# Interpretation of signifcance of continous by continous regression with interaction term

Hello,

I am running a regression with a continous DP (scale of self-assesement of a person´s willingness to accept pay cuts to protect the climate) and two continous IVs (1=age 2=value-index) and their interaction.

My hypothesis is that age has a negative effect on a person´s willingness to accept said paycut and want to control it with IV2 for a person´s political values. In short: When holding values constant, does age still have the negative effect?

In Model 1, the Interaction and IV 2 are significant, while the IV 1 is not. In Model 2 only IV 2 is signficant. I have created marginplots but need some input in regard of the interpretation of the signficance in both Models.

1. Can I say that IV 1 has no significant effect on the DP in Model 1 and 2 and therefore I cannot accept my hypothesis?

2. How do I interpret an non-siginifacnt Interaction Term?

3. Is it still okay to show the plots of average marginal effects and predicitve margins for illustrative purposes and add a "non-significant but still relevant - mark"?

Here for more visual explanation

Model 1 | Model 2 | ||

Age | -0.4 | -0.6 | |

Value-Index | 1.1*** | 1.2*** | |

Age#Value-Index | -0.01** | -0.002 |

Thank you very much for any input

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