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Multiple regression results help



The 2019 Stack Overflow Developer Survey Results Are In
Announcing the arrival of Valued Associate #679: Cesar Manara
Planned maintenance scheduled April 17/18, 2019 at 00:00UTC (8:00pm US/Eastern)Not-significant F but a significant coefficient in multiple linear regressionWhen to transform predictor variables when doing multiple regression?Combining multiple imputation results for hierarchical regression in SPSSHow to deal with different outcomes between pairwise correlations and multiple regressionProbing effects in a multivariate multiple regressionPredictor flipping sign in regression with no multicollinearityMeta analysis of Multiple regressionWhat is the difference between each predictor's standardized betas (from multiple regression) and it's Pearson's correlation coefficient?Multiple linear regression coefficients meaningWhat is the correct way to follow up a multivariate multiple regression?



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$begingroup$


For my first ever research paper I've run a hierarchal multiple linear regression with two predictors and one outcome variable, however I don't understand my results. I've found predictor A to be a significant predictor for my outcome variable alone. However, when both my predictors are in the model, predictor A is not a significant predictor, only predictor B is. How can this be if predictor A was significant in the first model? How does predictor B change how significant predictor A is?



Thank you!










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    $begingroup$


    For my first ever research paper I've run a hierarchal multiple linear regression with two predictors and one outcome variable, however I don't understand my results. I've found predictor A to be a significant predictor for my outcome variable alone. However, when both my predictors are in the model, predictor A is not a significant predictor, only predictor B is. How can this be if predictor A was significant in the first model? How does predictor B change how significant predictor A is?



    Thank you!










    share|cite|improve this question







    New contributor




    ummmm is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
    Check out our Code of Conduct.







    $endgroup$














      2












      2








      2





      $begingroup$


      For my first ever research paper I've run a hierarchal multiple linear regression with two predictors and one outcome variable, however I don't understand my results. I've found predictor A to be a significant predictor for my outcome variable alone. However, when both my predictors are in the model, predictor A is not a significant predictor, only predictor B is. How can this be if predictor A was significant in the first model? How does predictor B change how significant predictor A is?



      Thank you!










      share|cite|improve this question







      New contributor




      ummmm is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
      Check out our Code of Conduct.







      $endgroup$




      For my first ever research paper I've run a hierarchal multiple linear regression with two predictors and one outcome variable, however I don't understand my results. I've found predictor A to be a significant predictor for my outcome variable alone. However, when both my predictors are in the model, predictor A is not a significant predictor, only predictor B is. How can this be if predictor A was significant in the first model? How does predictor B change how significant predictor A is?



      Thank you!







      multiple-regression mlr






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      ummmm is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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      asked 9 hours ago









      ummmmummmm

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          3 Answers
          3






          active

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          1












          $begingroup$

          regression coefficients reflect the simultaneous effects of multiple predictors. If the two predictors are inter-dependent (i.e. correlated) the results can differ from single input models.






          share|cite|improve this answer









          $endgroup$




















            1












            $begingroup$

            The tests in multiple regression are "added last" tests. That means they test whether the model significantly improves after including the extra variable in a regression that contains all other predictors.



            In your model with no predictors, adding A improves the model, so the test of A is significant in the model with only A.



            In a model with A already in the model, adding B improves the model, so the test of B is significant in the model with A and B. But in a model with B already in the model, adding A doesn't improve the model, so the test of A is not significant in the model with A and B. B is doing all the work that A would do, so adding A doesn't improve the model beyond B.



            As @IrishStat mentioned, this can occur when A and B are correlated (positively or negatively) with each other. It's a fairly common occurrence in regression modeling. The conclusion you might draw is that A predicts the outcome when B is not in the model (i.e., unavailable), but after including B, A doesn't do much more to predict the outcome. Unfortunately, without more information about the causal structure of your variables, there is little more interpretation available.






            share|cite|improve this answer









            $endgroup$




















              0












              $begingroup$

              To expand a little on @Noah and @IrishStat's answers, in a multiple regression, coefficients for each independent variable/predictor are estimated to obtain the direct effect of each variable, using variation unique to that variable and the variable's correlation with the outcome variable, not using variation shared by predictors. (In technical terms, we are talking about variance and covariance of these variables.) The less unique variation there is, the less significant the estimate will become.



