Statistics for Business & Economics, Revised (MindTap Course List)
12th Edition
ISBN: 9781285846323
Author: David R. Anderson, Dennis J. Sweeney, Thomas A. Williams, Jeffrey D. Camm, James J. Cochran
Publisher: South-Western College Pub
expand_more
expand_more
format_list_bulleted
Concept explainers
Videos
Textbook Question
Chapter 17, Problem 53SE
Refer to the Hudson Marine problem in exercise 52. Suppose the quarterly sales values for the seven years of historical data are as follows.
| Year | Quarter 1 | Quarter 2 | Quarter 3 | Quarter 4 | Total Yearly Sales |
| 1 | 6 | 15 | 10 | 4 | 35 |
| 2 | 10 | 18 | 15 | 7 | 50 |
| 3 | 14 | 26 | 23 | 12 | 75 |
| 4 | 19 | 28 | 25 | 18 | 90 |
| 5 | 22 | 34 | 28 | 21 | 105 |
| 6 | 24 | 36 | 30 | 20 | 110 |
| 7 | 28 | 40 | 35 | 27 | 130 |
- a. Use the following dummy variables to develop an estimated regression equation to account for any season and linear trend effects in the data: Qtr1 = 1 if Quarter 1, 0 otherwise; Qtr2 = 1 if Quarter 2, 0 otherwise; and Qtr3 = 1 if Quarter 3, 0 otherwise.
- b. Compute the quarterly forecasts for next year.
Expert Solution & Answer
Want to see the full answer?
Check out a sample textbook solution
Students have asked these similar questions
Do students with higher college grade point averages (GPAs) earn more than those graduates with lower GPAs?† Consider the following hypothetical college GPA and salary data (10 years after graduation).
GPA
Salary ($)
2.22
72,000
2.29
48,000
2.57
72,000
2.59
64,000
2.77
88,000
2.85
98,000
3.12
133,000
3.35
130,000
3.66
157,000
3.68
162,000
Use these data to develop an estimated regression equation that can be used to predict annual salary 10 years after graduation given college GPA. (Let x = GPA, and let y = salary (in $). Round your numerical values to the nearest integer.)
ŷ =
Find the value of the test statistic. (Round your answer to two decimal places.)
=
Find the p-value. (Round your answer to three decimal places.)
p-value =
Do students with higher college grade point averages (GPAs) earn more than those graduates with lower GPAs?† Consider the following hypothetical college GPA and salary data (10 years after graduation).
GPA
Salary ($)
2.22
72,000
2.27
48,000
2.57
72,000
2.59
62,000
2.77
86,000
2.85
96,000
3.12
133,000
3.35
130,000
3.66
157,000
3.68
162,000
#1) Use these data to develop an estimated regression equation that can be used to predict annual salary 10 years after graduation given college GPA. (Let x = GPA, and let y = salary (in $). Round your numerical values to the nearest integer.) ŷ =
#2) Find the value of the test statistic. (Round your answer to two decimal places.)
#3)Find the p-value. (Round your answer to three decimal places.)
p-value =
The following table shows data for the cost of natural gas in Maryland (in dollars per Million Btu) for x years since 1990.
Year
x
Price in $ per Million Btu
1990
6.31
1991
6.14
1992
6.32
1993
6.75
1994
6.8
1995
6.34
1996
7.46
1997
8.12
1998
8.04
1999
8.25
2000
9.58
2001
11.28
2002
9.25
a. Define the explanatory and response variables for this problem.
b. Use the calculator to obtain the linear regression line of best fit for the original prices; round to three decimal places. Write this prediction equation in the form:
a. In a brief sentence, interpret the slope in the context of the problem.
Chapter 17 Solutions
Statistics for Business & Economics, Revised (MindTap Course List)
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...
