I performed a polynomial regression using the following formula:
lm(deviance ~ poly(myDF$distance,3,raw=T))
However, the summary output states that only the third term is significant:
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.014825 0.095987 -0.154 0.8774 poly(myDF$distance, 3, raw = T)1 0.031286 0.143283 0.218 0.8273 poly(myDF$distance, 3, raw = T)2 -0.080363 0.065591 -1.225 0.2215 poly(myDF$distance, 3, raw = T)3 0.021517 0.009377 2.295 0.0224 *
How is this to be interpreted? My first guess is that only the full third-degree model (including the lower degree terms) fits the data significantly better than the null hypothesis. Is this correct? Put simply: Does the non-significance of the first and second degree terms impair the goodness of the model?