3 deleted 96 characters in body
source | link

You'll notice that in your ANOVA's (deviance tables) of the models there is no difference in the main effects with, or without the interaction. You don't have to know how to interpret the deviance table, just recognize that there's no difference! Keep

Keep in mind that your "Estimate" column in the regression is about the magnitude of the slope and the associated tests are of that magnitude. Your ANOVA deviances, or MS values are about the variance accounted for. When you add interactions you can change how the slope is calculated and change it's significance. But without some kind of multi-collinearity issues the amount of variance accounted for remains the same That doesn't mean it went away, it just means it's qualified by an interaction.

So, a short answer is that, if you had a main effect without the interaction then you have a main effect. It's very common to do the additive and then additive+interaction models separately so you can see where your main effects are and then look at your interactions. The fact that it went away gives you some clues about the kind of interaction that you have but it's hard for someone to answer the whole thing with just what you've reported. Your next step is to start making some graphs. For example, make one with obdobinehn at different levels of kraj.

You should really look at a paper on interpreting these interaction effects. A complete answer for your query is far too difficult to do based on what you've provided, and even guidance about where to go next is very involved. Read the linked paper, see how far you get, and get back to the SE with more questions at that time.

You'll notice that in your ANOVA's (deviance tables) of the models there is no difference in the main effects with, or without the interaction. You don't have to know how to interpret the deviance table, just recognize that there's no difference! Keep in mind that your "Estimate" column in the regression is about the magnitude of the slope and the associated tests are of that magnitude. Your ANOVA deviances, or MS values are about the variance accounted for. When you add interactions you can change how the slope is calculated and change it's significance. But without some kind of multi-collinearity issues the amount of variance accounted for remains the same.

So, a short answer is that, if you had a main effect without the interaction then you have a main effect. It's very common to do the additive and then additive+interaction models separately so you can see where your main effects are and then look at your interactions. The fact that it went away gives you some clues about the kind of interaction that you have but it's hard for someone to answer the whole thing with just what you've reported. Your next step is to start making some graphs. For example, make one with obdobinehn at different levels of kraj.

You should really look at a paper on interpreting these interaction effects. A complete answer for your query is far too difficult to do based on what you've provided, and even guidance about where to go next is very involved. Read the linked paper, see how far you get, and get back to the SE with more questions at that time.

You'll notice that in your ANOVA's (deviance tables) of the models there is no difference in the main effects with, or without the interaction. You don't have to know how to interpret the deviance table, just recognize that there's no difference!

Keep in mind that your "Estimate" column in the regression is about the magnitude of the slope and the associated tests are of that magnitude. When you add interactions you can change how the slope is calculated and change it's significance. That doesn't mean it went away, it just means it's qualified by an interaction.

So, a short answer is that, if you had a main effect without the interaction then you have a main effect. It's very common to do the additive and then additive+interaction models separately so you can see where your main effects are and then look at your interactions. The fact that it went away gives you some clues about the kind of interaction that you have but it's hard for someone to answer the whole thing with just what you've reported. Your next step is to start making some graphs. For example, make one with obdobinehn at different levels of kraj.

You should really look at a paper on interpreting these interaction effects. A complete answer for your query is far too difficult to do based on what you've provided, and even guidance about where to go next is very involved. Read the linked paper, see how far you get, and get back to the SE with more questions at that time.

2 deleted 8 characters in body
source | link

You'll notice that in your ANOVA's (deviance tables) of the models there is no difference in the main effects with, or without the interaction. You don't have to know how to interpret the deviance table, just recognize that there's no difference! Keep in mind that your "Estimate" column in the regression is about the magnitude of the slope and the associated tests are of that magnitude. Your ANOVA deviances, or MS values are about the variance accounted for. When you add interactions you can change how the slope is calculated and change it's significance. But without some kind of multi-collinearity issues the amount of variance accounted for remains the same.

