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GLM model not corresponding to exploratory analysis (R-studio)


Quantitative Analysis of Structure of Gaussian Mixture ModelTime series analysis ARCH($m$) modelTopics under Model Based Cluster AnalysisTime series analysis, moving-average model, ARMA modelsensitivity analysis of a modelFind the MLE of a GLMConstructing a 95% Confidence Interval (using output from r-studio)Why use the log_e function as link function for Poisson data in a GLM modelAnalysis of model fitDefinition of the fitted values of a GLM?













0












$begingroup$


I am working with a dataset from a job center that specialises in finding work placements for individuals with a criminal record.



There are 2 continuous factors (age, number of pre-convictions) and 4 discrete factors (placement, re-convicted, age group (grouped into 3 levels) and number of pre-convictions (grouped into 3 levels).



My exploratory analysis (consisting of Pearson's Chi-squared test, confidence regions, box plots and barcharts) suggested a strong relationship between number of pre-convictions and getting re-convicted, also with getting a work placement and getting re-convicted. I also found a weak (non-significant) relationship between age of individuals and getting re-convicted. However when I look at the summary data for the saturated model, the significance of my co-efficients seems to suggest the opposite to my analysis.



Could someone please explain what I am doing wrong? Or is this okay? I figured this model would be a good place to start and it would support my findings. Is this the correct approach for finding a suitable GLM?



Here is an image of the summary data as well as the factors



Thanks!










share|cite|improve this question









$endgroup$











  • $begingroup$
    Just in case you get no response here, try the statistics site after a while. Don't forget to mention the cross-post here if/when you do.
    $endgroup$
    – Lee David Chung Lin
    Mar 13 at 22:05















0












$begingroup$


I am working with a dataset from a job center that specialises in finding work placements for individuals with a criminal record.



There are 2 continuous factors (age, number of pre-convictions) and 4 discrete factors (placement, re-convicted, age group (grouped into 3 levels) and number of pre-convictions (grouped into 3 levels).



My exploratory analysis (consisting of Pearson's Chi-squared test, confidence regions, box plots and barcharts) suggested a strong relationship between number of pre-convictions and getting re-convicted, also with getting a work placement and getting re-convicted. I also found a weak (non-significant) relationship between age of individuals and getting re-convicted. However when I look at the summary data for the saturated model, the significance of my co-efficients seems to suggest the opposite to my analysis.



Could someone please explain what I am doing wrong? Or is this okay? I figured this model would be a good place to start and it would support my findings. Is this the correct approach for finding a suitable GLM?



Here is an image of the summary data as well as the factors



Thanks!










share|cite|improve this question









$endgroup$











  • $begingroup$
    Just in case you get no response here, try the statistics site after a while. Don't forget to mention the cross-post here if/when you do.
    $endgroup$
    – Lee David Chung Lin
    Mar 13 at 22:05













0












0








0





$begingroup$


I am working with a dataset from a job center that specialises in finding work placements for individuals with a criminal record.



There are 2 continuous factors (age, number of pre-convictions) and 4 discrete factors (placement, re-convicted, age group (grouped into 3 levels) and number of pre-convictions (grouped into 3 levels).



My exploratory analysis (consisting of Pearson's Chi-squared test, confidence regions, box plots and barcharts) suggested a strong relationship between number of pre-convictions and getting re-convicted, also with getting a work placement and getting re-convicted. I also found a weak (non-significant) relationship between age of individuals and getting re-convicted. However when I look at the summary data for the saturated model, the significance of my co-efficients seems to suggest the opposite to my analysis.



Could someone please explain what I am doing wrong? Or is this okay? I figured this model would be a good place to start and it would support my findings. Is this the correct approach for finding a suitable GLM?



Here is an image of the summary data as well as the factors



Thanks!










share|cite|improve this question









$endgroup$




I am working with a dataset from a job center that specialises in finding work placements for individuals with a criminal record.



There are 2 continuous factors (age, number of pre-convictions) and 4 discrete factors (placement, re-convicted, age group (grouped into 3 levels) and number of pre-convictions (grouped into 3 levels).



My exploratory analysis (consisting of Pearson's Chi-squared test, confidence regions, box plots and barcharts) suggested a strong relationship between number of pre-convictions and getting re-convicted, also with getting a work placement and getting re-convicted. I also found a weak (non-significant) relationship between age of individuals and getting re-convicted. However when I look at the summary data for the saturated model, the significance of my co-efficients seems to suggest the opposite to my analysis.



Could someone please explain what I am doing wrong? Or is this okay? I figured this model would be a good place to start and it would support my findings. Is this the correct approach for finding a suitable GLM?



Here is an image of the summary data as well as the factors



Thanks!







statistics






share|cite|improve this question













share|cite|improve this question











share|cite|improve this question




share|cite|improve this question










asked Mar 13 at 18:54









user553480user553480

62




62











  • $begingroup$
    Just in case you get no response here, try the statistics site after a while. Don't forget to mention the cross-post here if/when you do.
    $endgroup$
    – Lee David Chung Lin
    Mar 13 at 22:05
















  • $begingroup$
    Just in case you get no response here, try the statistics site after a while. Don't forget to mention the cross-post here if/when you do.
    $endgroup$
    – Lee David Chung Lin
    Mar 13 at 22:05















$begingroup$
Just in case you get no response here, try the statistics site after a while. Don't forget to mention the cross-post here if/when you do.
$endgroup$
– Lee David Chung Lin
Mar 13 at 22:05




$begingroup$
Just in case you get no response here, try the statistics site after a while. Don't forget to mention the cross-post here if/when you do.
$endgroup$
– Lee David Chung Lin
Mar 13 at 22:05










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