Consider the multiple regression output shown: The regression equation is Y = 59+ 4.53X10.26X2 + 6.35X3 + 1.71X4 - 1.12X5. Ο X1 X2 O X3 O X4 O X5 Predictor Constant X1 X2 X3 X4 X5 Coef SE Coef Total Which variable is least significant in this model? -58.9 103.3 -0.57 0.575 T 4.528 2.146 2.11 0.053 P -0.263 1.252 -0.21 0.833 6.348 2.386 2.66 0.019 Analysis of Variance Source Regression Residual Error 14 14546 1039.0 1.7098 0.5624 3.04 0.009 S = 32.23352 R - Sq = 77.9% R - Sq (adj) = 70.0% -1.1210 0.7626 -1.47 0.163 MS 19 65821 DF SS 5 51275 10255 9.87 0.000 F P
Consider the multiple regression output shown: The regression equation is Y = 59+ 4.53X10.26X2 + 6.35X3 + 1.71X4 - 1.12X5. Ο X1 X2 O X3 O X4 O X5 Predictor Constant X1 X2 X3 X4 X5 Coef SE Coef Total Which variable is least significant in this model? -58.9 103.3 -0.57 0.575 T 4.528 2.146 2.11 0.053 P -0.263 1.252 -0.21 0.833 6.348 2.386 2.66 0.019 Analysis of Variance Source Regression Residual Error 14 14546 1039.0 1.7098 0.5624 3.04 0.009 S = 32.23352 R - Sq = 77.9% R - Sq (adj) = 70.0% -1.1210 0.7626 -1.47 0.163 MS 19 65821 DF SS 5 51275 10255 9.87 0.000 F P
Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
Publisher:Swokowski
Chapter5: Inverse, Exponential, And Logarithmic Functions
Section5.6: Exponential And Logarithmic Equations
Problem 68E
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Transcribed Image Text:Consider the multiple regression output shown:
The regression equation is
Y = 59 +4.53X10.26X2 + 6.35X3 + 1.71X4 - 1.12X5.
O XI
O X2
O X3
O X4
O X5
Predictor
Constant
X1
X2
X3
X4
X5
Coef SE Coef
Which variable is least significant in this model?
Regression
Residual Error
Total
4.528
-58.9 103.3 -0.57 0.575
-0.263
6.348
Analysis of Variance
Source
2.146 2.11
T
DF SS
1.252 -0.21 0.833
1.7098 0.5624 3.04 0.009
-1.1210 0.7626 -1.47 0.163
S = 32.23352 R - Sq = 77.9% R - Sq (adj) = 70.0%
P
2.386 2.66 0.019
5 51275
0.053
MS
19 65821
14 14546 1039.0
F P
10255 9.87 0.000
Transcribed Image Text:Consider the multiple regression output shown:
State R² for this model.
The regression equation is
Y = 61+ 4.96X10.20X2 + 6.73X3 + 1.49X4 - 1.06X5.
R² = i
Predictor Coef SE Coef T
Constant
X1
X2
X3
X4
X5
Regression
-60.9 106.8 -0.57 0.575
4.961
Total
-0.203
6.726
%
Analysis of Variance
Source
S = 32.74032 R - Sq = 77.9% R - Sq (adj)
2.351 2.11 0.053
1.4899 0.4901 3.04 0.009
DF SS
P
0.967 -0.21 0.833
-1.0622 0.7226 -1.47 0.163
2.529 2.66 0.019
Round your answer to one decimal place, if necessary.
Residual Error 14 15007 1071.9
MS
19 67907
5 52900 10580 9.87 0.000
F P
= 70.0%
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