Linear Regression Model (Residual vs fitted and Normal QQ-plot) I would like to ask according to these graphs, does it appear as if a simple linear regression model is
appropriate for these data?


 A: Your diagnostic plots look fine to me.  There is no indication of non-linearity, heteroscedasticity, or substantial deviation from normality.  If these are time-series data (of if they have some other meaningful ordering) you should also add an auto-correlation plot; otherwise you don't need it.  You might also consider adding a kernel density plot with a dotted line showing the theoretical residual distribution (T-distribution with appropriate degrees-of-freedom).
It would be better if you put the studentised residuals on your plots, rather than using the raw residuals.  This is necessary because the variance of the residuals is not constant (though it is usually close), and studentisation adjusts them to constant scale.  Using studentised residuals allows the reader to get a better sense of their size in standardised terms, which makes it easier to scrutinise the diagnostic plots.  It also means that the quantiles in the QQ-plot will be on the same scale.  (Ideally, the QQ-plot should actually be against the theoretical distribution of the residuals, which is a T-distribution with $df_{Res}$ degrees-of-freedom, not a normal distribution.)  You should also add a dashed line to your QQ-plot showing perfect fit to the normal distribution. 