              So why, in your example, did you end up with an insignificant predictor A when B was added, and not with a significant predictor A and insignificant predictor B? It is likely because the proportion of variance of predictor A that it has in common with predictor B is larger than the proportion of variance of predictor B that it has in common with predictor A.






              share|cite|improve this answer









              $endgroup$













                Your Answer








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                3 Answers
                3






                active

                oldest

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                3 Answers
                3






                active

                oldest

                votes









                active

                oldest

                votes






                active

                oldest

                votes









                1












                $begingroup$

                regression coefficients reflect the simultaneous effects of multiple predictors. If the two predictors are inter-dependent (i.e. correlated) the results can differ from single input models.






                share|cite|improve this answer









                $endgroup$

















                  1












                  $begingroup$

                  regression coefficients reflect the simultaneous effects of multiple predictors. If the two predictors are inter-dependent (i.e. correlated) the results can differ from single input models.






                  share|cite|improve this answer









                  $endgroup$















                    1












                    1








                    1





                    $begingroup$

                    regression coefficients reflect the simultaneous effects of multiple predictors. If the two predictors are inter-dependent (i.e. correlated) the results can differ from single input models.






                    share|cite|improve this answer









                    $endgroup$



                    regression coefficients reflect the simultaneous effects of multiple predictors. If the two predictors are inter-dependent (i.e. correlated) the results can differ from single input models.







                    share|cite|improve this answer












                    share|cite|improve this answer



                    share|cite|improve this answer










                    answered 8 hours ago









                    IrishStatIrishStat

                    21.4k42342




                    21.4k42342























                        1












                        $begingroup$

                        The tests in multiple regression are "added last" tests. That means they test whether the model significantly improves after including the extra variable in a regression that contains all other predictors.



                        In your model with no predictors, adding A improves the model, so the test of A is significant in the model with only A.



                        In a model with A already in the model, adding B improves the model, so the test of B is significant in the model with A and B. But in a model with B already in the model, adding A doesn't improve the model, so the test of A is not significant in the model with A and B. B is doing all the work that A would do, so adding A doesn't improve the model beyond B.



                        As @IrishStat mentioned, this can occur when A and B are correlated (positively or negatively) with each other. It's a fairly common occurrence in regression modeling. The conclusion you might draw is that A predicts the outcome when B is not in the model (i.e., unavailable), but after including B, A doesn't do much more to predict the outcome. Unfortunately, without more information about the causal structure of your variables, there is little more interpretation available.






                        share|cite|improve this answer









                        $endgroup$

















                          1












                          $begingroup$

                          The tests in multiple regression are "added last" tests. That means they test whether the model significantly improves after including the extra variable in a regression that contains all other predictors.



                          In your model with no predictors, adding A improves the model, so the test of A is significant in the model with only A.



                          In a model with A already in the model, adding B improves the model, so the test of B is significant in the model with A and B. But in a model with B already in the model, adding A doesn't improve the model, so the test of A is not significant in the model with A and B. B is doing all the work that A would do, so adding A doesn't improve the model beyond B.



                          As @IrishStat mentioned, this can occur when A and B are correlated (positively or negatively) with each other. It's a fairly common occurrence in regression modeling. The conclusion you might draw is that A predicts the outcome when B is not in the model (i.e., unavailable), but after including B, A doesn't do much more to predict the outcome. Unfortunately, without more information about the causal structure of your variables, there is little more interpretation available.






                          share|cite|improve this answer









                          $endgroup$















                            1












                            1








                            1





                            $begingroup$

                            The tests in multiple regression are "added last" tests. That means they test whether the model significantly improves after including the extra variable in a regression that contains all other predictors.



                            In your model with no predictors, adding A improves the model, so the test of A is significant in the model with only A.



                            In a model with A already in the model, adding B improves the model, so the test of B is significant in the model with A and B. But in a model with B already in the model, adding A doesn't improve the model, so the test of A is not significant in the model with A and B. B is doing all the work that A would do, so adding A doesn't improve the model beyond B.



                            As @IrishStat mentioned, this can occur when A and B are correlated (positively or negatively) with each other. It's a fairly common occurrence in regression modeling. The conclusion you might draw is that A predicts the outcome when B is not in the model (i.e., unavailable), but after including B, A doesn't do much more to predict the outcome. Unfortunately, without more information about the causal structure of your variables, there is little more interpretation available.






