Questions tagged [regression]
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
36 questions from the last 30 days
6
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3
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466
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Is Bayesian ordinal logistic regression (OLR) a better choice than conventional OLR when certain cells have a small number of observations (<10)?
The outcome variable of my study is the level of knowledge of tobacco cessation services, with three categories: no, low, and moderate/high. The sample size is 660, but some predictors have cell ...
4
votes
4
answers
331
views
Borderline interaction p value
I’m working on a logistic regression model where I want to examine whether the effect of one continuous predictor (X1) on a binary outcome depends on another ...
6
votes
3
answers
178
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Narrow vs Broad-based U-shape comparisons
I’m modeling mortality using a multivariate logistic regression model with a nonlinear effect of X1 and I’m examining whether this relationship changes across ...
7
votes
2
answers
343
views
Use of differential equations in statistical modelling
Consider a situation where there are multiple subjects and each subject has multiple measurements (response, covariates) over time. The goal is to identify a statistical regression model which allows ...
user499545
5
votes
1
answer
222
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Number of knots in splines (internal vs total)
I’m trying to understand how natural cubic splines (splines::ns) and restricted cubic splines (rms::rcs) handle knots — ...
2
votes
2
answers
101
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lm() and glm() equivalence for log-transformed response variable [duplicate]
I can't seem to wrap my head around this:
What is the glm() equivalent for lm(log(y) ~ x1 + x2, data=data)?
Is it?
a. ...
- 121
3
votes
2
answers
169
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Understanding and interpreting Cox Regression when using ordered factors
I am trying to understand ordered factors (polynomial terms) and their interpretation in Cox Proportional Hazards regression model. I know when using lm() to fit ...
- 133
2
votes
1
answer
233
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Do regression coefficients imply anything about the relationship *between* the predictors?
I have a conceptual question about what a regression model's output implies about the raw relationship between its predictors. I'm trying to build a better intuition for what "controlling for&...
3
votes
2
answers
116
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How can I compare models in weighted multinomial logistic regression?
I want to do a regression analysis after a GMM. I have a dependent variable with three categories (classes), which differ in their posterior probabilities. That's why I included the posterior ...
- 31
1
vote
1
answer
170
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Does strict exogeneity imply uncorrelation among error terms?
Does the strict exogeneity assumption of OLS $ \mathbb{E} [\epsilon \mid X ] = 0 $ imply that the error terms of different observations are uncorrelated with one another, that is $ \text{Cov}( \...
- 3,403
2
votes
1
answer
115
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How do I estimate the linear effect for a factor so that my estimate doesn't depend on the sample size?
I’m trying to use the R poly() function with degree 1 to force glm to interpret a factor linearly. I’m puzzled by the fact that the size of the sample seems to increase the coefficient of the ...
- 45
3
votes
1
answer
92
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Advice on regression approach
How should I handle a mass-point in the dependent variable when running OLS regression in R?
I’m working with a a household expenditure dataset (Living Costs 2019) where the dependent variable is the ...
- 31
0
votes
1
answer
109
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Deciding which data points to include in a model beforehand?
This is the set up I have:
J clusters
Each cluster has Nj individuals
Some individuals have a response variable Y1, some individuals have a response variable Y2, some have both Y1 and Y2
Each ...
user499322
2
votes
2
answers
123
views
Why is my simulation not showing bias?
I am trying to observe the Nickell bias (https://www.jstor.org/stable/1911408) in simulation. For example, its said that using a lagged response as a predictor in a regression model can create a bias.
...
1
vote
0
answers
87
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Conditioning on $X=x$ and independence of errors in simple linear regression
Under random-design (the predictors are random), the simple linear regression model states that $\forall i$:
$$Y_i = b_0 + b_1X_i + \varepsilon_i$$
for some independent variable $X_i$ and error term $\...
- 135