Error term too big/can in improve my multiple regression?

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I have made regression selecting the best features (p-val<0.05). I now have the model with a 0.85 R-squared and residual of 2.64.

Well, sometimes when I try to predict some new instances, my predictions is way outside my residual deviation. I mean sometime my actual y-value is supposed to be 20 and I have 26,7.

I also fell the need to mention that when I explored my target with a boxplot; I had a maximum of 33 and outliers going up to 100 and the standard deviation for that target is nearly 7.

The good thing is, I have over a million of rows and the outliers represent about 5000 rows. Is taking them off improve my model? what can I do to improve such a model?










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    0












    $begingroup$


    I have made regression selecting the best features (p-val<0.05). I now have the model with a 0.85 R-squared and residual of 2.64.

    Well, sometimes when I try to predict some new instances, my predictions is way outside my residual deviation. I mean sometime my actual y-value is supposed to be 20 and I have 26,7.

    I also fell the need to mention that when I explored my target with a boxplot; I had a maximum of 33 and outliers going up to 100 and the standard deviation for that target is nearly 7.

    The good thing is, I have over a million of rows and the outliers represent about 5000 rows. Is taking them off improve my model? what can I do to improve such a model?










    share|cite|improve this question









    $endgroup$















      0












      0








      0





      $begingroup$


      I have made regression selecting the best features (p-val<0.05). I now have the model with a 0.85 R-squared and residual of 2.64.

      Well, sometimes when I try to predict some new instances, my predictions is way outside my residual deviation. I mean sometime my actual y-value is supposed to be 20 and I have 26,7.

      I also fell the need to mention that when I explored my target with a boxplot; I had a maximum of 33 and outliers going up to 100 and the standard deviation for that target is nearly 7.

      The good thing is, I have over a million of rows and the outliers represent about 5000 rows. Is taking them off improve my model? what can I do to improve such a model?










      share|cite|improve this question









      $endgroup$




      I have made regression selecting the best features (p-val<0.05). I now have the model with a 0.85 R-squared and residual of 2.64.

      Well, sometimes when I try to predict some new instances, my predictions is way outside my residual deviation. I mean sometime my actual y-value is supposed to be 20 and I have 26,7.

      I also fell the need to mention that when I explored my target with a boxplot; I had a maximum of 33 and outliers going up to 100 and the standard deviation for that target is nearly 7.

      The good thing is, I have over a million of rows and the outliers represent about 5000 rows. Is taking them off improve my model? what can I do to improve such a model?







      statistics linear-regression






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      share|cite|improve this question











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      share|cite|improve this question










      asked Nov 28 '18 at 21:55









      CamueCamue

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