Question 1. How many statements are correct? (A) 0 (B) 1 (C) 2 (D) 3 (E) 4 Statement 1. A time series is the value of a business variable over time. Statement 2. Stationarity is the overall nature of a time series. Statement 3. A component is a cause of variability around the stationary mean. Statement 4. Variability in a time series can be due to patterns observed within a time series or outside causal factors that influence the time series or both.

Question 3. How many statements are correct about a moving average (MA)? (A) 0 (B) 1 (C) 2 (D) 3 (E) 4 Statement 1. Using moving average assumes only a trend component. Statement 2. The parameter for a moving average technique is the window. Statement 3. Using MA3 averages the last three values in a time series. Statement 4. The value from a moving average technique is an estimate of the stationary mean.

Question 5. How many statements are correct about the regression forecasting technique? (A) 0 (B) 1 (C) 2 (D) 3 (E) 4 Statement 1. Regression is a technique of preference for a stationary time series. Statement 2. The parameters for regression are the intercept and slope. Statement 3 A regression trend line can have only a positive slope. Statement 4. Variability due to the stationary nature of the time series is a trend component. Explained variability due to the trend line is a random component.

Question 7. How many statements are correct about the seasonal index forecasting technique? (A) 0 (B) 1 (C) 2 (D) 3 (E) 4 Statement 1. The seasonal index approach can be used only with a non-stationary time series. Statement 2. The seasonal index approach requires the use of seasonal indexes. Statement 3. The seasonal index approach can never be used for a cyclical component. Statement 4. For seasonal and random components, the variability within a year is random and the variability between the years is seasonal.

Question 9. How many statements are correct about the exponential smoothing (ES) technique? (A) 0 (B) 1 (C) 2 (D) 3 (E) 4 Statement 1. Exponential smoothing is a type of moving average technique. Statement 2. Exponential smoothing with one parameter assumes a stationary time series with a random component. Statement 3. Exponential smoothing with one parameter estimates the stationary mean. Statement 4. Exponential smoothing with one parameter is an iterative forecasting technique where the forecast is determined by the parameter multiplied by the current data value minus one plus the parameter multiplied by the last forecast.

Question 10. How many statements are correct? (A) 0 (B) 1 (C) 2 (D) 3 (E) 4 Statement 1. An annual time series will never have a seasonal component. Statement 2. A random component can exist in both a stationary time series and a non-stationary time series. Statement 3. A seasonal component is always a cyclical component. Whereas a cyclical component is not always a seasonal component. Statement 4. A non-stationary time series can have random, trend, and seasonal components at the same time.