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The parameters of a regression model. Most commonly, the values by which the independent variables will be multiplied to get the predicted value of the dependent variable.

5
votes
The problem is with this: I had always interpreted the betas as the partial derivative of X on Y 'in reality' That's not always true in a model with interactions or various other forms of comple …
answered Aug 21 '14 by conjugateprior
9
votes
Quite generally you want the vcov function which provides the complete parameter covariance matrix. To get the regular asymptotic standard errors reported by summary you can use se <- sqrt(diag(vcov …
answered Apr 18 '12 by conjugateprior
1
vote
The intercept in a linear model is the value of the dependent variable that the model expects when all your independent variables are equal to 0. So if you redefine an independent variable in such as …
answered Feb 14 '15 by conjugateprior
11
votes
Like this: fit = lm(ydata ~ .,data = data) se <- sqrt(diag(vcov(fit))) These are the classical asymptotic ones you see in summary. Please also see the links in my answer to this same question abou …
answered May 2 '12 by conjugateprior
2
votes
Output Coefficients: (Intercept) wt hp wt:hp 49.80842 -8.21662 -0.12010 0.02785 How do we read this output? ... We have a slope for wt and a slope …
answered Dec 8 '15 by conjugateprior
20
votes
A little niggle 'Now many textbook examples tell me that if there is a significant effect of the interaction, the main effects cannot be interpreted' I hope that's not true. They should say th …
answered Jun 24 '12 by conjugateprior