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I have the following data:

Code   EBP       EBM      Genre    Experience  Zone  Time

 1     4.35      4.83        0         5         0      1
 2     4.2       4.1         1         5         0      1           
 
...

1     3.8        4,23        1         0          5     2

And I am tasked with creating a linear regression to predict the values EBP (this is a scale from 0-5 that measures the satisfaction with the company) and EBM (also a 0-5 scale that measures the satisfaction with the clients) of the whole staff. It has been measured two times for each individual defined by the variable time with values 1 and 2. From previous experiences with similar data I've been told that the model would be linear and I have two measurements so I thought doing a paired repeated measures.

What I am trying to do is create a linear model where I can predict and measure the impacts of genre, Experience, Zone and so on. I´ve alredy done t-test and d-cohen to try to measure the impact.

I am not that experienced so I feel I am confusing thing and tried porgramming :

empleo.nested<-lmer( EBP~ ns(Time, 2) * Experience + Genre + 
                     (Time | Genero),data = Empleo)

But I feel I am missing things, also tried de ezAnova but it is not quite working out because of NAs.

Any help is deeply appreciated

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Diego Fernández Baena is a new contributor to this site. Take care in asking for clarification, commenting, and answering. Check out our Code of Conduct.
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    $\begingroup$ These three rows say very little about the data or the problem you are trying to solve. Where does the data come from and how was it collected? Why do mean by "simple paired regression"? Why do you think there are mixed effects? What do the variables Code, EBP, EBM, EBT, Genre, Experience, Zone and Time represent? ... $\endgroup$
    – dipetkov
    Sep 23 at 19:06
  • $\begingroup$ I tried to explain it, any more clarifications that you need I'll be happy to explain $\endgroup$ yesterday

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