 1990 to 2005, the values 1990, 1991, etc. are used as the values of the independent variable t when the regression is conducted.
Q:
Renton Industries makes replacement parts for the automobile industry. As part of the company's capacity planning, it needs a long-range total demand forecast. The following information was generated based on 10 years of historical data on total number of parts sold each year.Based on this information, we can conclude that sales on average have been growing by more than 48 thousand annually.
Q:
You are given the following linear trend model: Ft= 345.60 - 200.5(t). This model implies that in year 1, the dependent variable had a value of 145.1.
Q:
From an annual time series of a company's sales the linear trend model Ft= 127 + 54(t) has been developed. This means that on average sales have been increasing by 127 per year.
Q:
A time-series graph shows that annual sales data have grown gradually over the past 10 years. Given this, if a linear trend model is used to forecast future years' sales, the sign on the regression slope coefficient will be positive.
Q:
One of the basic tools for creating a trend-based forecasting model is regression analysis.
Q:
Two common unweighted indexes are the Paasche Index and the Laspeyres Index.
Q:
To compare one value measured at one point in time with other values measured at different points in time, index numbers must be used.
Q:
Stock analysts have recently stated in a meeting on Wall Street that over the past 50 years there have been periods of high market prices followed by periods of lower prices but over time prices have moved upwards. Given their statement, stock prices most likely exhibit only trend and cyclical components.
Q:
In order to identify a cyclical component in time-series data, one year of weekly data should be sufficient.
Q:
Some stocks are referred to as cyclical stock because they tend to be in favor for several years and then out of favor for several years. This is a correct use of the term cyclical.
Q:
Harrison Hollow, an upscale eatery in Atlanta, tracks its sales on a daily basis. Recently, the manager stated that sales over the past three weeks have been very cyclical. Given the data she has, this statement is not a reasonable one to make.
Q:
The Gilbert Company chief financial officer has been tracking annual sales for each of the company's three divisions for the past 10 years. At a recent meeting, he pointed to the annual data and indicated that it clearly showed the seasonality associated with its business. Given the data, this statement may have been very appropriate.
Q:
While virtually all time series exhibit a random component, not all time series exhibit other components.
Q:
In a recent meeting, a manager indicated that sales tend to be higher during October, November, and December and lower in the spring. In making this statement, she is indicating that sales for the company are cyclical.
Q:
An annual time series cannot exhibit a seasonal component.
Q:
The time-series component that implies a long-term upward or downward pattern is called the trend component.
Q:
In order for a time series to exhibit a seasonal component, the data must be measured in periods as short or shorter than quarterly.
Q:
A stockbroker at a large brokerage firm recently analyzed the combined annual profits for all firms in the airline industry. One time-series component that may have been present in these annual data was a seasonal component.
Q:
The forecasting interval is the unit of time for which forecasts are made.
Q:
If the historical data on which the model is being built consist of weekly data, the forecasting period would also be weekly.
Q:
If a manager is planning for an expansion of the factory, a forecast model with a long-term planning horizon would probably be used.
Q:
Model specification is the process of determining how well a forecasting model fits the past data.
Q:
If one or more of the regression assumptions has been violated this means that the current regression model is not the best one for this data set, and another model should be sought.
Q:
A useful method for determining whether a linear function is the appropriate function to describe the relationship between the x and y variable is a residual plot in which the residuals are plotted on the vertical axis and the independent variable is on the horizontal axis.
Q:
When we say that we wish to determine the aptness of a regression model, we are actually saying that we wish to check to see whether the resulting model meets the basic assumptions of regression analysis.
Q:
The best subsets method will involve trying fewer different regression models than stepwise regression.
Q:
If a stepwise regression approach is used to enter, one at a time, four variables into a regression model, the resulting regression equation may differ from the regression equation that occurs when all four of the variables are entered at one step.
Q:
Standard stepwise regression is a good way of identifying potential multicollinearity problems since we are able to see the impact on the model at each step that occurs when a new variable is added to the model. For instance, if bringing in a new variable causes the sign to change on a previously entered variable, we have evidence of multicollinearity.
Q:
The forward selection method and the backward elimination method will always lead to choosing the same final regression model.
Q:
One reason for examining the adjusted R-square value in a multiple regression analysis is that the R-square value will increase just by adding additional independent variables to the model, whereas the adjusted R-square accounts for the relationship between the number of independent variables and the sample size and may actually decline if inappropriate independent variables are included in the model.
Q:
When the best subsets approach is used in a regression application, one method for determining which of the many possible models to select for potential use is called the Cpstatistic.
Q:
In the best subsets approach to regression analysis, if we start with 4 independent variables, a total of 33 different regression models will actually be computed for possible selection as the best model to use.
Q:
It is possible for the standard error of the estimate to actually increase if variables are added to the model that do not aid in explaining the variation in the dependent variable.
Q:
Standard stepwise regression combines attributes of both forward selection and backward elimination.
Q:
In a forward stepwise regression process, it is actually possible for the R-square value to decline if variables are added to the regression model that do not help to explain the variation in the dependent variable.
Q:
Stepwise selection will always find the best regression model.
Q:
In a forward selection stepwise regression process, the second variable to be selected from the list of potential independent variables is always the one that has the second highest correlation with the dependent variable.
Q:
In a forward selection stepwise regression process, the first variable to be selected will be the variable that can, by itself, do the most to explain the variation in the dependent variable. This will be the variable that provided the highest possible R-square value by itself.
Q:
A decision maker is considering constructing a multiple regression model with two independent variables. The correlation between x1and y is 0.70, and the correlation between variable x2and y is 0.50. Based on this, the regression model containing both independent variables will explain 74 percent of the variation in the dependent variable.
Q:
Stepwise regression is the approach that is always taken when developing a regression model to fit a curvilinear relationship between the dependent and potential independent variables.