(a)
For Model 1,
Numerator df = Number of predictors in regression model (k) = 5
Denominator df = degree of freedom for residual eror = 14
df total = 5 + 14 = 19
For Model 2,
Numerator df = Number of predictors in regression model (k) = 2
Denominator df = df total - Numerator df = 19 - 2 = 17
Thus,
(a) = 5 , (b) = 14 , (c) = 17 , (d) = 2 (e) = 17
(b)
Standard error for model 1 = 6.157
Standard error for model 2 = 6.081
Model 2 has small test error.
(c)
n = df total + 1 = 19 + 1 = 20
AIC = n log() + 2(k + 1)
where s is the standard error.
For Model 1.
AIC = 20 log() + 2(5 + 1) = 84.70359
For Model 2.
AIC = 20 log() + 2(2 + 1) = 78.20677
Since AIC for model 2 is less than that of model 1, model 2 has small test error.
(d)
For Model 1,
F statistic value = 3.154 on 5 and 14 degree of freedom
For Model 2,
F statistic value = 7.685 on 2 and 17 degree of freedom
(a) fill the blank (a),(b),(c),(d), and (e) (b) which model has small test error? justify your...
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