5- According to the summary result of linear regression model between A and B obtained from R given below, we can fit a regression line. Assume that A has any value. If we decrease the value of A by 3, how would Y be affected? Call: 1m (formula - B- A) Residuals: Min 10 Median 30 Max -16.340 -10.793 -9.653 -8.502 58.325 Coefficients: Estimate Std. Error t value Pr (>It|) (Intercept) 19.6315 20.6457 0.951 0.373 A 9.9609 0.3717 26.800 2.58e-08 *** Signif. codes: 0 **** 0.001 *** 0.01 * 0.05 , 0.1 1 Residual standard error: 26.46 on 7 degrees of freedom 0.9903, Adjusted R-squared: 0.989 Multiple R-squared: F-statistic: 718.2 on 1 and 7 DF, p-value: 2.58le-08 a) 29.8827 increase b) 29.8827 decrease c) 58.8945 increase d) 49.5142 decrease e) 58.8945 decrease Leave blank

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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5- According to the summary result of linear regression model between A and B obtained from R given below, we can fit
a regression line. Assume that A has any value. If we decrease the value of A by 3, how would Y be affected?
Call:
1m (formula = B A)
Residuals:
Min
10 Median
30
Max
-16.340 -10.793
-9.653
-8.502
58.325
Coefficients:
Estimate Std. Error t value Pr(>1t|)
20.6457
(Intercept)
19.6315
0.951
0.373
A
9.9609
0.3717
26.800 2.58e-08 ***
Signif. codes:
0 *** 0.001 ** 0.01 * 0.05, 0.1 1
Residual standard error: 26.46 on 7 degrees of freedom
Adjusted R-squared:
p-value: 2.58le-08
0.989
Multiple R-squared:
F-statistic: 718.2 on 1 and 7 DF,
0.9903,
a)
29.8827 increase
b)
29.8827 decrease
C)
58.8945 increase
d)
49.5142 decrease
e)
58.8945 decrease
Leave blank
Transcribed Image Text:5- According to the summary result of linear regression model between A and B obtained from R given below, we can fit a regression line. Assume that A has any value. If we decrease the value of A by 3, how would Y be affected? Call: 1m (formula = B A) Residuals: Min 10 Median 30 Max -16.340 -10.793 -9.653 -8.502 58.325 Coefficients: Estimate Std. Error t value Pr(>1t|) 20.6457 (Intercept) 19.6315 0.951 0.373 A 9.9609 0.3717 26.800 2.58e-08 *** Signif. codes: 0 *** 0.001 ** 0.01 * 0.05, 0.1 1 Residual standard error: 26.46 on 7 degrees of freedom Adjusted R-squared: p-value: 2.58le-08 0.989 Multiple R-squared: F-statistic: 718.2 on 1 and 7 DF, 0.9903, a) 29.8827 increase b) 29.8827 decrease C) 58.8945 increase d) 49.5142 decrease e) 58.8945 decrease Leave blank
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