# 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

## Answer

**Answers can be viewed only if**

- The questioner was satisfied and accepted the answer, or
- The answer was disputed, but the judge evaluated it as 100% correct.

- answered
- 214 views
- $4.00

### Related Questions

- Mathematical modeling
- Two statistics proofs with regressions, any help much appreciated!
- Help structure linear mixed effects model random effects structure
- Use the Desmos graphing calculator to find slope and y-intercept for the least-squares regression line for the dataset in the table
- How can I calculate incremental growth rate using a logarithm regression analysis? I have count data from 1992-2022 of a species and want to calculate the growth rate on a 3 year moving average.