Modern Business Statistics with Microsoft Office Excel (with XLSTAT Education Edition Printed Access Card) (MindTap Course List)
6th Edition
ISBN: 9781337115186
Author: David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran
Publisher: Cengage Learning
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Question
Chapter 17.3, Problem 8E
a.
To determine
Find the three-week weighted moving averages.
b.
To determine
Calculate MSE for three-week weighted moving averages.
Check whether a person can prefer the weighted moving average to the unweighted moving average.
c.
To determine
Explain whether it is possible to always find the set of weights that makes the least MSE for weighted moving averages than the unweighted moving averages.
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Consider the following gasoline sales time series data. Click on the datafile logo to reference the data.
Week
Sales (1000s of gallons)
1
16
2
20
3
20
4
23
5
18
6
17
8
19
9
23
10
19
11
14
12
21
a. Using a weight of 1/2 for the most recent observation, 1/3 for the second most recent observation, and 1/6 the most recent observation, compute a three-week weighted moving average for the time series (to 2 decimals). Enter negative values as negative numbers.
Week
Time-Series Value
Weighted MovingAverage Forecast
ForecastError
(Error)2
1
2
3
4
5
6
7
8
9
10
11
12
Total
b. Compute the MSE for the weighted moving average in part (a).MSE =
Do you prefer this weighted moving average to the unweighted moving average? Remember that…
consider the following time series data.Month 1 2 3 4 5 6 7Value 24 13 20 12 19 23 15a. compute MSe using the most recent value as the forecast for the next period. Whatis the forecast for month 8?b. compute MSe using the average of all the data available as the forecast for the nextperiod. What is the forecast for month 8?c. Which method appears to provide the better forecast?
The following data set provides the total number of shipments of core major household appliances in the U.S. from 2000 to 2016 (in millions):
Year
Shipments (millions)
2000
38.4
2001
38.2
2002
40.8
2003
42.5
2004
46.1
2005
47.0
2006
46.7
2007
44.1
2008
39.8
2009
36.5
2010
38.2
2011
36.0
2012
35.8
2013
39.2
2014
41.5
2015
42.9
2016
44.7
a. Plot the time series.
b. Fit a three-year moving average to the data and plot the results.
c. Fit a five-year moving average to the data and plot the results.
d. Compute a linear trend forecasting equation and plot the trend line.
e. Compute a quadratic trend forecasting equation and plot the results.
Chapter 17 Solutions
Modern Business Statistics with Microsoft Office Excel (with XLSTAT Education Edition Printed Access Card) (MindTap Course List)
Ch. 17.2 - 1. Consider the following time series...Ch. 17.2 - 2. Refer to the time series data in exercise 1....Ch. 17.2 - Prob. 3ECh. 17.2 - 4. Consider the following time series...Ch. 17.3 - Consider the following time series...Ch. 17.3 - Consider the following time series...Ch. 17.3 - Refer to the gasoline sales time series data in...Ch. 17.3 - Prob. 8ECh. 17.3 - 9. With the gasoline time series data from Table...Ch. 17.3 - 10. With a smoothing constant of α = .2, equation...
Ch. 17.3 - For the Hawkins Company, the monthly percentages...Ch. 17.3 - Corporate triple-A bond interest rates for 12...Ch. 17.3 - The values of Alabama building contracts (in $...Ch. 17.3 - The following time series shows the sales of a...Ch. 17.3 - Ten weeks of data on the Commodity Futures Index...Ch. 17.3 - Prob. 16ECh. 17.4 - Consider the following time series...Ch. 17.4 - Prob. 18ECh. 17.4 - Prob. 19ECh. 17.4 - Prob. 20ECh. 17.4 - Prob. 21ECh. 17.4 - Prob. 22ECh. 17.4 - The president of a small manufacturing firm is...Ch. 17.4 - The following data shows the average interest rate...Ch. 17.4 - Quarterly revenue ($ millions) for Twitter for the...Ch. 17.4 - Giovanni Food Products produces and sells frozen...Ch. 17.4 - The number of users of Facebook from 2004 through...Ch. 17.5 - Consider the following time series.
Construct a...Ch. 17.5 - Consider the following time series...Ch. 17.5 - The quarterly sales data (number of copies sold)...Ch. 17.5 - Air pollution control specialists in southern...Ch. 17.5 - South Shore Construction builds permanent docks...Ch. 17.5 - Prob. 33ECh. 17.5 - Prob. 34ECh. 17.6 - Consider the following time series...Ch. 17.6 - Refer to exercise 35.
Deseasonalize the time...Ch. 17.6 - The quarterly sales data (number of copies sold)...Ch. 17.6 - Three years of monthly lawn-maintenance expenses...Ch. 17.6 - Air pollution control specialists in southern...Ch. 17.6 - Electric power consumption is measured in...Ch. 17 - The weekly demand (in cases) for a particular...Ch. 17 - The following table reports the percentage of...Ch. 17 - United Dairies, Inc., supplies milk to several...Ch. 17 - Annual retail store revenue for Apple from 2007 to...Ch. 17 - The Mayfair Department Store in Davenport, Iowa,...Ch. 17 - Prob. 47SECh. 17 - The Costello Music Company has been in business...Ch. 17 - Consider the Costello Music Company problem in...Ch. 17 - Prob. 50SECh. 17 - Refer to the Costello Music Company time series in...Ch. 17 - Prob. 52SECh. 17 - Refer to the Hudson Marine problem in exercise 52....Ch. 17 - Refer to the Hudson Marine problem in exercise...Ch. 17 - Refer to the Hudson Marine data in exercise...Ch. 17 - Forecasting Food and Beverage Sales
The Vintage...Ch. 17 - The Carlson Department Store suffered heavy damage...
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- Refer to the gasoline sales time series data in the given table. Compute four-week and five-week moving averages for the time series. Round your answers to two decimal places. Compute the MSE for the four-week and five-week moving average forecasts. Round your intermediate calculations and final answers to two decimal places. What appears to be the best number of weeks of past data (three, four, or five) to use in the moving average computation? Recall that MSE for the three-week moving average is 12.89.arrow_forwardConsider the following time series data. Week 1 2 3 4 5 6 Value 18 13 16 11 17 14 Construct a time series plot. What type of pattern exist in the data? Develop a three-week moving average for this time series. Compute MSE and forecast for week 7. Use a = 0.2 to compute the exponential smoothing values for the time series. Compute MSE and forecast for week 7.arrow_forwardConsider the following gasoline sales time series data. Click on the datafile logo to reference the data. Week. Sales (1000s of gallons) 1 16 2. 22 3. 18 4. 23 5. 18 6. 17 7 21 8 17 9 21 10. 21 11 14 12 23 Using a weight of 1/2 for the most recent observation, 1/3 for the second most recent observation, and third the most recent observation, 1/6 compute a three-week weighted moving average for the time series (to 2 decimals). Enter negative values as negative numbers. Week Time-Series Value Weighted MovingAverage Forecast ForecastError (Error)2 Total b.…arrow_forward
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