5
votes
Accepted
What I have to do more to improve my regression model in r
First off, the transformation of the dependent variable is just odd. I'm guessing there is no way to meaningfully interpret the dependent variable if it is converted in this fashion, and its not clear ...
- 16.1k
5
votes
Accepted
In classical regression, are residuals uncorrelated?
It's easy to show using the definition of the residuals $\mathbf e=(\mathbf I_n-\mathbf H) \mathbf Y, $ where $\mathbf H$ is the hat matrix and is idempotent, that if the error $\boldsymbol{\...
- 9,567
4
votes
Fitting against residuals equivalent to optimizing global linear model?
Extending what @whuber said in a comment:
Assuming by "$r^2$" you mean something related to the sum of squared residuals in a way that does not depend on the number of explanatory variables ...
- 97.6k
1
vote
How to visualise the value of one predictor in a multiple linear regression
Well, start by looking at your model summary if you are using R: summary(mod)
You should see a column "Estimate" and you can look at the value of the ...
- 343
1
vote
Why estimates of data via residuals has 50/50 effect on significance compared to original data?
I'll focus on the second model with fixed coefficients. In that case, it's because the estimate of I based on the residuals of ...
- 97.6k
1
vote
Accepted
How to interpret dispersion estimated for a Poisson model?
You would typically use those test statistics to conduct a goodness-of-fit $\chi^2$ test after fitting the model. The tests return p-values for the null that the conditional distribution is Poisson. I ...
- 37.8k
1
vote
Regression model with (almost) non-negative residuals
Your response is non-negative and the estimate is non-negative.
But the difference, the residuals, can be negative.
See below an example for exponentially distributed data
...
- 82.8k
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