Interaction between two predictors

Sorry if my question is pretty basilar, I am trying to learn about interaction between the predictors, but still I don’t understand for what an interaction is used. For example, when making a multiple regression when the DEPENDENT variable is gasoline required by a car to make 100km in 1 hour, and the predictors are the atmospherical weather (bad condition marked as ”B” and good atmospherical weahter marked as ”G”) , the state of the road ( variable ranging from 1 ”optimal condition” to 0 ”bad condition” ) and the speed of the driver who is driving the car (in KM/h) If I would make a linear regression I would put it like this: Y ~ atmospherical weather + the state of the road + speed of the driver who is driving the car ​ Now I don’t know the usefulness of making a regression making an interaction between two of the predictors. ​ Basically I want to know the usefulness of interaction when I am making regressions. ​ If I would have written (I am doing it with R software) : ​ Y ~ atmospherical weather : the state of the road + speed of the driver who is driving the car or Y ~ atmospherical weather * the state of the road + speed of the driver who is driving the car ​ What would have been changed from: Y ~ atmospherical weather + the state of the road + speed of the driver who is driving the car ​ Which is the difference? What would have the first two lines of code explained to me that the last one would’nt have explained to me? What more? ​ Thank you, I hope I have made it clear for you to understand, also just feel free to correct me if I am doing somenthing wrongin this regression analysis (maybe I am missing somenthing, or I have mispelled somenthing, or I have missed somenthing to put in the model above) ​ Please forgive me, I am still at the beginning ​ ​ ​ ​ ​ ​ ​ submitted by Nevin Manimala /u/luchins [link] [comments]

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Nevin Manimala

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