## Add variables into an interation

interpreting interaction terms continuous variables

interpreting interaction effects in multiple regression

interaction effect graph

interpreting interaction effects in multiple regression spss

interpreting interaction terms stata

interaction between categorical variables python

spss regression - interaction categorical variables

I would like to use a variable present into my partial to change the result of my iteration. To illustrate that let's take an example to be more explicit.

My variable would be @cat = 2

And I would like to run this iteration :

Post.where(cat#{@cat}: true).each do |post|

Witch should give that :

Post.where(cat2: true).each do |post|

But I don't know how I can do it. Any ideas ?

You can use string interpolation to achieve this. Check the below example:

@variable = 'column' Post.where("post_#{@variable}": true).each {|x| puts x.inspect}

**Interpreting Interactions in Regression,** Adding interaction terms to a regression model can greatly expand understanding of the relationships among the variables in the model and allows more Height = B0 + B1*Bacteria + B2*Sun + B3*Bacteria*Sun Adding an interaction term to a model drastically changes the interpretation of all the coefficients. If there were no interaction term, B1 would be interpreted as the unique effect of Bacteria on Height.

In this case `cat2`

is not a "variable", but a symbol/string, which makes it easier. This should work as desired:

Post.where("cat#{@cat}" => true).each do |post|

In general case, however, it's a very bad idea to generate variable names like that. It's a sign that you've likely chosen a wrong data structure for the job.

**Understanding Interaction Between Dummy Coded Categorical ,** Understanding Interaction Between Dummy Coded Categorical Variables in So we'll need to add to the constant the value of being married, of being male and In regression, an interaction effect exists when the effect of an independent variable on a dependent variable changes, depending on the value(s) of one or more other independent variables. Interaction Effects in Equations. In a regression equation, an interaction effect is represented as the product of two or more independent variables.

try this code, suggestions are welcome

@cat = 2 Post.where(:"cat#{@cat}" => true).each do |post|

**Understanding Interaction Effects in Statistics,** Interaction effects occur when the effect of one variable depends on another variable. All statistical software allow you to add interaction terms in a model. Before running the regression, I add interaction variable (β2 Total sales*industry) to the above model, where total sales is continuous variable in USD and industry is a dichotomous variable where industry = 1 for consumer, 0 otherwise (regression 1), 1 for Hi-Technology, 0 otherwise (regression 2), 1 for Manufacturing, 0 otherwise (regression 3).

**Which interaction terms should I include for my regression analysis?,** IF you continue using R&D as one of variables in the interaction term, then one interaction term is to include R&D, then add a constant to each variable in the The easiest way to create an interaction of one variable with all variables is: poisson Depresion_1 i.SEXO2##(i.ns10_recod i.accidente i.familia i.estres_financiero c.EDAD), irr You need to put a “c.” in front of continuous variables such as age (EDAD).

**Can I keep interaction term without original terms to avoid ,** Yes another way of dealing with correlated variables is to add, multiply them. So what you do by only keeping the interaction term in the equation, is just this If you are committed to proc reg, rather than the many other linear modeling procs, you will have to create the interaction variable in a data step. You will probably have to do something like: data test3; set test2; x1_x4=x1*x4; run; proc reg data=test3; model y = x1 x2 x3 x4 x1_x4; Hope this gets at what you are trying to accomplish. Steve Denham

**[PDF] Interaction terms and prediction in linear regression,** the effect of both together for any dose, we simply add the two individual effects. the interaction depends on the level of the other variable in the interaction. For continuous variables, you only need to multiply two variables to form an interaction (again after mean-centering or standardizing if you wish). When categorical variables are involved, you can create an interaction term by first creating separate numerical variables that correspond to contrasts of interest.

##### Comments

- Thanks @Abhilash Reddy, that's exactly what I'm looking for
- No need for symbolizing, btw. Strings would work just as well.