R 如何处理拟合值过多的二次模型？

我试图将二次回归模型拟合到数据集，然后在散点图上绘制曲线。该数据集是关于电视节目中角色的集数和屏幕时间

我画了一个散点图，在x轴上有情节，在y轴上有放映时间，效果很好

然后，我创建模型，如下所示：

#ordering
gottemp <- got[order(got$episodes),]

#plotting
plot(screentime~episodes, data = gottemp, xlab ="Number of episodes", ylab = "Screentime (minutes)", col=c("blue","red")[gender], pch=c(1,2)[gender])
legend("topleft",pch = c(1,2),col=c("blue","red"),c("female","male"))
title("Plot of Screentimes vs Number of Episodes")

#creating 3model and plotting line
model <- lm(screentime~episodes+I(episodes^2), data = got)
lines(fitted(model))

#订购
gottemp类似于

nd <- data.frame(episodes=seq(min(episodes), max(episodes), length=51)
nd$screentime <- predict(model, newdata=nd)
with(nd, lines(episodes, screentime))

nd说明正确解决的问题。编辑是否有帮助？