Statistics for Business & Economics
12th Edition
ISBN: 9781285528830
Author: David R. Anderson
Publisher: Cengage Learning US
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Textbook Question
Chapter 17.5, Problem 34E
Three years of monthly lawn-maintenance expenses ($) for a six-unit apartment house in southern Florida follow.
- a. Construct a time series plot. What type of pattern exists in the data?
- b. Develop an estimated regression equation that can be used to account for any seasonal and linear trend effects in the data. Use the following dummy variables to account for the seasonal effects in the data: Jan = 1 if January, 0 otherwise; Feb = 1 if February, 0 otherwise; Mar = 1 if March, 0 otherwise; … Nov = 1 if November, 0 otherwise. Note that using this coding method, when all the 11 dummy variables are 0, the observation corresponds to an expense in December.
- c. Compute the monthly forecasts for next year based upon both trend and seasonal effects.
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Statistics for Business & Economics
Ch. 17.2 - Consider the following time series data. Week 1 2...Ch. 17.2 - Refer to the time series data in exercise 1. Using...Ch. 17.2 - Exercises 1 and 2 used different forecasting...Ch. 17.2 - Consider the following time series data. Month 1 2...Ch. 17.3 - Consider the following time series data. Week 1 2...Ch. 17.3 - Consider the following time series data. Month 1 2...Ch. 17.3 - Refer to the gasoline sales time series data in...Ch. 17.3 - Refer again to the gasoline sales time series data...Ch. 17.3 - With the gasoline time series data from Table...Ch. 17.3 - With a smoothing constant of = .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 - The U.S. Census Bureau tracks the median price for...Ch. 17.4 - Consider the following time series data. a....Ch. 17.4 - Prob. 18ECh. 17.4 - Consider the following time series. a. Construct a...Ch. 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 - FRED (Federal Reserve Economic Data), a database...Ch. 17.4 - Automobile unit sales at B. J. Scott Motors, Inc.,...Ch. 17.4 - Giovanni Food Products produces and sells frozen...Ch. 17.4 - Prob. 27ECh. 17.5 - Consider the following time series. a. Construct a...Ch. 17.5 - Consider the following time series data. a....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 - Three years of monthly lawn-maintenance expenses...Ch. 17.6 - Consider the following time series data. a....Ch. 17.6 - Refer to exercise 35. a. 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 - Prob. 44SECh. 17 - Prob. 45SECh. 17 - The Mayfair Department Store in Davenport, Iowa,...Ch. 17 - Canton Supplies, Inc., is a service firm that...Ch. 17 - The Costello Music Company has been in business...Ch. 17 - Consider the Costello Music Company problem in...Ch. 17 - Refer to the Costello Music Company problem in...Ch. 17 - Refer to the Costello Music Company time series in...Ch. 17 - Hudson Marine has been an authorized dealer for CD...Ch. 17 - Refer to the Hudson Marine problem in exercise 52....Ch. 17 - Refer to the Hudson Marine problem in exercise 53....Ch. 17 - Refer to the Hudson Marine data in exercise 53. a....Ch. 17 - Forecasting Food and Beverage Sales The Vintage...Ch. 17 - Forecasting Lost Sales The Carlson Department...
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- Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4arrow_forwardDoes Table 2 represent a linear function? If so, finda linear equation that models the data.arrow_forwardOlympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?arrow_forward
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