I generated samples of (y,x) and fit simple linear regression models: (i) y on x, are the (partial) outputs. Fill in the blanks (1) and (2). Make sure to show your Problem 2. ( and (ii) x on y. Below calculations/reasoning. > summary (1m (y x)) Call: 1m (formula = y ~ x) Residuals: Min Median 3Q 1Q -4.3423 -1.3463 0.0226 1.4724 Max 4.6601 Coefficients: Estimate Std. Error t value Pr (>|t|) (Intercept) 0.1607 0.2117 0.45 0.759 0.2273 13.996 X 3.1814 <2e-16 *** Multiple R-squared: 0.6666 F-statistic: 195.9 on 1 and 98 DF, p-value: < 2.2e-16 > summary (1m (x*y)) Call: ~ 1m (formula = x y) Residuals: 1Q Median 3Q Max Min -1.17438 -0.37399 -0.03598 0.36148 1.52009 Coefficients: Estimate Std. Error t value Pr (>|t|) (Intercept) -0.04254 0.05431 -0.783 0.435 y (1) 0.01497 <2e-16 *** Multiple R-squared: (2)
I generated samples of (y,x) and fit simple linear regression models: (i) y on x, are the (partial) outputs. Fill in the blanks (1) and (2). Make sure to show your Problem 2. ( and (ii) x on y. Below calculations/reasoning. > summary (1m (y x)) Call: 1m (formula = y ~ x) Residuals: Min Median 3Q 1Q -4.3423 -1.3463 0.0226 1.4724 Max 4.6601 Coefficients: Estimate Std. Error t value Pr (>|t|) (Intercept) 0.1607 0.2117 0.45 0.759 0.2273 13.996 X 3.1814 <2e-16 *** Multiple R-squared: 0.6666 F-statistic: 195.9 on 1 and 98 DF, p-value: < 2.2e-16 > summary (1m (x*y)) Call: ~ 1m (formula = x y) Residuals: 1Q Median 3Q Max Min -1.17438 -0.37399 -0.03598 0.36148 1.52009 Coefficients: Estimate Std. Error t value Pr (>|t|) (Intercept) -0.04254 0.05431 -0.783 0.435 y (1) 0.01497 <2e-16 *** Multiple R-squared: (2)
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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Please provide this question correctly in 1 hour I will give you Thumbs up please provide correct solution

Transcribed Image Text:Problem 2. (
generated samples of (y,x) and fit simple linear regression models: (i) y on x,
and (ii) x on y. Below are the (partial) outputs. Fill in the blanks (1) and (2). Make sure to show your
calculations/reasoning.
> summary (1m (y^x))
Call:
1m (formula = y ~ x)
Residuals:
Min
1Q Median
3Q
Max
4.6601
-4.3423 -1.3463 0.0226 1.4724
Coefficients:
Estimate Std. Error t value Pr (>|t|)
(Intercept)
0.1607
0.2117
0.759
0.45
X
3.1814
0.2273 13.996
<2e-16 ***
Multiple R-squared: 0.6666
F-statistic: 195.9 on 1 and 98 DF, p-value: <2.2e-16
> summary (1m (x*y))
Call:
1m (formula = x y)
Residuals:
1Q Median
3Q
Max
Min
-1.17438 -0.37399 -0.03598 0.36148 1.52009
Coefficients:
Estimate Std. Error t value Pr (>|t|)
(Intercept) -0.04254
0.05431 -0.783
0.435
y
(1)
0.01497
<2e-16
Multiple R-squared: (2)
***
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