# How to interpret residual plots from time series regression

I am doing a time series regression between 2 variables. I used the dynlm library in R. I'm trying to understand how to interpret the results.

Could you please point out where I am getting it wrong:

1) The R squared seems very low -- this indicates a weak linear relationship. Or too many outliers. 2) Normal QQ plot shows that there are a significant number of outliers -- at a later date in the time series? 3) Does the 'Residuals vs Fitted' plot show that the model is a pretty good fit for most of the data, except for those outliers?

Any suggested readings (esp open-sourced materials available online) also appreciated.

Call:
dynlm(formula = P ~ L(B))
Residuals:
Min      1Q  Median      3Q     Max
-63.711 -27.687 -14.907   2.364 146.157
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 17.485051  13.500833   1.295    0.197
L(B)         0.019422   0.002384   8.146 5.84e-14
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 48.17 on 182 degrees of freedom
Multiple R-squared:  0.2672,    Adjusted R-squared:  0.2632
F-statistic: 66.36 on 1 and 182 DF,  p-value: 5.838e-14