Choose the correct computed and table test statistics Region 1 and for Ho;BRegion1= 0; O Fc= 6.9 O Table T.025,4,52 and T.025,4,52 O Tc= 12.51
Choose the correct computed and table test statistics Region 1 and for Ho;BRegion1= 0; O Fc= 6.9 O Table T.025,4,52 and T.025,4,52 O Tc= 12.51
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
Related questions
Question

Transcribed Image Text:Choose the correct computed and table test statistics Region 1 and for
Ho;BRegion1= 0;
O Fc= 6.9
O Table = T.025,4,52 and T.O25,4,52
O Tc= 12.51
O Tc=1.93
Choose the best recommendation for ABLine that minimizes Excess space
O Do not spend money using ABLine versus other carriers to minimize excess space.
O Choose ABLine and Region 2 in order to minimizes excess space.
O Choose other carriers rather than ABLine to minimize Excess space
O Choose ABLine in order to reduce excess space since ABLine has lower Excess space
than other carriers.
Choose the best recommendation for Region 2 to minimize Excess space.
O Use Region 3 rather than Region 2 origination.
O Use Region 2 versus other originations.
O Use Region 2 rather than Region 3 origination.
O Do not pay more for Region 2 versus Region 3 origination.

Transcribed Image Text:A logistics company needs to minimize the excess space measured as 1000 sqft on any given delivery Excess1000. He gathers
data on a sample of N=57 shipments and would like to run a regression with the following independent varlables. The results
are reported below.
Client10 is a continuous varlable that measures the number of customers in 10's on any one shlpment.
The categorical variables are Region and ABLine shown below.
ABLine =1 if shipment was on ABLine carrier
=0 if shipment was on Other carrier
Region1 =1 f shipment originated in Region1
=0 otherwise
Region2 =1 if shipment originated in Region 2
Omitted category is Region3
=0 otherwise
Regression Analysis: Excess1000 versus Client10, ABLine, Reg1, Reg2
Method
Categorical predictor coding (1, 0)
Analysis of Variance
Source
Regression
Client10
ABLine
Reg1
Reg2
Eror
Lack-of-Fit 18
Pure Error 34 12311.1
Total
DF Adj SS Adi MS F-Value P-Value
0.000
0.002
0.687
0.060
0.040
4 10103.1 2525.77
1 3796.3 3796.33
59.94
13552 1355.22
1619.0 1619.04
52 19007.3 365.52
372.01
362.09
691
10.39
0.16
3.71
59.9
1
443
6696.1
1.03
0457
56 29110.4
Model Summary
R-sg R-sgladi) R-sgfored
19.1107 34.71%
29.68%
23.09%
Coefficients
Term
Constant
Client10
ABLine
Coet SE Coef T-Value P.Value VIF
2.72
3.22
1855
681
0.009
0.002 1.05
529
1.64
2.15
531
-0.40
0.687 1.09
Reg
1251
650
1.93
0.060 116
Reg2
13.09
622
-2.10
0040 1.12
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