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I have plotted my predictors versus my response variable in order to be able to fit a good Poisson model to my data. However i am getting those scatter plots for nearly all predictors which make me rather worried since including these predictors is making my residual deviance a value of 575 on 571 df(high enough) Thanks scatterplot

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    $\begingroup$ (1) did you have the variables "the wrong way around"? (2) With discrete variables only taking a few values, consider doing a table rather than a plot, to start with; (3) If you do do a plot, consider jittering both variables, rather than only one; (4) can you tell us something about the two variables? Why does 'ageveh' only take the values 0 and 1? (5) Why are you worried?? $\endgroup$ – Glen_b Feb 27 '14 at 5:58
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    $\begingroup$ How can the residual variance be 650? That makes no sense for the data you show. Do you mean something else? $\endgroup$ – Glen_b Feb 27 '14 at 6:44
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    $\begingroup$ All the data values shown are between 0 and 8. Therefore a model with only an intercept on that data will not have a residual standard deviation exceeding 4 (probably more like 2.5). Therefore, none of the fitted models can have a residual variance exceeding 16. I ask again, did you mean something other than residual variance for the value of 650? Perhaps residual deviance? What makes the value surprising? What was the d.f.? When I asked for more details, I meant you should put them in your question -- other readers shouldn't have to read the comments to figure out what you're worried about. $\endgroup$ – Glen_b Feb 27 '14 at 8:47
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    $\begingroup$ A residual deviance of 575 on 571 df is pretty much exactly what you'd expect if your model was correct! Why do you think it's large? $\endgroup$ – Glen_b Feb 27 '14 at 11:43
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    $\begingroup$ You might find reading from about halfway down this page helpful. $\endgroup$ – Glen_b Feb 27 '14 at 13:37

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