Question 1 (3 points)
A linear trend means that the time series variable changes by a
Question 1 options:
|positive amount each time period|
|constant amount each time period|
|constant percentage each time period|
|negative amount each time period|
The forecast error is the difference between
Question 2 options:
|the actual value and the forecast|
|this period’s value and the next period’s value|
|the explanatory variable value and the response variable value|
|the average value and the expected value of the response variable|
The data below represents sales, in units, for a particular product. If you were to use the moving average method with a span of 4 periods, what would be your forecast for period 5?
Question 3 options:
Suppose that a simple exponential smoothing model is used (with = 0.30) to forecast monthly sandwich sales at a local sandwich shop. After June’s demand is observed at 1520 sandwiches, the forecasted demand for June is 1600 sandwiches. What would be the forecasted demand for July?
Question 4 options:
Which of the following is not one of the summary measures for forecast errors that is commonly used?
Question 5 options:
|MAPE (mean absolute percentage error)|
|MFE (mean forecast error)|
|RMSE (root mean square error)|
|MAE (mean absolute error)|
When using the moving average method, you must select ______ which represent(s) the number of terms in the moving average.
Question 6 options:
|a smoothing constant|
|an alpha value|
|the explanatory variables|
Holt’s model differs from simple exponential smoothing in that it includes a term for:
Question 7 options:
A moving average is the average of the observations in the past few periods, where the number of terms in the average is the span.
Question 8 options:
The cyclical component of a time series measures the over-all general directional movement over a long period of time.
Question 9 options:
If the observations of a time series increase or decrease regularly through time, we say that the time series has a random (or noise) component.
Question 10 options:
A trend component of a time series is a long-term direction exhibited by a series.
Question 11 options:
To calculate the five-period moving average for a time series, we average the values in the two preceding periods, and the values in the three following time periods.
Question 12 options:
Simple exponential smoothing is appropriate for a series without a pronounced trend or seasonality.
Question 13 options:
The difference between the actual data value and the forecasted data value is called the forecast error.
Question 14 options:
In the case of simple exponential smoothing, a smoothing constant, alpha, close to 1 places more weight on the prior forecast.
Question 15 options:
A mean absolute error value of zero means that the forecast is exactly accurate and there is no forecast error.
Question 16 options:
Which of the two scatterplots below (A or B) displays the stronger linear relationship between x and y.
Question 17 options:
|Both A and B have the same strength of linear relationship.|
The measure of forecast accuracy that is not influenced by the measurement scale of the time series data is
Question 18 options:
A stationary forecasting model is appropriate for a time series which exhibits primarily:
Question 19 options:
Suppose that simple exponential smoothing with =0.30 is used to forecast monthly Pepsi sales at a small grocery store. After March’s demand is observed, the forecasted demand for April is 5000 cans of Pepsi. Suppose that actual demands during April and May are as follows: April 5500 cans; May 4500 cans. After observing May’s demand, what is the forecast for June’s demand?
Question 20 options:
Suppose that simple exponential smoothing with = 0.40 is used to forecast monthly wine sales at a liquor store. After April’s demand is observed, the forecasted demand for May is 4500 bottles of wine. The actual demands during May and June are as follows: May, 5000 bottle of wine; June 4000 bottle of wine. Note that the demands during May and June average (5000+4000)/2 = 4500 bottle per month. This is the same as the forecast for monthly sales before we observed the May and June data. Yet after we observe the May and June demands for wine, our forecast for July demand has decreased from what it was at the end of April. (See below.) Why?
Question 21 options:
Given the following data about the number of wrecks that have occurred at the intersection of Green and Main Street during the past six-month period, January (10) February (15) March (12) April (20) May (18) June (24) and = .20, the simple exponential smoothing forecast for July is: (Enter the value rounded to the nearest whole number. Do not use a decimal, words, symbols or other marks)
Question 22 options:
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