MAT540
MAT540
Week 4 Homework
Chapter 15
1.      The manager of
the Carpet City outlet needs to make an accurate forecast of the demand for
Soft Shag carpet (its biggest seller). If the manager does not order enough
carpet from the carpet mill, customer 
will  buy  their 
carpet  from  one 
of  Carpet  City’s 
many  competitors.  The 
manager  has
collected the following demand data for the past 8 months:
Month
Demand for Soft Shag
Carpet (1,000 yd.)
1                                                  
10
2                                                   
9
3                                                   
8
4                                                   
9
5                                                  
10
6                                                  
12
7                                                  
14
8 11
a.    Compute a 3-month moving average forecast
for months 4 through 9.
b.    Compute 
a  weighted  3-month 
moving  average  forecast 
for  months  4 
through  9.  Assign weights  of 
0.55, 0.35, and 0.10 to the 
months in sequence, starting with 
the  most  recent month.
c.    Compare the two forecasts by using MAD.
Which forecast appears to be more accurate?
2.      The  manager 
of  the  Petroco 
Service  Station  wants 
to forecast  the  demand 
for  unleaded  gasoline next month so that the proper number
of gallons can be ordered from the distributor. The owner has accumulated  the 
following  data  on 
demand  for  unleaded 
gasoline  from  sales 
during  the  past 
10 months:
Month                                       Gasoline
Demanded (gal.) October                                                       
775
November 835
December                                                   
605
January                                                      
450
February                                                    
600
March                                                         
700
April                                                            
820
May                                                             
925
June
July
1500
1200
a.    Compute an exponential smoothed forecast,
using an α value of 0.4
b. Compute the MAD.
3.      Emily  Andrews 
has  invested  in 
a  science  and 
technology  mutual  fund. 
Now  she  is 
considering liquidating and investing in another fund. She would like to
forecast the price of the science and technology fund for the next month before
making a decision. She has collected the following data on the average price of
the fund during the past 20 months:
Month                                        Fund
Price
1                                               
$55 ¾
2                                                
54 ¼
3                                               
55 1/8
4                                               
58 1/8
5                                               
53 3/8
6                                               
51 1/8
7                                                
56 ¼
8                                               
59 5/8
9                                                
62 ¼
10                                               
59 ¼
11                                              
62 3/8
12                                              
57 1/1
13                                              
58 1/8
14                                               
62 ¾
15                                               
64 ¾
16 66 1/8
17                                               
68 ¾
18                                               
60.5
19                                             
65.875
20                                              
72.25
a.    Using a 3-month average, forecast the fund
price for month 21.
b.    Using a 3-month weighted average with the
most recent month weighted 0.5, the next most recent month weighted 0.30, and
the third month weighted 0.20, forecast the fund price for month 21.
c.   Compute an
exponentially smoothed forecast, using α=0.3, and forecast the fund price for
month 21.
d.    Compare the forecasts in (a), (b), and (c),
using MAD, and indicate the most accurate.
4.      Carpet City wants to develop
a means to forecast its carpet sales. The store manager believes that the
store’s sales are directly related to the number of new housing starts in town.
The manager has gathered data from county records on monthly house construction
permits and from store records
on monthly sales. These data are as follows:
Monthly
Carpet Sales
(1,000 yd.)
Monthly
Construction
Permits
9                                                                    
17
14                                                                  
25
10 8
12                                                                   
7
15                                                                  
14
9                                                                     
7
24                                                                  
45
21                                                                  
19
20 28
29                                                                  
28
a.   Develop 
a linear  regression  model 
for these  data and  forecast 
carpet  sales if 30  construction permits for new homes are filed.
b.    Determine 
the  strength  of  the  causal 
relationship  between  monthly 
sales  and  new 
home construction by using correlation.
5.       The manager of Gilley’s Ice Cream Parlor
needs an accurate forecast of the demand for ice cream.
The store orders ice
cream from a distributor a week ahead; if the store orders too little, it loses
business, and  if  it 
orders too  much,  the 
extra  must  be 
thrown away.  The  manager 
belives that  a major determinant
of ice cream sales is temperature (i.e.,the hotter the weather, the more ice
cream people buy). Using an almanac, the manager has determined the average day
time temperature for
14 weeks, selected at random, and from store records
he has determined the ice cream consumption
for the same 14 weeks. These data are
summarized as follows:
Week
Average Temperature
(Degrees)
Ice Cream Sold
(gal.)
1                                           
68                                               
80
2                                           
70                                              
115
3                                           
73                                                91
4                                           
79                                               
87
5                                           
77                                              
110
6                                           
82                                              
128
7                                           
85                                              
164
8                                           
90                                               178
9                                           
85                                              
144
10                                          
92                                              
179
11                                           90                                              
144
12                                          
95                                              
197
13                                          
80                                              
144
14                                           75                                              
123
a.   Develop a linear regression model for these
data and forecast the ice cream consumption if the average weekly daytime
temperature is expected to be 85 degrees.
b.    Determine  
the   strength   of   the   linear  
relationship   between   temperature  
and   ice   cream consumption by using correlation.
c.    What is the coefficient of determination?
Explain its meaning.
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