Residuals analysis: interpretation of a scatter plot I have problems with the interpretation of a scatter plot in a multiple linear regression (OLS method). I have posted an image below of the scatter plot of the standardized residuals vs the predicted value of my dependent variable (C). 
My question is: in this graph can I assume that my linear regression model is good? The disposition of the residuals is suspect and this often is trace of nonlinear relationship between the variables. What do you think of my case? Thanks to everyone.
EDIT: here is the dataset
https://mega.nz/#!ehtEERJA!_3OMnu2GutFmM9R9fZjfQIthF7bzCNMaT_g1Q2033ko
C is the dependent variable, the other variables are the indipendent.
Durbin Watson stat: 1,241603582
Shapiro-Wilk test shows that residuals are normally distributed
EDIT 2: here is the qq plot for residuals

 A: No, this does not look good. You appear to have a problem with heteroscedasticity as there is increasing variance of residuals with increasing predicted values. Constant variance is an important condition for OLS regression in order to perform valid inference. This might be resolved by log-transforming the response variable.
There is also a hint of autocorrelation but this is hard to assess with so few data points.
Edit, after downloading the data:
Log-transforming C helps with heteroscedasticity, though there are few data points so I would advise some caution: while it seems to help with these data, it may not be the case with more observations. There could be other non-linearities that should be accounted for.

However, all your independent variables are highly correlated with each other, which is not good at all for model interpretation:
      years    Y    W  SSW    G    T   TR    D
years  1.00 0.95 0.96 0.96 0.98 0.98 1.00 0.98
Y      0.95 1.00 0.99 0.95 0.97 0.98 0.95 0.87
W      0.96 0.99 1.00 0.97 0.98 0.98 0.96 0.89
SSW    0.96 0.95 0.97 1.00 0.98 0.97 0.97 0.93
G      0.98 0.97 0.98 0.98 1.00 0.99 0.99 0.95
T      0.98 0.98 0.98 0.97 0.99 1.00 0.98 0.93
TR     1.00 0.95 0.96 0.97 0.99 0.98 1.00 0.98
D      0.98 0.87 0.89 0.93 0.95 0.93 0.98 1.00

A: Visually at first it seemed to me that your residuals look like they were heteroskedastic (non-constant variance), autocorrelated (not independent), and non-Normally distributed.  Those are actually issues that could be resolved anyway. It turns out that you tested your residuals, and they appear to have done ok on all those counts. However, those issues are minute vs. the multicollinearity issue uncovered by Long.  All your independent variables are very highly correlated with positive correlation coefficients ranging between 0.9 and 1.0.  The multicollinearity is a huge problem.  You indicated not being very concerned about it.  But, you should.  This problem does not go away just by wishful thinking.  And, logging the variables will certainly not solve that.  It may actually exacerbate it.  A symptom of multicollinearity is that your variables' regression coefficients are likely very unstable.  Rerun your regression by omitting some of the data to test the stability of the coefficients.  They are likely to be very unstable.  Another symptom is that the statistical significance of some of your variables may be questionable.  It does not make sense to have 8 independent variables in a model that are all very highly correlated.  They simply impart to your model almost the exact same type of info in terms of explaining the variance (or behavior) of your dependent variable.  
I think you need to rebuild this model by starting by selecting one single of the best time-oriented independent variable you have.  And, then adding other variables that provide information other than time.  One type of time-oriented variable you may add to this model without running into excessive multicollinearity issues are seasonality dummy variable.  But, this is not a sure thing.  You would have to test for that (the multicollinearity bit).        
