Questions tagged [regression]
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
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What is the benefit of breaking up a continuous predictor variable?
I'm wondering what the value is in taking a continuous predictor variable and breaking it up (e.g., into quintiles), before using it in a model.
It seems to me that by binning the variable we lose ...
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How to deal with perfect separation in logistic regression?
If you have a variable which perfectly separates zeroes and ones in target variable, R will yield the following "perfect or quasi perfect separation" warning message:
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When is it ok to remove the intercept in a linear regression model?
I am running linear regression models and wondering what the conditions are for removing the intercept term.
In comparing results from two different regressions where one has the intercept and the ...
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Including the interaction but not the main effects in a model
Is it ever valid to include a two-way interaction in a model without including the main effects? What if your hypothesis is only about the interaction, do you still need to include the main effects?
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Principled way of collapsing categorical variables with many levels?
What techniques are available for collapsing (or pooling) many categories to a few, for the purpose of using them as an input (predictor) in a statistical model?
Consider a variable like college ...
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Is $R^2$ useful or dangerous?
I was skimming through some lecture notes by Cosma Shalizi (in particular, section 2.1.1 of the second lecture), and was reminded that you can get very low $R^2$ even when you have a completely linear ...
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How exactly does one “control for other variables”?
Here is the article that motivated this question: Does impatience make us fat?
I liked this article, and it nicely demonstrates the concept of “controlling for other variables” (IQ, career, income, ...
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Does an unbalanced sample matter when doing logistic regression?
Okay, so I think I have a decent enough sample, taking into account the 20:1 rule of thumb: a fairly large sample (N=374) for a total of 7 candidate predictor variables.
My problem is the following: ...
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What if residuals are normally distributed, but y is not?
I've got a weird question. Assume that you have a small sample where the dependent variable that you're going to analyze with a simple linear model is highly left skewed. Thus you assume that $u$ is ...
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What is a complete list of the usual assumptions for linear regression?
What are the usual assumptions for linear regression?
Do they include:
a linear relationship between the independent and dependent variable
independent errors
normal distribution of errors
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In linear regression, when is it appropriate to use the log of an independent variable instead of the actual values?
Am I looking for a better behaved distribution for the independent variable in question, or to reduce the effect of outliers, or something else?
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Omitted variable bias in logistic regression vs. omitted variable bias in ordinary least squares regression
I have a question about omitted variable bias in logistic and linear regression.
Say I omit some variables from a linear regression model. Pretend that those omitted variables are uncorrelated with ...
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Goodness of fit and which model to choose linear regression or Poisson
I need some advice regarding two main dilemmas in my research, which is a case study of 3 big pharmaceuticals and innovation. Number of patents per year is the dependent variable.
My questions are
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How do you deal with "nested" variables in a regression model?
Consider a statistical problem where you have a response variable that you want to describe conditional on an explanatory ...
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Is there a difference between 'controlling for' and 'ignoring' other variables in multiple regression?
The coefficient of an explanatory variable in a multiple regression tells us the relationship of that explanatory variable with the dependent variable. All this, while 'controlling' for the other ...
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