The runs test is a formal test of the null hypothesis of randomness

Part 1 of 1 – Multiple Choice

Question 1 of 10

The runs test is a formal test of the null hypothesis of randomness. If there are too many or too few runs in the series, then we conclude that the series is not random.

•A. True

•B. False•          

Question 2 of 10

A linear trend means that the time series variable changes by a:

•A. constant amount each time period

•B. constant percentage each time period

•C. positive amount each time period

•D. negative amount each time period

Question 3 of 10

Which of the following is not one of the techniques that can be used to identify whether a time series is truly random?

•A. a graph (plot the data)

•B. the runs test

•C. a control chart

•D. the autocorrelations (or a correlogram)

Question 4 of 10

The forecast error is the difference between:

•A. this period’s value and the next period’s value

•B. the average value and the expected value of the response variable

•C. the explanatory variable value and the response variable value

•D. the actual value and the forecast value

Question 5 of 10

The runs test uses a series of 0’s and 1’s. The 0’s and 1’s typically represent whether each observation is:

•A. above or below the predicted value of Y

•B. above or below the mean value of Y

•C. is above or below the mean value of the previous two observations

•D. is positive or negative

Question 6 of 10

A time series is any variable that is measured over time in sequential order.

•A. True

•B. False

Question 7 of 10

In a random series, successive observations are probabilistically independent of one another. If this property is violated, the observations are said to be:

•A. autocorrelated

•B. intercorrelated

•C. causal

•D. seasonal

Question 8 of 10

The purpose of using the moving average is to take away the short-term seasonal and random variation, leaving behind a combined trend and cyclical movement.

•A. True

•B. False

Question 9 of 10

An exponential trend is appropriate when the time series changes by a constant percentage each period.

•A. True

•B. False

Question 10 of 10

Which of the following is not a method for dealing with seasonality in data?

•A. Winter’s exponential smoothing model

•B. deseasonalizing the data, using any forecasting model, then reseasonalizing the data

•C. multiple regression with lags for the seasons

•D. multiple regression with dummy variables for the seasons