coffeinjunky
• Member for 8 years, 3 months
• Last seen more than a month ago
• London, UK

If your question is: what is the difference between these two codes? A look at ?glm says See family for details of family functions, and a look at ?family reveals the following description: The ...

For people with a similar question, let me provide a simple outline of the answer. The trick is to set up the two equations as a system of seemingly unrelated equations and to estimate them jointly. ...

You might understand this behavior better if you look at the model matrices. model.matrix(lm1 <- lm(y ~ r*s, data=d)) model.matrix(lm2 <- lm(y ~ r + s + rs, data=d)) When you look at these ...

Aksakal's answer is correct. By controlling for all variables in a regression, you "keep them constant" and you are able to identify the partial correlation between your regressor of interest. Let me ...

Just to add a small explanation: as already pointed out in the comments to your question, the density itself can be above 1. The basic requirement is that it integrates to 1, i.e. that if the support ...

I have also recently become interested in data science as a career, and when I think of what I learnt about the data science job in comparison to the numerous statistics courses that I took (and ...

When you ask if centering is a valid solution to the problem of multicollinearity, then I think it is helpful to discuss what the problem actually is. I say this because there is great disagreement ...

I fully agree with Andy, and I was actually thinking of writing something similar, but then I started to wonder myself about this topic. I think we all agree that Granger causality itself really has ...

There is not necessarily a problem in terms of getting estimates for each of the components. There is however a problem of interpreting them, as has been outlined above already. Having said that, ...

For what it is worth, the simple answer is: you can't. Your columns are linearly dependent, and you run into multicollinearity/incidential parameter issues. For instance, df <- data.frame(Noutside, ...

There are many so-called quasi-experimental methods with which you can credibly argue about causality, even though your data are observational. These methods typically rely on finding a source of ...

This is more a question for CrossValidated, but for what it is worth, the intercept is capturing the grand mean and the remaining factors the differences to the overall mean (as if all those other ...

You write: Both of the following approaches should lead to the same results in my opinion As I have outlined in my earlier answer, I think this is not correct. The reason is first: as far as I ...

To start with, and given that this is a school project, one simple approach would be to look at the $R^2$ of the two regressions. They tell you how much of the variation in the dependent variable is ...

Despite your feelings, you might want to try multiple regression nevertheless. Put your two populations into one regression and add an indicator for group membership (i.e. to which population does the ...

Strictly speaking, whenever you can express one variable in terms of a linear combination of one or many other variables (which are included in your regression model), then you have perfect ...

Strictly speaking, you run the same regression as if the $j$-th variable was your dependent variable (and you exclude that from your independent variables). The corresponding $R^2$ will be your \$R^2_{...