Questions tagged [regression]

Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.

"Regression" is a general term for a wide variety of techniques to analyze the relationship between one (or more) dependent variables and independent variables. Typically the dependent variables are modeled with probability distributions whose parameters are assumed to vary (deterministically) with the independent variables.

Ordinary least squares (OLS) regression affords a simple example in which the expectation of one dependent variable is assumed to depend linearly on the independent variables. The unknown coefficients in the assumed linear function are estimated by choosing values for them that minimize the sum of squared differences between the values of the dependent variable and the corresponding fitted values.

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What happens if the explanatory and response variables are sorted independently before regression?

Suppose we have data set $(X_i,Y_i)$ with $n$ points. We want to perform a linear regression, but first we sort the $X_i$ values and the $Y_i$ values independently of each other, forming data set $(X_i,Y_j)$ . Is there any meaningful interpretation…
arbitrary user
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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 model. To paraphrase Shalizi's example: suppose…
raegtin
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Interpretation of R's lm() output

The help pages in R assume I know what those numbers mean, but I don't. I'm trying to really intuitively understand every number here. I will just post the output and comment on what I found out. There might (will) be mistakes, as I'll just write…
Alexander Engelhardt
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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?
d_2
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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, age, etc) in order to best isolate the true…
JackOfAll
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When should I use lasso vs ridge?

Say I want to estimate a large number of parameters, and I want to penalize some of them because I believe they should have little effect compared to the others. How do I decide what penalization scheme to use? When is ridge regression more…
Larry Wang
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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: Warning message: glm.fit: fitted probabilities numerically 0 or 1 occurred We…
user333
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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 other does not, I notice that the $R^2$ of the…
analyticsPierce
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How are the standard errors of coefficients calculated in a regression?

For my own understanding, I am interested in manually replicating the calculation of the standard errors of estimated coefficients as, for example, come with the output of the lm() function in R , but haven't been able to pin it down. What is the…
ako
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When is R squared negative?

My understanding is that $R^2$ cannot be negative as it is the square of R. However I ran a simple linear regression in SPSS with a single independent variable and a dependent variable. My SPSS output give me a negative value for $R^2$. If I was to…
Anne
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Why L1 norm for sparse models

I am reading books about linear regression. There are some sentences about the L1 and L2 norm. I know the formulas, but I don't understand why the L1 norm enforces sparsity in models. Can someone give a simple explanation?
Yongwei Xing
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What is the difference between linear regression on y with x and x with y?

The Pearson correlation coefficient of x and y is the same, whether you compute pearson(x, y) or pearson(y, x). This suggests that doing a linear regression of y given x or x given y should be the same, but I don't think that's the case. Can someone…
user9097
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Why does a time series have to be stationary?

Would like to understand primary reasons for making a data stationary? I understand that a stationary time series is one whose mean and variance is constant over time. Can someone please explain why we have to make sure our data set is stationary…
alex
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What is the difference between linear regression and logistic regression?

What is the difference between linear regression and logistic regression? When would you use each?
B Seven
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Is it necessary to scale the target value in addition to scaling features for regression analysis?

I'm building regression models. As a preprocessing step, I scale my feature values to have mean 0 and standard deviation 1. Is it necessary to normalize the target values also?
user2806363
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