BUS 660 Week 2 Time Series Analysis and Forecasting – Homework

(BUS 660 Week 2 Time Series Analysis, BUS 660 Week 2 Time Series Analysis)

Problem 6-09: With the gasoline time series data from the … table, show the exponential smoothing forecasts using a = 0.1.

1. Applying the MSE measure of forecast accuracy, would you prefer a smoothing constant of a = 0.1 or a = 0.2 for the gasoline sales time series? Do not round your interim computations and round your final answers to three decimal places.
2. Are the results the same if you app y MAE as the measure of accuracy? Do not round your interim computations and round your final answers to three decimal places.
3. What are the results if MAPE is used? Do not round your interim computations and round your final answers to two decimal places.

Problem 6-25: Consider the following time series data

1. What type of pattern exists in the data?
2. Use a multiple regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data. Qtrl = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; Qtr3 = 1 if Quarter 3, 0 otherwise. If required, round your answers to three decimal places. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300)
3. Compute the quarterly forecasts for next year. If required, round your answers to two decimal places.

Problem 6-01 (Algorithmic): Consider the following time series data. Using the naive method (most recent value) as the forecast for the next week, compute the following measures of forecast accuracy.

1. Mean absolute error. If required, round your answer to one decimal place.
2. Mean squared error. If required, round your answer to one decimal place.
3. Mean absolute percentage error. If required, round your intermediate calculations and final answer to two decimal places.
4. What is the forecast for week 7? If required, round your answer to two decimal place.

Problem 6-23 (Algorithmic): The medical community unanimously agrees on the health benefits of regular exercise, but are adults listening? During each of the past 15 years, a polling organization has surveyed americans about their exercise habits. In the most recent of these polls, slightly over half of all American adults … that they exercise for 30 or more minutes at least three times per week. The following data show the percentages of adults who reported that they exercise for 30 or more minutes at least three times per week during each of the 15 years of this study.

1. Does a linear trend appear to be present?
2. use simple linear regression to find the parameters for the line that minimizes MSE for this time series. Do not round your interim computations and round your final answers to four decimal places. For subtractive or negative numbers use a minus sign. (Example: -300)
3. Use the trend equation from part (b) to forecast the percentage of adults next year (year 16 of the study) who will report that they exercise for 30 or more minutes at least three times per week. Do not round your interim computations and round your final answers to four decimal places. For subtractive or negative numbers use a minus sign. (Example: -300)
4. Use the trend equation from part (b) to forecast the percentage of adults three years from now (year 18 of the study) who will report that they exercise for 30 or more minutes at least three times per week. Do not round your interim computations and round your final answers to four decimal places. For subtractive or negative numbers use a minus sign. (Example: -300)

Problem 6-19: Consider the following time series data

1. Choose the correct time series plot. What type of pattern exists in the data?
2. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series. Do not round your interim computations and round your final answers to three decimal places. For subtractive or negative numbers use a minus sign. (Example: -300)
3. What is the forecast for t = 8? If required, round your answer to three decimal places.

Problem 6-21 (Algorithmic): The Centers for Disease Control and Prevention Office on Smoking and Health (OSH) is the lead federal agency responsible for comprehensive tobacco prevention and control. OSH was established in 1965 to reduce the death and disease caused by tobacco use and exposure to secondhand smoke. One of the many responsibilities of the OSH is to collect data on tobacco use. The following data show the percentage of U.S. adults who were users of tobacco for a recent 11-year period (http://www.cdc.govitobacco/data_statisticsitables/trends/cig_smoking/index.htm).

1. Choose the correct time series plot. What type of pattern exists in the data?
2. Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series. Do not round your interim computations and round your final answers to three decimal places. For subtractive or negative numbers use a minus sign. (Example: -300)
3. One of OSH’s goals is to cut the percentage of U.S. adults who were users of tobacco to 12% or less within nine years of the last year of these data. Does your regression model from part (b) suggest that OSH is on target to meet this goal? Use your model from part (b) to estimate the number of years that must pass after these data have been collected before OSH will achieve this goal. Round your answer to the nearest whole number.

Problem 6-11: For the Hawkins Company, the monthly percentages of all shipments received on time over the past 12 months are 80, 82, 84, 83, 83, 84, 85, 84, 82, 83, 84, and 83.

1. Choose the correct time series plot.
2. Compare a three-month moving average forecast with an exponential smoothing forecast for a = 0.2. Which provides the better forecasts using MSE as the measure of model accuracy? Do not round your interim computations and round your final answers to three decimal places.
3. What is the forecast for next month? If required, round your answer to two decimal places.

Problem 6-05: Consider the following time series data

1. Choose the correct time series plot. What type of pattern exists in the data?
2. Develop a three-week moving average for this time series. Compute MSE and a forecast for week 7. If required, round your answers to two decimal places.
3. Use a = 0.2 to compute he exponential smoothing values for the time series. Compute MSE and a forecast for week 7. If required, round your answers to two decimal places.
4. Compare the three-week moving average forecast with the exponential smoothing forecast using a = 0.2. Which appears to provide the better forecast … on MSE?
5. Use trial and error to find a value of the exponential smoothing coefficient a that results in a smaller MSE than what you calculated for a = 0.2.