1 P₁ = 1+e¯²; and is bound between -∞ and +∞ Let Z₁ = a + ẞx; We cannot use OLS on the logistics distribution because Pi is non-linear in both the x's and B's. However, we can get around this by linearizing the model as a log of the odds ratio. Derive the regression model for the binary logit model expressed as the log of the odds ratio (10 points).

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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1
P₁ =
1+e¯²;
and is bound between -∞ and +∞
Let Z₁ = a + ẞx;
We cannot use OLS on the logistics distribution because Pi is
non-linear in both the x's and B's. However, we can get around
this by linearizing the model as a log of the odds ratio. Derive
the regression model for the binary logit model expressed as the
log of the odds ratio (10 points).
Transcribed Image Text:1 P₁ = 1+e¯²; and is bound between -∞ and +∞ Let Z₁ = a + ẞx; We cannot use OLS on the logistics distribution because Pi is non-linear in both the x's and B's. However, we can get around this by linearizing the model as a log of the odds ratio. Derive the regression model for the binary logit model expressed as the log of the odds ratio (10 points).
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