# Questions tagged [regression]

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

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### is it possible for odds ratios' or risk ratios' confidence interval to contain 1 and still be statistically significant?

my understanding is that if a 95% CI for odds ratios and risk ratios contains 1 then it is not statistically significant. However I have seen in studies such as https://pubmed.ncbi.nlm.nih.gov/...
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### When does R-squared in multiple linear regression equals the sum of the R-squared from two simple linear regression?

I know that in the simple linear regression, the $R^2$ is just the sample correlation between the response and covariate. My question is that suppose I fit a linear regression by using two covariates, ...
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### What estimation methods other than ordinary least squares guarantee the orthogonality of predictions and residuals?

From my question here, it is evident that estimation approaches to linear regression other than ordinary least squares can result in the predictions and residuals lacking orthogonality, despite the ...
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### If residuals are serially correlated does that mean they are normally distributed

In linear regression if the residuals are serially correlated does that mean then that they are normally distributed
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### Correction for multiple comparisons using sum contrasts with linear regression

I am computing the following model using the lme4 package in R: ...
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### One of the main effects not significant, but interaction term significant

A is significant B is not significant A x B is significant Do I say that (1) There exists a ...
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### For ecological data, when is a gaussian distribution appropriate?

This may seem like a very basic question, but is something I have become more confused about the more I read. Say I have a dataset with morphological measurements of various plant traits (e.g. leaf ...
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### How to determine if the log likelihood of logistic regression is too large or not?

I am running a logistic regression on STATA with binary response variable, and 2 predictor variable that are discrete, as such one is in % (but takes only 2 values strictly i.e., 5% or 10%) and ...
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### Prove that the sample covariance between observation and OLS fittings are nonnegative

I am trying to show that $$\frac{1}{n} \sum_{i = 1}^n (y_i - \overline{y})(\hat{y}_i - \overline{\hat{y}}) \geq 0$$ where $y_i$s are the observations, $\hat{y}_i$s are corresponding LS fitting values, ...
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### Build three models in one study

I build three models. First model has two mediators, one is not significant, then I removed it from the second model. The second one has one moderator and one mediator. The third one has another ...
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### What is the best model selection method for high-dimensional linear regression?

Model selection (best subset selection) in linear regression is quite important in many applications. Among the methods belonging to different frameworks such as information criterion, hypothesis ...
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### Simulated data for logistic regression

I used the code below to create the random variable x1 and binary variable y, and fit the regression with y and x1. My questions are: Why regression coefficient estimates are not close to 2 and 10 (...
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### What is better OMP or LASSO?

Variable selection in linear regression models is quite important. In this regard, the orthogonal matching pursuit (OMP) is a classical greedy approach to variable selection. On the other hand, LASSO ...
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### Calculate uncertainty of surface normal based on points uncertainty

How to calculate angular variation of fitted plane from points that has positional uncertainty with normal probability distribution? For example, I have 4 points P1 = 102.0000 84.0000 139.5443 P2 = ...
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### Knockoff filters simple explanation and importance!

Knockoff filters are new in the field of variable selection. Can someone provide (or refer to) a slightly simple understanding of the topic? Also, what is the fuzz about this new method compared to ...
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### Controlling for Variables in Binary Logistic Regression

How do you control for variables such as gender within a binary logistic regression? I have a yes/no dependent variable with several variables that are also either yes/no. I'd like to control for ...
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### Why linear mixed effect regression over multiple linear regressions for individual random effect?

I have a dataset on green-up days pan-Arctic and relate this to weather variables. I therefore want to use a linear mixed effect model where region (e.g. Alaska, ...
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### Reasoning about modelling uncertainty w.r.t input

I am trying to build up my reasoning about uncertainty modelling and ways of modelling it. What I am trying to essentially get at is how changes in input variables results in different posteriors(...
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