13.3
(a)
When
X
= 0, the estimated expected value of
Y
is 16.
(b)
For each increase in the value
X
by 1 unit, you can expect a decrease in an estimated 0.5 units in the value
of
Y
.
(c)
13
ˆ
Y
13.13
r
2
= 0.75. So, 75% of the variation in the dependent variable can be explained by the variation in the independent
variable.
13.15
Since
SST
=
SSR
+
SSE
and since
SSE
cannot be a negative number,
SST
must be at least as large as
SSR
.
13.17
(a)
r
2
= 0.9015. So, 90.15% of the variation in audit newsstand sales can be explained by the variation in
reported newsstand sales.
(b)
42.1859
YX
S
(c)
Based on (a) and (b), the model should be very useful for predicting audited sales.
13.35
(a)
b
0
= 169.455,
b
1
=
–
1.8579
(b)
Y
ˆ
= 76.56
(c)
“
No need to worry about plotting residuals because you won
’
t be asked to do that in the exam.
”
(d)
The DurbinWatson test statistic of 1.18<1.27. There is evidence of positive autocorrelation among the
residuals.
(e)
The plot of the residuals versus temperature indicates that positive residuals tend to occur for the lowest
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This note was uploaded on 05/31/2011 for the course MGT C06 taught by Professor A.stawinoga during the Fall '10 term at University of Toronto.
 Fall '10
 A.Stawinoga

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