An experiment was conducted to study the effect of stream characteristics on fish biomass. The regressor variables are as follows: average depth (of 50 cells), x₁; area of in-stream cover (i.e., undercut banks, logs, boulders, etc.), x2; percent canopy cover (average of 12), x3; and area ≥ 25 centimeters in depth, x4. The response is y, the fish biomass. Use the accompanying data to complete parts (a) through (c). Click the icon to view the fish biomass data. (a) Fit a multiple linear regression including all four regression variables. ŷ = 86 + (-16)×₁ + (2.42) x2 + (1.83) ×3 + (3.07) ×4 (Round the constant and x₁-coefficient to the nearest integer as needed. Round all other coefficients to two decimal places as needed.) (b) Use C., R², and s² to determine the best subsets of variables. Compute these statistics for all possible subsets. Find the two models which use the best subset of variables. Select both of these models below. A. X2x3 B. X3X4 E. X1 X3 X4 D. X1 X3 H. X2X3X4 G. X1 K. X2X4 J. X1x2x3 N. X₁x2 M. X4 C. x₁x4 F. x2 1. x1x2x3x4 L. X3 0. x1X2X4 Fish Biomass Data n y x₁ x2 X3 X4 100 14.3 15.0 12.2 48.0 388 19.1 29.4 26.0 152.2 755 54.6 58.0 24.2 469.7 1288 28.8 42.6 26.1 485.9 230 16.1 15.9 31.6 87.6 0 10.0 56.4 23.3 6.9 551 28.5 95.1 13.0 192.9 345 13.8 60.6 7.5 105.8 0 10.7 35.2 40.3 0.0 348 25.9 52.0 40.3 116.6
An experiment was conducted to study the effect of stream characteristics on fish biomass. The regressor variables are as follows: average depth (of 50 cells), x₁; area of in-stream cover (i.e., undercut banks, logs, boulders, etc.), x2; percent canopy cover (average of 12), x3; and area ≥ 25 centimeters in depth, x4. The response is y, the fish biomass. Use the accompanying data to complete parts (a) through (c). Click the icon to view the fish biomass data. (a) Fit a multiple linear regression including all four regression variables. ŷ = 86 + (-16)×₁ + (2.42) x2 + (1.83) ×3 + (3.07) ×4 (Round the constant and x₁-coefficient to the nearest integer as needed. Round all other coefficients to two decimal places as needed.) (b) Use C., R², and s² to determine the best subsets of variables. Compute these statistics for all possible subsets. Find the two models which use the best subset of variables. Select both of these models below. A. X2x3 B. X3X4 E. X1 X3 X4 D. X1 X3 H. X2X3X4 G. X1 K. X2X4 J. X1x2x3 N. X₁x2 M. X4 C. x₁x4 F. x2 1. x1x2x3x4 L. X3 0. x1X2X4 Fish Biomass Data n y x₁ x2 X3 X4 100 14.3 15.0 12.2 48.0 388 19.1 29.4 26.0 152.2 755 54.6 58.0 24.2 469.7 1288 28.8 42.6 26.1 485.9 230 16.1 15.9 31.6 87.6 0 10.0 56.4 23.3 6.9 551 28.5 95.1 13.0 192.9 345 13.8 60.6 7.5 105.8 0 10.7 35.2 40.3 0.0 348 25.9 52.0 40.3 116.6
Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
Related questions
Question
I need help with part b in attached image, please. Thanks.
Transcribed Image Text:An experiment was conducted to study the effect of stream characteristics on fish biomass. The regressor variables
are as follows: average depth (of 50 cells), x₁; area of in-stream cover (i.e., undercut banks, logs, boulders, etc.), x2;
percent canopy cover (average of 12), x3; and area ≥ 25 centimeters in depth, x4. The response is y, the fish biomass.
Use the accompanying data to complete parts (a) through (c).
Click the icon to view the fish biomass data.
(a) Fit a multiple linear regression including all four regression variables.
ŷ = 86 + (-16)×₁ + (2.42) x2 + (1.83) ×3 + (3.07) ×4
(Round the constant and x₁-coefficient to the nearest integer as needed. Round all other coefficients to two decimal
places as needed.)
(b) Use C., R², and s² to determine the best subsets of variables. Compute these statistics for all possible subsets.
Find the two models which use the best subset of variables. Select both of these models below.
A. X2x3
B. X3X4
E. X1 X3 X4
D. X1 X3
H. X2X3X4
G. X1
K. X2X4
J. X1x2x3
N. X₁x2
M. X4
C. x₁x4
F. x2
1. x1x2x3x4
L. X3
0. x1X2X4
Transcribed Image Text:Fish Biomass Data
n
y
x₁
x2
X3
X4
100
14.3
15.0
12.2
48.0
388
19.1
29.4
26.0
152.2
755
54.6
58.0
24.2
469.7
1288
28.8
42.6
26.1
485.9
230
16.1
15.9
31.6
87.6
0
10.0
56.4
23.3
6.9
551
28.5
95.1
13.0
192.9
345
13.8
60.6
7.5
105.8
0
10.7
35.2
40.3
0.0
348
25.9
52.0
40.3
116.6
Expert Solution
This question has been solved!
Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.
Step by step
Solved in 2 steps
Recommended textbooks for you
Advanced Engineering Mathematics
Advanced Math
ISBN:
9780470458365
Author:
Erwin Kreyszig
Publisher:
Wiley, John & Sons, Incorporated
Numerical Methods for Engineers
Advanced Math
ISBN:
9780073397924
Author:
Steven C. Chapra Dr., Raymond P. Canale
Publisher:
McGraw-Hill Education
Introductory Mathematics for Engineering Applicat…
Advanced Math
ISBN:
9781118141809
Author:
Nathan Klingbeil
Publisher:
WILEY
Advanced Engineering Mathematics
Advanced Math
ISBN:
9780470458365
Author:
Erwin Kreyszig
Publisher:
Wiley, John & Sons, Incorporated
Numerical Methods for Engineers
Advanced Math
ISBN:
9780073397924
Author:
Steven C. Chapra Dr., Raymond P. Canale
Publisher:
McGraw-Hill Education
Introductory Mathematics for Engineering Applicat…
Advanced Math
ISBN:
9781118141809
Author:
Nathan Klingbeil
Publisher:
WILEY
Mathematics For Machine Technology
Advanced Math
ISBN:
9781337798310
Author:
Peterson, John.
Publisher:
Cengage Learning,
