# Tag Info

5

The two models yield different results because they are, well, different models. Clearly you are interested in the "effects" of education and year and naturally you include an interaction between them, which is fine. age and gender are presumably a potential confounder, hence this is also correclty included. So the question really comes down to how ...

4

Feature interaction seems to be just new (machine learning?) terminology for plain old interaction. As such there is already many posts at this site, see this list.

3

One possible explanation here is that you don't have sufficient statistical power to detect a "significant" interaction effect. We can do a simple simulation to show this. We simulate data similar to that in the question: set.seed(48) dt <- expand.grid(car = c("A", "B", "C"), gas = c("petrol", "...

3

Quoted from the OP's link: 5.4 Feature Interaction When features interact with each other in a prediction model, the prediction cannot be expressed as the sum of the feature effects, because the effect of one feature depends on the value of the other feature. Aristotle's predicate "The whole is greater than the sum of its parts" applies in the ...

3

Since you want to address non-STEM people, I assume that you want to convey the meaning of $H$ in an intuitive manner. In this case, the sum, the square inside, and the denominator are not the most important things here. Let's assume that your model predicts one response variable and has two independent variables or features. Simply put, the statistic $H$ ...

2

You are correct that your Model 2 makes the most sense if you wish to standardize your continuous predictor C. The confusion comes from what the intercept and the coefficient for the binary predictor B mean in Model 0 versus Model 2. I assume that Stata is using treatment coding of the predictors, and that by standardizing C you mean subtracting its mean and ...

2

You have given proper justification in your question. If you want to know the effect of one with the rest held constant, use a main effects model. The principle of marginality states that main effects do not exist in the presence of an interaction, so beware that this is only valid if there is indeed only a negligible interaction effect. Note that the ...

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