Search Results
| Search type | Search syntax |
|---|---|
| Tags | [tag] |
| Exact | "words here" |
| Author |
user:1234 user:me (yours) |
| Score |
score:3 (3+) score:0 (none) |
| Answers |
answers:3 (3+) answers:0 (none) isaccepted:yes hasaccepted:no inquestion:1234 |
| Views | views:250 |
| Code | code:"if (foo != bar)" |
| Sections |
title:apples body:"apples oranges" |
| URL | url:"*.example.com" |
| Saves | in:saves |
| Status |
closed:yes duplicate:no migrated:no wiki:no |
| Types |
is:question is:answer |
| Exclude |
-[tag] -apples |
| For more details on advanced search visit our help page | |
Results tagged with regression
Search options not deleted
user 107799
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
0
votes
0
answers
56
views
What is the main conceptual difference between GEE and GLM? [duplicate]
I have been often using GEE for complex panel analyses. However, it occurs to me that in many cases GLM would have provided similar results.
What is the main conceptual difference between the two?
- 2,622
7
votes
Can simple linear regression be done without using plots and linear algebra?
I can propose not a visual explanation to linear regression, but something very close: a tactile explanation to linear regression.
Imagine you are entering a room from a door. … The final path you will take to reach the next door, guided by this people, is analogous to a regression line, which minimizes the distance between these people, and brings you toward the door, close to …
- 2,622
1
vote
Is there a Regression Model for "Negative Count Data?"?
Still, I believe that a linear regression model could be sufficiently robust. …
- 2,622
0
votes
Calculate a slope with only 3 points?
I think that as long as you recognize the limitations of your inference, eg by confidence intervals, or bootstrap, then you can proceed.
Of course, you can't believe that your analysis will have an ou …
- 2,622
0
votes
How big is the risk for regression in a single arm meta-analysis?
The risk of regression to the mean is always present when conducting an aggregate level meta-analysis. … article=1012&context=articles
https://us.sagepub.com/en-us/nam/correlation-and-regression/book17559
In any case, my pragmatic approach would be for you to proceed any way. …
- 2,622
1
vote
Linear regression or mixed effects models for data with two time points?
I recommend to perform multiple imputation (eg with mice in R), and then use a mixed model or generalizing estimating equations, explicitly recognizing the clustering features.
Reliance on multiple i …
- 2,622
1
vote
Adjust for covariables even in randomized experiments?
It has been shown that covariate adjustment increases precision and reduces bias, eg by Lee.
However, it is not commonly performed.
In addition, what would you do in a scenario in which unadjusted a …
- 2,622
1
vote
0
answers
77
views
Can we apply propensity scores or the Miettinen multivariate confounder score to meta-regres...
It basically consists on a regression model with study-wise weighting depending on study precision. … One of the main drawbacks of meta-regression is however its inefficiency and need for relatively large samples of studies. …
- 2,622
0
votes
The necessary analyses to perform on a dataset before running a GEE
If you follow the examples in the Stata xtgee help you should have good guidance on what to do.
Imagine you have the following dataset:
In case you want to check the association between gender and …
- 2,622
4
votes
1
answer
981
views
Using limited independent variables in a multivariable regression model
Further to my prior question on multivariable adjustment in regression models, using covariates which are available only for some cases, I have researched in some detail the main methods for limited dependent … Should I instead conduct two separate analyses and then pool the regression coefficients according to their standard error? Or is there any other more reasonable approach? …
- 2,622
1
vote
0
answers
316
views
Regression discontinuity vs propensity score matching
I have recently read some pieces suggesting that regression discontinuity designs could be the best statistical approach for causal inference stemming from non-randomized studies (eg 1 and 2). … claim for accuracy and validity has been made so far for propensity score matching (and possibly inverse probability of treatment weighting) (eg 3)
Thus, what is the comparative accuracy and validity of regression …
- 2,622