Question 1.How many statements are correct about components?

(A) 0 (B) 1 (C) 2 (D) 3 (E) 4

Statement 1. A time series is the intervals of time within a series.

Statement 2. Stationarity is the nonlinear nature of a time series.

Statement 3. A component is the constant value of a time series called 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 2. How many statements are correct about a moving average (MA) with a window of three?

(A) 0 (B) 1 (C) 2 (D) 3 (E) 4

Statement 1. Using moving average assumes only a random component.

Statement 2. The parameters for a moving average technique are the intercept and slope.

Statement 3. Using MA3 averages the three values in a time series closest to the forecast.

Statement 4. The value from a moving average technique is an estimate of the trend component.

Question 3.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 non-stationary time series.

Statement 2. The parameters for regression are the intercept and slope.

Statement 3 A regression trend line can have a positive or negative slope.

Statement 4. Variability due to the non-stationary nature of the time series is a trend component. Unexplained variability around the trend line is a random component.

Question 4.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 with a stationary or non-stationary time series.

Statement 2.The seasonal index approach can obtain forecasts without seasonal indexes.

Statement 3. The seasonal index approach cannot 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 5.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 trend 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 value of the last forecast multiplied by the parameter plus the value of the current data multiplied by one minus the parameter.

Question 6.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.