For observed data y=(y1,…,yn)y=(y1,…,yn) with  n=21n=21, the above linear regression model was fitted in R, with the following output:  >n = 21 >xi = seq(0, n-1,1)/(n-1) >p1 =2*xi-1 >p2 =6*xi^2- 6*xi+1-1/(n-1) > summary(lm(y ~ p1+p2)) Call: lm(formula = y ~ p1 + p2) Residuals:     Min      1Q  Median      3Q     Max -0.5258 -0.2153  0.0813  0.1770  0.4669 Coefficients:              Estimate Std. Error t value Pr(>|t|) (Intercept) -0.004238   0.063450  -0.067    0.947 p1           1.181260   0.104784  11.273 1.37e-09 *** p2          -0.953388   0.129422  -7.366 7.77e-07 *** --- Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 0.2908 on 18 degrees of freedom Multiple R-squared:  0.9097,    Adjusted R-squared:  0.8997 F-statistic: 90.68 on 2 and 18 DF,  p-value: 3.989e-10   Write this linear regression model in the vector form and answer the following questions, using the above R output where necessary.

Calculus For The Life Sciences
2nd Edition
ISBN:9780321964038
Author:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Publisher:GREENWELL, Raymond N., RITCHEY, Nathan P., Lial, Margaret L.
Chapter1: Functions
Section1.2: The Least Square Line
Problem 4E
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For observed data y=(y1,…,yn)y=(y1,…,yn) with  n=21n=21, the above linear regression model was fitted in R, with the following output: 

>n = 21

>xi = seq(0, n-1,1)/(n-1)

>p1 =2*xi-1

>p2 =6*xi^2- 6*xi+1-1/(n-1)

> summary(lm(y ~ p1+p2))

Call:

lm(formula = y ~ p1 + p2)

Residuals:

    Min      1Q  Median      3Q     Max

-0.5258 -0.2153  0.0813  0.1770  0.4669

Coefficients:

             Estimate Std. Error t value Pr(>|t|)

(Intercept) -0.004238   0.063450  -0.067    0.947

p1           1.181260   0.104784  11.273 1.37e-09 ***

p2          -0.953388   0.129422  -7.366 7.77e-07 ***

---

Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.2908 on 18 degrees of freedom

Multiple R-squared:  0.9097,    Adjusted R-squared:  0.8997

F-statistic: 90.68 on 2 and 18 DF,  p-value: 3.989e-10

 

Write this linear regression model in the vector form and answer the following questions, using the above R output where necessary.  

3. Consider the following normal linear regression model
where x₂ = (i-1)/(n − 1), i = 1, 2.….., n, and på are Legendre polynomials on [0, 1] given by
Po(x) = 1,
P₁(x) = 2x 1,
P2(x) = 6x² - 6x +1 -1/(n-1).
You are given that
Σ\Po(x;)]²
i=1
a =
E(Yixi)=Bkpk (xi) = ßo + B₁p₁ (xi) + B2p2(xi),
= n₂
22
k=0
Σ\pa(i)]2
i=1
n
ΣPo (xi)P₁ (xi) = 0,
=
n(n+1)
3(n − 1)'
n
EP₂(x)]²=
i=1
n
ΣPo (xi)p2 (xi) = 0,
i=1
(c) Provide estimates for the following values to two decimal places.
(i) Construct a 951% confidence interval [a, b] for 3₁:
=
(n-6)
5
5n²-11n
5(n-1)³
n
ΣP₁ (xi)p2 (xi) = 0.
(12)
i=1
b=
(ii) The estimate of o² is o²
(iii) For testing hypothesis that E(Y₁ | x;) does not depend on x for this model, determine the value of the F
test statistic
and whether this hypothesis is rejected at 51% significance level:
F statistic =
this hypothesis is rejected at 51% significance level: (No answer given) +
Transcribed Image Text:3. Consider the following normal linear regression model where x₂ = (i-1)/(n − 1), i = 1, 2.….., n, and på are Legendre polynomials on [0, 1] given by Po(x) = 1, P₁(x) = 2x 1, P2(x) = 6x² - 6x +1 -1/(n-1). You are given that Σ\Po(x;)]² i=1 a = E(Yixi)=Bkpk (xi) = ßo + B₁p₁ (xi) + B2p2(xi), = n₂ 22 k=0 Σ\pa(i)]2 i=1 n ΣPo (xi)P₁ (xi) = 0, = n(n+1) 3(n − 1)' n EP₂(x)]²= i=1 n ΣPo (xi)p2 (xi) = 0, i=1 (c) Provide estimates for the following values to two decimal places. (i) Construct a 951% confidence interval [a, b] for 3₁: = (n-6) 5 5n²-11n 5(n-1)³ n ΣP₁ (xi)p2 (xi) = 0. (12) i=1 b= (ii) The estimate of o² is o² (iii) For testing hypothesis that E(Y₁ | x;) does not depend on x for this model, determine the value of the F test statistic and whether this hypothesis is rejected at 51% significance level: F statistic = this hypothesis is rejected at 51% significance level: (No answer given) +
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