Linked Questions

0
votes
0answers
776 views

Interpreting results of cubic regression [duplicate]

I have a simple question. I'm doing a regression with countries (346 countries). I have a variable that measures level of previous conflict. I rescaled this variable in a variable that goes from 0.0 (...
2
votes
1answer
145 views

How can my regression coefficients be so far from the underlying model? [duplicate]

I'm performing regression on data derived from a known underlying model with normally distributed errors, and I don't understand how the fitted regression coefficients can be as far as they are from ...
1
vote
0answers
62 views

Interpreting summary output in R [duplicate]

I'm fitting a linear model to a dataset. I've reduced the model from 7 variables to 3 and as part of that I've transformed one of my variables (wt) in order to get a better fit. For my final model, ...
60
votes
4answers
56k views

Does it make sense to add a quadratic term but not the linear term to a model?

I have a (mixed) model in which one of my predictors should a priori only be quadratically related to the predictor (due to the experimental manipulation). Hence, I would like to add only the ...
48
votes
3answers
95k views

What is the effect of having correlated predictors in a multiple regression model?

I learned in my linear models class that if two predictors are correlated and both are included in a model, one will be insignificant. For example, assume the size of a house and the number of ...
23
votes
5answers
7k views

Raw or orthogonal polynomial regression?

I want to regress a variable $y$ onto $x,x^2,\ldots,x^5$. Should I do this using raw or orthogonal polynomials? I looked at question on the site that deal with these, but I don't really understand ...
6
votes
1answer
11k views

I need both quadratic and linear coefficients in a GLM with binary response. What's the best option? [closed]

I have three predictors and one response. What can I do if my response variable is binary?
11
votes
2answers
1k views

If you can't do it orthogonally, do it raw (polynomial regression)

When performing polynomial regression for $Y$ onto $X$, people sometimes use raw polynomials, sometimes orthogonal polynomials. But when they use what seems completely arbitrary. Here and here raw ...
3
votes
1answer
341 views

Backtransforming the vertex of a quadratic function

I have created a model for which it was necessary to scale my predictor values by subtracting by the mean and dividing by the standard deviation of the X values. This resulted in variables centered ...
1
vote
0answers
30 views

Orthogonal polynomials lme4: Interpretation of significant quadratic predictor when linear predictor is not significant [duplicate]

Summary of Study Participants worked in pairs to complete three tasks. Periodically throughout the interaction, they evaluated one another across a variety of categories. The primary category of ...