11 votes

Linear regression's (OLS) coefficient interpretation with heteroscedasticity

Heteroscedasticity makes it so that the OLS estimator is not the best linear unbiased estimator of the regression slopes and makes it so that the usual standard errors (and the quantities based on ...
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  • 21.9k
7 votes

Interpreting log-log regression with log(1+x) as independent variable

Definitely not, except when $x$ is much larger than $1.$ This is one reason why the automatic reflex to "just add $1$ to values that might be zero before taking the log" is difficult to ...
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  • 287k
5 votes
Accepted

Regression coefficients do not match conditional means

It's not straightforward to estimate effects by hand. The math works out nicely only in "simple" cases. One simple case is a saturated model. A saturated model includes all main effects and ...
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  • 2,166
2 votes
Accepted

Why are the estimates of three mixed models so different from each other?

This is an extreme example of "comparing apples and oranges". Because the models are all fitted on different scales (more on this below), it's almost impossible to meaningfully compare ...
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1 vote

Interpreting log-log regression with log(1+x) as independent variable

You can interpret it as following: a $q\%$ change in $x$ leads to a $q \frac{x}{1+x}\%$ change in $(1+x)$. If $x$ is large they have a similar interpretation but not when $x$ is small. For instance, ...
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1 vote

Interpreting interaction effect with ln(time) in Cox regression

Although the large number of cases makes most of your coefficients "statistically significant," your model is not distinguishing cases very successfully. The concordance of 0.503 is the ...
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  • 62.4k
1 vote

Transforming ordinal logistic regression coefficients to beta weights

In logistic regression you linearly regress the log odds, so the fitted coefficients are the "beta weights" for the log odds. So you can say that the "IV1 effect on the log odds of the ...
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  • 2,978
1 vote

Help with Excel's Regression Output

My guess is that your original model had a linear dependence among the predictors, so that you couldn't get an estimate for the logFOP coefficient. I don't know if ...
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  • 62.4k
1 vote

Regression coefficients do not match conditional means

This is related to an answer in this question. Why is the intercept in multiple regression changing when including/excluding regressors? In this image you see how a fitted curve does not have to ...
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1 vote

Regression coefficients do not match conditional means

The first comment form this post helped me understand my problem. There are eight conditional means, from which I calculated four differences. My regression model had six parameters. In order for the ...
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