What is an interaction? Describe an example and identify the variables within your population (work, social, academic, etc.) for which you might expect interactions

ADDITIONAL DETAILS 

What is an interaction in statistics?

Introduction

Interaction terms are a special kind of effect in which more than one predictor variable is considered at a time. For example, if we are trying to predict the probability that someone will have diabetes based on their gender and age, then we might consider adding age and gender together in order to see if either one of those variables could help us predict the other. If so, this would be an interaction term because there are two variables being considered at once when predicting something else (e.g. having diabetes). Interaction terms can be added to any kind of statistical model and not just regression models—in fact, they’re typically added to causal models as well!

Interaction terms are also called interaction effects.

An interaction term is also called an interaction effect.

Interaction terms can be added to any kind of statistical model, not just regression models. They are frequently used in multiple linear regression and logistic regression models (among others). There is no limit to the number of interaction terms you can add to a model—you may have hundreds or even thousands!

Interaction terms can be added to any kind of statistical model, not just regression models.

Interaction terms can be added to any kind of statistical model, not just regression models. An interaction term is an additional explanatory variable that has an effect on the dependent variable when another variable is also present.

For example, let’s say you’re studying how people use social media in different countries and cities. You’ve collected data from 10 different countries and 10 different cities (USA: NYC; UK: London) with the following results:

• NYC users spend more time looking at pictures than videos or text posts; London users spend more time looking at videos than pictures or text posts; San Francisco users spend more time looking at both types of content than anywhere else on our list…and so forth.* *

If we add an interaction term into our model where each country equals one dummy variable representing whether it belongs to Europe (which has higher spending levels), then we can see what happens when we increase spending levels in these other countries by adding those dummy variables into our final equation above.*

Interaction terms are typically added to regression models in order to see if their addition significantly improves the fit of that model.

As an example, let’s say you’re trying to model the relationship between weight and height. You could use a linear regression model with one predictor variable (weight) and two independent variables (height). This would give you something similar to this:

This is all well and good, but what if we wanted to know if there was an interaction between these two factors? If so, then the coefficients for each term would have different signs and magnitudes than those without interaction terms. For example:

If our goal was simply just knowing whether or not there was an interaction effect on average weight change over time between our inputs of height and age at baseline measurement point in this case then adding our new parameterized term wouldn’t really help us much since it doesn’t tell us anything about how particular combinations fared relative to others!

Conclusion

Interaction terms are a powerful tool in the hands of statisticians. They allow us to derive new models that can help us better understand our data, and they also provide a valuable method for exploring causal relationships between variables. As we’ve seen above, there are many different types of interactions that statisticians have studied over time, with different properties and uses depending on the type of model being analyzed. I hope this article has given you a good overview of what interaction terms are and how they fit into statistical modeling as well as some examples where they’re used commonly today!