# Tag Info

### 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 ...
• 21.9k
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 ...
• 2,156
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 ...
• 35k

### Variance of $\hat{\beta}$ in Ridge Regression

This question seeks information that is similar to an answer in another question here, though it is not a duplicate of that other question. Most of the present answer is adapted from the answer to ...
• 94.6k
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 ...
• 2,958
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 ...
• 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 ...
• 46.5k
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 ...
• 341

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