So, a short answer is that, if you had a main effect without the interaction then you have a main effect. It's very common to do the additive and then additive+interaction models separately so you can see where your main effects are and then look at your interactions. The fact that it went away gives you some clues about the kind of interaction that you have. But but it's impossiblehard for someone to answer the whole thing with just what you've reported. Your next step is to start making some graphs. For example, make one with obdobinehn at different levels of kraj.

You should really look at a paper on interpreting these interaction effects. A complete answer for your query is far too difficult to do based on what you've provided, and even guidance about where to go next is very involved. Read the linked paper, see how far you get, and get back to the SE with more questions at that time.

You'll notice that in your ANOVA's (deviance tables) of the models there is no difference in the main effects with, or without the interaction. You don't have to know how to interpret the deviance table, just recognize that there's no difference! Keep in mind that your "Estimate" column in the regression is about the magnitude of the slope and the associated tests are of that magnitude. Your ANOVA deviances, or MS values are about the variance accounted for. When you add interactions you can change how the slope is calculated and change it's significance. But without some kind of multi-collinearity issues the amount of variance accounted for remains the same.

So, a short answer is that, if you had a main effect without the interaction then you have a main effect. It's very common to do the additive and then additive+interaction models separately so you can see where your main effects are and then look at your interactions. The fact that it went away gives you some clues about the kind of interaction that you have. But it's impossible for someone to answer the whole thing with just what you've reported. Your next step is to start making some graphs. For example, make one with obdobinehn at different levels of kraj.

You should really look at a paper on interpreting these interaction effects. A complete answer for your query is far too difficult to do based on what you've provided, and even guidance about where to go next is very involved. Read the linked paper, see how far you get, and get back to the SE with more questions at that time.

You'll notice that in your ANOVA's (deviance tables) of the models there is no difference in the main effects with, or without the interaction. You don't have to know how to interpret the deviance table, just recognize that there's no difference! Keep in mind that your "Estimate" column in the regression is about the magnitude of the slope and the associated tests are of that magnitude. Your ANOVA deviances, or MS values are about the variance accounted for. When you add interactions you can change how the slope is calculated and change it's significance. But without some kind of multi-collinearity issues the amount of variance accounted for remains the same.

So, a short answer is that, if you had a main effect without the interaction then you have a main effect. It's very common to do the additive and then additive+interaction models separately so you can see where your main effects are and then look at your interactions. The fact that it went away gives you some clues about the kind of interaction that you have but it's hard for someone to answer the whole thing with just what you've reported. Your next step is to start making some graphs. For example, make one with obdobinehn at different levels of kraj.

You should really look at a paper on interpreting these interaction effects. A complete answer for your query is far too difficult to do based on what you've provided, and even guidance about where to go next is very involved. Read the linked paper, see how far you get, and get back to the SE with more questions at that time.

1
source | link

You'll notice that in your ANOVA's (deviance tables) of the models there is no difference in the main effects with, or without the interaction. You don't have to know how to interpret the deviance table, just recognize that there's no difference! Keep in mind that your "Estimate" column in the regression is about the magnitude of the slope and the associated tests are of that magnitude. Your ANOVA deviances, or MS values are about the variance accounted for. When you add interactions you can change how the slope is calculated and change it's significance. But without some kind of multi-collinearity issues the amount of variance accounted for remains the same.

So, a short answer is that, if you had a main effect without the interaction then you have a main effect. It's very common to do the additive and then additive+interaction models separately so you can see where your main effects are and then look at your interactions. The fact that it went away gives you some clues about the kind of interaction that you have. But it's impossible for someone to answer the whole thing with just what you've reported. Your next step is to start making some graphs. For example, make one with obdobinehn at different levels of kraj.

You should really look at a paper on interpreting these interaction effects. A complete answer for your query is far too difficult to do based on what you've provided, and even guidance about where to go next is very involved. Read the linked paper, see how far you get, and get back to the SE with more questions at that time.