2. Using the linear model of a researcher calculated the hat-matrix = X(X'X)-¹X' = H = y =B₁ + B₁x +€, H and the residual e using 6 observations. 0.0818 [0.2516 0.1101 0.3931 0.0534 0.1101 0.1101 0.2044 0.0157 0.2421 0.2044 0.2233 0.3931 0.1572 0.7704 -0.1352 0.0157 -0.0597 0.0535 0.2421 -0.1352 0.3176 0.2421 0.2233 0.1101 0.2044 0.0157 0.2421 0.2044 0.22327 0.0818 0.2232 -0.0597 0.2799 0.2233 0.2516 (w/ soment o. That is, (b) Identify observations with large leverage value. 1 -) e = -0.02 0.12 -0.56 0.88 -0.01 -0.07

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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ith leverage value (hat-value) = (H)ii= hii, ith diagnal entry of hat matrix
• The h¡¡ is considered to be large if
hii > 2 * h = 2 *
k + 1 = # of parameters,
k + 1
n
n = # of sample,
where Ehii = k+1
h = average of diagonal entry.
Transcribed Image Text:ith leverage value (hat-value) = (H)ii= hii, ith diagnal entry of hat matrix • The h¡¡ is considered to be large if hii > 2 * h = 2 * k + 1 = # of parameters, k + 1 n n = # of sample, where Ehii = k+1 h = average of diagonal entry.
2. Using the linear model of
y = Bo + B12 +€,
a researcher calculated the hat-matrix H and the residual e using 6 observations.
H = X(X'X)-¹X' =
[0.2516 0.1101 0.3931 0.0534 0.1101 0.0818
0.1101 0.2044 0.0157 0.2421 0.2044 0.2233
0.3931 0.1572 0.7704 -0.1352 0.0157 -0.0597
0.0535 0.2421 -0.1352 0.3176 0.2421 0.2233
0.1101 0.2044 0.0157 0.2421 0.2044 0.22327
0.2233 0.2516
0.0818 0.2232 -0.0597 0.2799
(c)
ut σ. That is,
(b) Identify observations with large leverage value.
identify usta vati
CONS
1 1
2
e =
-0.02]
0.12
-0.56
0.88
-0.01
-0.07
Transcribed Image Text:2. Using the linear model of y = Bo + B12 +€, a researcher calculated the hat-matrix H and the residual e using 6 observations. H = X(X'X)-¹X' = [0.2516 0.1101 0.3931 0.0534 0.1101 0.0818 0.1101 0.2044 0.0157 0.2421 0.2044 0.2233 0.3931 0.1572 0.7704 -0.1352 0.0157 -0.0597 0.0535 0.2421 -0.1352 0.3176 0.2421 0.2233 0.1101 0.2044 0.0157 0.2421 0.2044 0.22327 0.2233 0.2516 0.0818 0.2232 -0.0597 0.2799 (c) ut σ. That is, (b) Identify observations with large leverage value. identify usta vati CONS 1 1 2 e = -0.02] 0.12 -0.56 0.88 -0.01 -0.07
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