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I am really confused on how to interprete these 3 different models.

Can anyone explain this to me with the help of this little example maybe.

I was thinking that i would test the relationship between sunshine and sales.

Let us say that in my basic model without controlling for user heterogeneity, sunshine negatively effects sales.

Location fixed-effects: Let us say that in this model sunshine is no longer significant. What would that mean for the interpretation? Would that suggest, that sunshine is only important for different locations?

Random effects Let us say that in this model sunshine is no longer significant. What would that mean for the interpretation?

Fixed effects Let us say that in this model sunshine is no longer significant. What would that mean for the interpretation?

Thank you very much in advance, any literature would also be intresting.

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Before going into the details of the interpretation, I suggest first that you understand the reason why a particular variable is included in the model as random or fixed, as the practical interpretation varies according to the problem and the reason why the variable entered as fixed or random. See this post:

What is the difference between fixed effect, random effect and mixed effect models?

After deciding how the variable enters the model and how that model will be, I also suggest that you ask another question about the interpretation of your model by adding this information, because at the moment the question is very wide and this makes it difficult for people to answer and you find the ideal answer.

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