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A generalization of linear regression allowing for nonlinear relationships via a "link function" and for the variance of the response to depend on the predicted value. (Not to be confused with "general linear model" which extends the ordinary linear model to general covariance structure and multivariate response.)
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Similar LMs, very different results. Possible causes?
First I ran the following model:
$$y = \beta_1 x_1 + \beta_2 x_2 + ...$$
And I tested (t-test) if $\beta_1>0$ and $\beta_2>0$, which is true for both.
Then I split $x_1$ in two, such that $x_{1a}+x_{1 …
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Similar LMs, very different results. Possible causes?
The answer is that $x_{1a}$ and $x_2$ are negatively correlated and not correlated at all with $y$. Because $x_{1b}$ is positively correlated with $y$, when I sum $x_{1a} + x_{1b}$, the coefficient fo …