                            share|cite|improve this answer









                            $endgroup$



                            The tests in multiple regression are "added last" tests. That means they test whether the model significantly improves after including the extra variable in a regression that contains all other predictors.



                            In your model with no predictors, adding A improves the model, so the test of A is significant in the model with only A.



                            In a model with A already in the model, adding B improves the model, so the test of B is significant in the model with A and B. But in a model with B already in the model, adding A doesn't improve the model, so the test of A is not significant in the model with A and B. B is doing all the work that A would do, so adding A doesn't improve the model beyond B.



                            As @IrishStat mentioned, this can occur when A and B are correlated (positively or negatively) with each other. It's a fairly common occurrence in regression modeling. The conclusion you might draw is that A predicts the outcome when B is not in the model (i.e., unavailable), but after including B, A doesn't do much more to predict the outcome. Unfortunately, without more information about the causal structure of your variables, there is little more interpretation available.







                            share|cite|improve this answer












                            share|cite|improve this answer



                            share|cite|improve this answer










                            answered 7 hours ago









                            NoahNoah

                            3,6811417




                            3,6811417





















                                0












                                $begingroup$

                                To expand a little on @Noah and @IrishStat's answers, in a multiple regression, coefficients for each independent variable/predictor are estimated to obtain the direct effect of each variable, using variation unique to that variable and the variable's correlation with the outcome variable, not using variation shared by predictors. (In technical terms, we are talking about variance and covariance of these variables.) The less unique variation there is, the less significant the estimate will become.



                                So why, in your example, did you end up with an insignificant predictor A when B was added, and not with a significant predictor A and insignificant predictor B? It is likely because the proportion of variance of predictor A that it has in common with predictor B is larger than the proportion of variance of predictor B that it has in common with predictor A.






                                share|cite|improve this answer









                                $endgroup$

















                                  0












                                  $begingroup$

                                  To expand a little on @Noah and @IrishStat's answers, in a multiple regression, coefficients for each independent variable/predictor are estimated to obtain the direct effect of each variable, using variation unique to that variable and the variable's correlation with the outcome variable, not using variation shared by predictors. (In technical terms, we are talking about variance and covariance of these variables.) The less unique variation there is, the less significant the estimate will become.



                                  So why, in your example, did you end up with an insignificant predictor A when B was added, and not with a significant predictor A and insignificant predictor B? It is likely because the proportion of variance of predictor A that it has in common with predictor B is larger than the proportion of variance of predictor B that it has in common with predictor A.






                                  share|cite|improve this answer









                                  $endgroup$















                                    0












                                    0








                                    0





                                    $begingroup$

                                    To expand a little on @Noah and @IrishStat's answers, in a multiple regression, coefficients for each independent variable/predictor are estimated to obtain the direct effect of each variable, using variation unique to that variable and the variable's correlation with the outcome variable, not using variation shared by predictors. (In technical terms, we are talking about variance and covariance of these variables.) The less unique variation there is, the less significant the estimate will become.



                                    So why, in your example, did you end up with an insignificant predictor A when B was added, and not with a significant predictor A and insignificant predictor B? It is likely because the proportion of variance of predictor A that it has in common with predictor B is larger than the proportion of variance of predictor B that it has in common with predictor A.






                                    share|cite|improve this answer









                                    $endgroup$



                                    To expand a little on @Noah and @IrishStat's answers, in a multiple regression, coefficients for each independent variable/predictor are estimated to obtain the direct effect of each variable, using variation unique to that variable and the variable's correlation with the outcome variable, not using variation shared by predictors. (In technical terms, we are talking about variance and covariance of these variables.) The less unique variation there is, the less significant the estimate will become.



                                    So why, in your example, did you end up with an insignificant predictor A when B was added, and not with a significant predictor A and insignificant predictor B? It is likely because the proportion of variance of predictor A that it has in common with predictor B is larger than the proportion of variance of predictor B that it has in common with predictor A.







                                    share|cite|improve this answer












                                    share|cite|improve this answer



                                    share|cite|improve this answer










                                    answered 28 mins ago









                                    AlexKAlexK

                                    1908




                                    1908




















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