Essentials Of Business Analytics
1st Edition
ISBN: 9781285187273
Author: Camm, Jeff.
Publisher: Cengage Learning,
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Textbook Question
Chapter 5, Problem 28P
Donna Nickles manages a gasoline station on the corner of Bristol Avenue and Harpst Street in Arcata, California. Her station is a franchise, and the parent company calls her station every day at midnight to give her the prices for various grades of gasoline for the upcoming day. Over the past eight weeks Donna has recorded the price and sales (in gallons) of regular-grade gasoline at her station as well as the price of regular-grade gasoline charged by her competitor across the street. She is curious about the sensitivity of her sales to the price of regular gasoline she charges and the price of regular gasoline charged by her competitor across the street. She also wonders whether her sales differ systematically by day of the week and whether her station has experienced a trend in sales over the past eight weeks. The data collected by Donna for each day of the past eight weeks are provided in the tile GasStation.
- a. Construct a time series plot of daily sales, a
scatter plot of the price Donna charges for a gallon of regular gasoline and daily sales at Donna’s station, and a scatter plot of the price Donna’s competitor charges for a gallon of regular gasoline and daily sales at Donna’s station. What types of relationships exist in the data? - b. Use a multiple regression model with the price Donna charges for a gallon of regular gasoline and the price Donna’s competitor charges for a gallon of regular gasoline as causal variables to develop an equation to account for the relationships between these prices and Donna’s daily sales in the data. Based on this model, compute an estimate of sales for a day on which Donna is charging $3.50 for a gallon for regular gasoline and her competitor is charging $3.45 for a gallon of regular gasoline.
- c. Use a multiple linear regression model with the trend and dummy variables as follows to develop an equation to account for both trend and seasonal effects in the data:
Monday = 1 if the sales were recorded on a Monday. 0 otherwise;
Tuesday = 1 if the sales were recorded on a Tuesday, 0 otherwise;
M
Saturday = 1 if the sales were recorded on a Saturday, 0 otherwise;
Note that when the values of the six dummy variables are equal to 0, the observation corresponds to Sunday.
Based on this model, compute an estimate of sales for Tuesday of the first week after Donna collected her data.
- d. Use a multiple regression model with the price Donna charges for a gallon of regular gasoline and the price Donna’s competitor charges for a gallon of regular gasoline as causal variables and the trend and dummy variables from part (c) to create an equation to account for the relationships between these prices and daily sales as well as the trend and seasonal effects in the data. Based on this model, compute an estimate of sales for Tuesday of the first week after Donna collected her data a day if Donna is charging $3.50 for a gallon for regular gasoline and her competitor is charging $3.45 for a gallon of regular gasoline.
- e. Which of the three models you developed in parts (b), (c), and (d) is most effective? Justify your answer.
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The following table shows a data set containing information for 25 of the shadow stocks tracked by the American Association of Individual Investors (aaii.com, February 2002). Shadow stocks are common stocks of smaller companies that are not closely followed by Wall Street analysts.
Click on the datafile logo to reference the data.
DATA file
Company
DeWolfe Companies
Exchange
Ticker
Symbol
Market
Cap
($ millions)
Price/
Gross
Profit
Earnings
Ratio
Margin
(%)
AMEX
DWL
36.4
8.4
36.7
North Coast Energy
OTC
NCEB
52.5
6.2
59.3
Hansen Natural Corp.
OTC
HANS
41.1
14.6
44.8
MarineMax, Inc.
NYSE
HZO
111.5
7.2
23.8
Nanometrics Incorporated
OTC
NANO
228.6
38.0
53.3
TeamStaff, Inc.
OTC
TSTF
92.1
33.5
4.1
Environmental Tectonics
AMEX
ETC
51.1
35.8
35.9
Measurement Specialties
AMEX
MSS
101.8
26.8
37.6
SEMCO Energy, Inc.