Knowledge Booster
Learn more about
Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.Similar questions
- Olympic 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_forwardThe following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.arrow_forwardConsider the following time series: Quarter Year 1 Year 2 Year 3 1 66 63 57 2 48 40 50 3 59 61 54 4 73 76 67 (b) Use a multiple linear regression model with dummy variables as follows to develop an equation to account for seasonal effects in the data: Qtr1 = 1 if quarter 1, 0 otherwise; Qtr2 = 1 if quarter 2, 0 otherwise; Qtr3 = 1 if quarter 3, 0 otherwise. For subtractive or negative numbers use a minus sign even if there is a + sign before the blank. (Example: -300) ŷ = + Qtr1 + Qtr2 + Qtr3 (c) Compute the quarterly forecasts for next year. Year Quarter Ft 4 1 4 2 4 3 4 4arrow_forward
- The operations manager of a musical instrument distributor feels that the demand for Bass Drums may be related to the number of television appearances by the popular rick group Green Shades during the previous month. The manager has collected the data shown in the following table. Demand for Bass Drums 3 6 7 5 10 8 Green Shades TV appearances 3 4 7 6 8 5 Develop the linear regression equation to forecast. Forecast demand for Bass Drums when Green Shades’ TV appearances are 10. Compute MSE and standard deviation for Problem 8.arrow_forwardThe following table shows the annual number of PhD graduates in a country in various fields. NaturalSciences Engineering SocialSciences Education 1990 70 10 70 30 1995 130 40 110 50 2000 330 130 280 140 2005 490 370 460 210 2010 590 550 830 520 2012 690 590 1,000 900 (a) With x = the number of social science doctorates and y = the number of education doctorates, use technology to obtain the regression equation. (Round coefficients to three significant digits.) y(x) = Graph the associated points and regression line. (b) What does the slope tell you about the relationship between the number of social science doctorates and the number of education doctorates? The slope tells us the increase in the number of social science doctorates for each additional education doctorate.The slope tells us the increase in the number of education doctorates for each additional social science doctorate. The slope tells us the decrease in the number…arrow_forwardThe following table shows the annual number of PhD graduates in a country in various fields. NaturalSciences Engineering SocialSciences Education 1990 70 10 60 30 1995 130 40 120 50 2000 330 130 280 140 2005 490 370 460 210 2010 590 550 830 520 2012 690 590 1,000 900 (a) With x = the number of social science doctorates and y = the number of education doctorates, use technology to obtain the regression equation. (Round coefficients to three significant digits.) y(x) = Graph the associated points and regression line. (b) What does the slope tell you about the relationship between the number of social science doctorates and the number of education doctorates? The slope tells us the increase in the number of education doctorates for each additional social science doctorate.The slope tells us the decrease in the number of education doctorates for each additional social science doctorate. The slope tells us the increase in the number…arrow_forward
arrow_back_ios
arrow_forward_ios
Recommended textbooks for you
Linear Algebra: A Modern IntroductionAlgebraISBN:9781285463247Author:David PoolePublisher:Cengage Learning
College AlgebraAlgebraISBN:9781305115545Author:James Stewart, Lothar Redlin, Saleem WatsonPublisher:Cengage Learning
Functions and Change: A Modeling Approach to Coll...AlgebraISBN:9781337111348Author:Bruce Crauder, Benny Evans, Alan NoellPublisher:Cengage Learning
Linear Algebra: A Modern Introduction
Algebra
ISBN:9781285463247
Author:David Poole
Publisher:Cengage Learning
College Algebra
Algebra
ISBN:9781305115545
Author:James Stewart, Lothar Redlin, Saleem Watson
Publisher:Cengage Learning
Functions and Change: A Modeling Approach to Coll...
Algebra
ISBN:9781337111348
Author:Bruce Crauder, Benny Evans, Alan Noell
Publisher:Cengage Learning
Correlation Vs Regression: Difference Between them with definition & Comparison Chart; Author: Key Differences;https://www.youtube.com/watch?v=Ou2QGSJVd0U;License: Standard YouTube License, CC-BY
Correlation and Regression: Concepts with Illustrative examples; Author: LEARN & APPLY : Lean and Six Sigma;https://www.youtube.com/watch?v=xTpHD5WLuoA;License: Standard YouTube License, CC-BY