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I have a dataset with binary (presence/absence) species data and 5 environmental factors (pH, light, temperature, moisture, nutrients). I want to test how the five factors explain the presence of species.

Is it correct to assume that the binary species data is my response variable and the factors are my explanatory variables?

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Sometimes it can be a little confusing when there are multiple names for the role of a variable. In an analysis, the response variable is the phenomenon we wish to explain/model. It is also known as the dependent variable, the explained variable, the predicted variable, or the regressand.

Your explanatory variables represent the set of determinants of your response variable, and are otherwise called the independent variable, the control variable, the predictor variable, or the regressor.

So in your case, you wish to explain the presence/absence of species (response variable) using your five environmental factors (explanatory variables).

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