NYSE
SEN
193.4
18.7
23.6
Party City Corporation
OTC
PCTY
97.2
15.9
36.4
Embrex, Inc.
OTC
EMBX
136.5
18.9
59.5
Tech/Ops Sevcon, Inc.
AMEX
ΤΟ
23.2
20.7
35.7
ARCADIS NV
OTC
ARCAF
173.4…
The following table shows a data set containing information for 25 of the shadow stocks tracked by the American Association of Individual Investors (aaii.com, February 2002). Shadow stocks are common stocks of smaller companies that are not closely followed by Wall Street analysts.
Click on the datafile logo to reference the data.
DATA file
Company
DeWolfe Companies
Exchange
AMEX
Ticker
Symbol
Market
Cap
Price/
Gross
Profit
Earnings Margin
($ millions)
Ratio
(%)
DWL
36.4
8.4
36.7
North Coast Energy
OTC
NCEB
52.5
6.2
59.3
Hansen Natural Corp.
OTC
HANS
41.1
14.6
44.8
MarineMax, Inc.
NYSE
HZO
111.5
7.2
23.8
Nanometrics Incorporated
OTC
NANO
228.6
38.0
53.3
TeamStaff, Inc.
OTC
TSTF
92.1
33.5
4.1
Environmental Tectonics
AMEX
ETC
51.1
35.8
35.9
Measurement Specialties
AMEX
MSS
101.8
26.8
37.6
SEMCO Energy, Inc.
NYSE
SEN
193.4
18.7
23.6
Party City Corporation
OTC
PCTY
97.2
15.9
36.4
Embrex, Inc.
OTC
EMBX
136.5
18.9
59.5
Tech/Ops Sevcon, Inc.
AMEX
ΤΟ
23.2
20.7
35.7
ARCADIS NV
OTC
ARCAF
173.4…
The following data show the year to date percent change (YTD % Change) for 30 stock-market indexes from around the word (The Wall Street Journal, August 26, 2013).
a. What index has the largest positive YTD % Change? Round your answer to once decimal place. index with a YTD % Change of %
b. Using a class width of 5 beginning with -20 and going to 40, develop a frequency distribution for the data.
YTD % Change
Frequency
-20 - -15
-15 - -10
-10 - -5
-5 - 0
0 - 5
5 - 10
10 - 15
15 - 20
20 - 25
30 - 35
c.
1.
2.
3.
4.
Chapter 5 Solutions
Essentials Of Business Analytics
Ch. 5 - Consider the following time series data:
Using...Ch. 5 - Refer to the time series data in Problem 1. Using...Ch. 5 - Problems 1 and 2 used different forecasting...Ch. 5 - Consider the following time series data:
Compute...Ch. 5 - Consider the following time series...Ch. 5 - Consider the following time series...Ch. 5 - Prob. 8PCh. 5 - Prob. 9PCh. 5 - Prob. 10PCh. 5 - For the Hawkins Company, the monthly percentages...
Ch. 5 - Corporate triple A bond interest rates for 12...Ch. 5 - The values of Alabama building contracts (in...Ch. 5 - The following time series shows the sales of a...Ch. 5 - Prob. 15PCh. 5 - The following table reports the percentage of...Ch. 5 - Consider the following time series: a. Construct a...Ch. 5 - Consider the following time series:
Construct a...Ch. 5 - The Seneca Children’s Fund (SCF) is a local...Ch. 5 - The president of a small manufacturing firm is...Ch. 5 - Consider the following time series: a. Construct a...Ch. 5 - Consider the following time series...Ch. 5 - The quarterly sales data (number of copies sold)...Ch. 5 - Prob. 25PCh. 5 - South Shore Construction builds permanent docks...Ch. 5 - Hogs & Dawgs is an ice cream parlor on the border...Ch. 5 - Donna Nickles manages a gasoline station on the...Ch. 5 - The Vintage Restaurant, on Captiva Island near...
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