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. y= 86 + (-16) ×₁ + (2.42 ) x2 + ( 1.83 ) x3 + (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 D. X1 X3 x1 J. X1x2x3 M. x4 B. X3X4 E. X1 X3 X4 H. X2X3x4 K. X2x4 N. x1x2 C. x₁x4 F. x2 X1X2X3X4 X3 O. X1x2x4 (c) Compare the appropriateness of the models in parts (a) and (b) for predicting fish biomass. Choose the correct answer below. While the value of R² does not vary greatly across the three models, the values of s² = ☐ and PRESS = | suggest that the (Round to one decimal places as needed.) model will have the strongest predictive power.
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. y= 86 + (-16) ×₁ + (2.42 ) x2 + ( 1.83 ) x3 + (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 D. X1 X3 x1 J. X1x2x3 M. x4 B. X3X4 E. X1 X3 X4 H. X2X3x4 K. X2x4 N. x1x2 C. x₁x4 F. x2 X1X2X3X4 X3 O. X1x2x4 (c) Compare the appropriateness of the models in parts (a) and (b) for predicting fish biomass. Choose the correct answer below. While the value of R² does not vary greatly across the three models, the values of s² = ☐ and PRESS = | suggest that the (Round to one decimal places as needed.) model will have the strongest predictive power.
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
Problem 94E
Related questions
Question
I need to solve part C. Give final answers I posted this multiple timees for nothing. Everything required is attached stop wasting my questions with non sense answers
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
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.
y= 86 + (-16) ×₁ + (2.42 ) x2 + ( 1.83 ) x3 + (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
D. X1 X3
x1
J. X1x2x3
M. x4
B. X3X4
E. X1 X3 X4
H. X2X3x4
K. X2x4
N. x1x2
C. x₁x4
F. x2
X1X2X3X4
X3
O. X1x2x4
(c) Compare the appropriateness of the models in parts (a) and (b) for predicting fish biomass. Choose the correct answer below.
While the value of R² does not vary greatly across the three models, the values of s² = ☐ and PRESS = |
suggest that the
(Round to one decimal places as needed.)
model will have the strongest predictive power.
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