Plots in R vs Summary of model So I'm using R to understand my dataset. I'm getting slightly confused since I have got an Adjusted R square of around 3% (which is clearly super low- so guessing my model isn't good). But then when i plot the model, I get the below two pictures which to me seem a good fit? The red line in the residuals plot shows it's close enough to 0- which indicates good? and The Q graph shows a normal distribution fit?


thanks for all your help in advance!
 A: Yes, the shape of your residual plot is good - nicely balanced around zero. However, look at the scales on the axes. Most of your fitted values range from 0 to 250. The residuals are mostly in the range from -1100 to +1100, i.e. the errors are about four times as big as the values that you are predicting. R-squared = 3% seems reasonable with such big errors. 
Also, to address your other question from the comments, the red line is a loess curve fitted to the residuals.  In your case, the curve is close to y=0 which says that on average, your results are good. But again, having one error of +500 and another of -500 may average to zero error, but does not indicate a good result. 
A: You're conflating two ways that a regression can be "good".
Low $R^2$ means that there's low correlation. That is, that the trendline doesn't provide much better prediction than just the sample average for the outcome.
Reasonably calibrated QQ plot and lack of trend in the plot of predicted vs residuals just means that the data are kind of normal.
