Explain why you would or wouldn’t agree with each of the following statements: (a) In the presence of autocorrelation, least-square estimate will be biased (b) If the pairwise correlations among predictors are all close to 0, then, there exists no collinearity; (c) If we center the predictors, VIF would change; (d) AIC can be used to select the models which are not nested with each other
Explain why you would or wouldn’t agree with each of the following statements: (a) In the presence of autocorrelation, least-square estimate will be biased (b) If the pairwise correlations among predictors are all close to 0, then, there exists no collinearity; (c) If we center the predictors, VIF would change; (d) AIC can be used to select the models which are not nested with each other
Linear Algebra: A Modern Introduction
4th Edition
ISBN:9781285463247
Author:David Poole
Publisher:David Poole
Chapter7: Distance And Approximation
Section7.3: Least Squares Approximation
Problem 31EQ
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Explain why you would or wouldn’t agree with each of the following statements:
(a) In the presence of autocorrelation, least-square estimate will be biased
(b) If the pairwise correlations among predictors are all close to 0, then, there exists no collinearity;
(c) If we center the predictors, VIF would change;
(d) AIC can be used to select the models which are not nested with each other;
(e) With the same data, the model selected by R-square is the same as the model selected by RMS;
(f) For multinomial logistic regression with response variables with k categories, we need to model k logit equations; (g) Odds ratio is between 0 and 1.
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