Linked Questions

62
votes
4answers
58k 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 ...
54
votes
2answers
18k views

Is there a difference between 'controlling for' and 'ignoring' other variables in multiple regression?

The coefficient of an explanatory variable in a multiple regression tells us the relationship of that explanatory variable with the dependent variable. All this, while 'controlling' for the other ...
14
votes
4answers
23k views

“Moderation” versus “interaction”?

I have come across these two terms which are used interchangeably in many contexts. Basically, a moderator (M) is a factor that impacts on the relationship between X and Y. Moderation analysis is ...
3
votes
2answers
3k views

How seriously should I consider the effects of multicollinearity in my regression model?

I have a model y ~ x + z and the correlation between x and z is 0.2. This is only weakly positive. So, how seriously should I consider the effects of ...
5
votes
2answers
1k views

How does statistical control work in logistic regression?

I want to make sure that I can generally interpret model findings accurately. Is it fair to say that each log-odds associated with a predictor assumes that the others are held constant at 0? Making it ...
3
votes
3answers
379 views

Is this the correct way to interpret regression coefficients?

Say I have some data on people where I have a measure of their general health (some score out of 1000), the number of apples they eat in a year, and the number of oranges they eat in a year. Then I ...
4
votes
2answers
1k views

At what level are covariates held constant in multiple logistic regression?

I'm running a multiple logistic regression with several continuous and categorical covariates. I was wondering how to interpret the results of each covariate if the others are held constant. At what ...
1
vote
1answer
2k views

Odds ratio in logistic regression with multiple predictors

It seems to be commonly accepted that $e^\beta$ corresponds to the OR in logistic regressions. Although I understand that in the univariate case this definitely seems to correspond, i.e. $$ OR = \...
0
votes
1answer
2k views

What do the coefficients of the crossproduct of regression mean?

How can I interpret the coefficients of the crossproduct of each of the following codes? What do they mean? How can I deduce that they correspond to our expectation? Also which crossproduct is correct?...
2
votes
2answers
1k views

ANOVA with and without interactions giving different values for main effects

I have measured Copper content in fishing nets. I have 2 independent variables - treatment of the net with 4 levels and ...
2
votes
3answers
442 views

What is the meaning of the beta for the interaction between continuous variables in a linear mixed-model?

If I create a mixed-effects linear regression model similar to the following (using the lme4 package in R), where all of the fixed effect variables are continuous: ...
2
votes
1answer
480 views

Interpretation of 2-way ANOVA with covariates

I have a question about interpreting two-way ANOVA including a couple of categorical covariates. As I conduct the analysis, I put $T_1$, $T_2$ and $T_1\times T_2$ to represent the main effects and ...
1
vote
0answers
380 views

what is held constant in case of categorical variables when multiple levels are present?

I performed a negative binomial regression and here is my output (variable names changed from my original output): ...
2
votes
1answer
140 views

Are p-values from a Pearson's correlation equivalent to p-values in a multiple linear regressions?

I've looked through a related question and another on this topic but it seems I'm still missing something. I understand that in a multiple regression from these questions and answers that a given ...
2
votes
1answer
86 views

Coefficient marginal to interactions in linear regression

Consider this model: $$y = \beta_0 +\beta_1x_1 + \beta_2x_2 + \beta_3x_1x_2 + \varepsilon$$ Somebody told me today that the coefficient for the main effect of $x_1$ (i.e., $\beta_1$) will be '...