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# Print Name : Tonature : 7( 3) Does Mat_ 3 significantly contribute to predicting Octane rating , when all the other predictors O are held constant ? I know the answers to these I just don't understand why or how these answers are acquired Print Name :
Tonature :
7( 3) Does Mat_ 3 significantly contribute to predicting Octane rating , when all the other predictors
O are held constant ?`
A . No , because it's p- value is larger than the significance level , and therefore its \$ coefficient is *
(B *). No , because its p- value is larger than the significance level , and therefore its \$ coefficient is =
\1000176}
5010
C . Yes , because its p- value is smaller than the significance level , and therefore its \$ coefficient is
to
D. Yes, because it's p- value is smaller than the significance level , and therefore its \$ coefficient is
= 0
E . Yes , because the calculated t-value is larger than the critical value , and therefore its \$ coefficient
is to
(4) What is the predicted value for Octane rating , when Mat _ 1 = 69.93 , Mat_ 2 = 0. 05 , Mat_ 3 = 55 ,
V
and Man_ condition = 1 .364 ?'
A . 91 . 721
SO
B * . 90. 644
C. 93. 338
D. 94 . 214
E. 95.853
[ 5) What is the percentage variation in Octane_ rating explained by this multiple linear regression
model ?
A . 44 . 146%
B. 95 . 164%
C. 95.853%
D* . 90. 563%
E. 78. 255 %

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