EBK OPERATIONS MANAGEMENT
14th Edition
ISBN: 9781260718447
Author: Stevenson
Publisher: MCG COURSE
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Textbook Question
Chapter 3, Problem 26P
The following data were collected during a study of consumer buying patterns:
1. Plot the data.
2. Obtain a linear regression line foe the data.
3. What percentage of the variation is explained by the regression line?
4. Use the equation determined in part b to predict the expected value of y for x = 41.
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The following multiple regression printout can be used to predict a person's height (in inches) given his or her shoe size and gender, where gender = 1 for males and 0 for females.
Regression Analysis: Height Versus Shoe Size,
Gender
Coefficients
Term
Coef
Constant
55.28
SE Coef
1.04
T-Value
P-Value
Shoe Size
0.105
Gender
0.268
0.12
0.489
53.1
0.875
0.000
0.000
0.548
0.000
(a) The dependent variable in this regression is which of the following?
height
gender
shoe size
constant
(b)
What is the regression coefficient of shoe size?
(c) What is the regression coefficient of gender?
The following time series represents the number of automobiles sold by a car dealership each of the past five months.
t
1
2
3
4
5
Yt
7
12
10
13
14
(a) Construct a time series plot.
What type of pattern exists in the data?
The time series plot shows a linear trend.The time series plot shows a horizontal pattern. The time series plot shows a seasonal pattern.The time series plot shows a nonlinear trend.
(b)
Use simple linear regression analysis to find the parameters for the line that minimizes MSE for this time series.
t =
(c)
What is the forecast for
t = 6?
Answer each of the following two problems (1) using regression analysis (easiest way is using the scatter
diagram approach. But you can also use the regression analysis routine in the Data Analysis module in
Excel
Bus and subway ridership in Washington, D.C., dur-
ing the summer months is believed to be heavily tied
to the number of tourists visiting the city. During
the past 12 years, the following data have been
obtained:
YEAR
1
2
3
4
5
6
7
8
9
10
11
12
NUMBER
OF TOURISTS
(1,000,000s)
7
2
6
4
14
15
16
12
14
20
15
7
RIDERSHIP
(100,000s)
15
10
13
15
25
27
24
20
27
44
34
17
(a) Plot these data and determine if a linear model is
reasonable.
(b) Develop a regression model
(c) What is expected ridership if 10 million tourists
visit the city?
(d) If there are no tourists at all, explain the predicted
ridership.
Chapter 3 Solutions
EBK OPERATIONS MANAGEMENT
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