# Tag Info

### Why does the normality assumption not affect Linear Regression in large samples?

The assumption of the Normality of the error term in a regression that applies Least-Squares estimation methods, is used to make statistical inferences about the coefficients after estimation, it is ...

### Can I use multiple linear regression with binary output?

You can. It's called a linear probability model. Should you? There are a couple of problems. Someone can only score 0 or 1. What does it mean to predict they score 0.5? (Maybe it means 50% probability)...
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### What test is most appropriate if you're interested in an interaction, but have more variables than samples

You have far too few observations and far too many predictors to reach conclusions. Whatever you do, you will be chasing noise, even if you correct for multiple comparisons and regularize, unless ...
• 129k
Accepted

### Dropping outlier from linear regression model reducing adjusted R^2

Seems to be a situation like in the well known Anscombe quartet, the lower right graph with Y4. Probably your situation is less dramatic, in the sense the vertical bar of 9 points (at the left in that ...
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### Is it possible to calculate the power for a specific beta coefficient in a multiple linear regression?

Yes, this is possible. Eric Vittinghoff and coauthors include sample size equations at the end of the chapters in their book Regression Methods in Biostatistics. The power $\gamma$ attained given a ...
• 38.6k
Accepted

### Model reduction in linear regression by stepwise elimination of predictors with "non-significant" coefficients

This procedure looks like standard backward elimination based on p-values except for the "smallest absolute value" selection, which only makes sense if predictors are standardised. The major ...
• 26.8k
Accepted

### Why do we use an F distribution instead of just chi-squared when testing multiple hypotheses from regression?

Hint This numerator is distributed as chi-sq. Almost right, but not quite. The precise statement is that the numerator scaled by the dispersion parameter $\sigma^2$ of error $\varepsilon$ is chi-...
• 20.6k
Accepted

### How to analyze a dichotomous outcome with 50% missing data?

Your description implies that you committed the mortal sin of not pre-specifying the final model in the sense that you tried different models in a way not unlike stepwise variable selection does (...
• 96.5k

### Count predictor and binary outcome

Per your questions... Is a binary logistic regression the best approach when I have a count predictor and a binary outcome? Yes. Logistic regression handles any linear equation which requires the ...
• 16.2k
Accepted

### Assumptions of Linear Regression (homoscedasticity and normality of residuals)

The questions themselves are interesting and nontrivial enough that I believe you may have some basic knowledge about assumption testing already, so I'm not telling you what to do in particular (for ...
• 26.8k
Accepted

### Can I use multiple linear regression with binary output?

Along with Jeremy's excellent answer, I'll focus on the most relevant bit here: I searched in the stack exchange site, and people suggest logistic regression. However, I cannot understand why to use ...
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• 20.6k
Accepted

### When does SEM have little to no benefit over multiple regression, and there is a distinction without a difference between two approaches?

Relative to multiple regression (and assuming a single DV and only direct effects like you wrote), I see the main advantages of SEM in the possibility to test the model against the observed data (in ...
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### Does it make sense to talk of "multicollinearity" in the context of simple linear regression?

IMO, it does make sense, to some extent. Although it depends on what you mean by "multicollinearity" and "simple linear regression". Many people have distinct definitions for the ...
• 309
Accepted

### Variable selection in logistic regression

The problem with your proposed approach is that every predictor in itself may not correlate with the outcome, but interactions between them might. Or you might have a curvilinear relationship between ...
• 129k