Interpretation of Coefficients in 'fitlm' regression

Hello,

I require help with regards to the interpretation of 'fitlm' results.

My data has 8 features, and when each feature is plotted against the response variable there are some obvious relationships (see figure below).

From looking at this plot I would expect features x4, x5, x6, and x7 to all have negative coefficients, and probably x8 to have a positive coefficient.

When I execute the following code:

 mdl = fitlm(features,y,'linear','RobustOpts','on')

I get the following output:

                   Estimate        SE        tStat        pValue
(Intercept)         3.1936    0.038772      82.368    3.9862e-95
    x1             -0.021465    0.040015    -0.53643       0.59283
    x2              0.012444    0.018055     0.68919       0.49227
    x3              0.014156    0.031247     0.45305       0.65148
    x4               0.25286     0.09546      2.6488     0.0093614
    x5                 2.378      1.2857      1.8496      0.067274
    x6               -2.0413     0.25464     -8.0164     1.882e-12
    x7               -1.7374      1.0649     -1.6314       0.10588
    x8              -0.17522    0.031894      -5.494    2.9021e-07

which seems a bit counter-intuitive, since the coefficients of some of the features (i.e. the gradients) are positive when I would expect them to be negative.

Also, the model reports a low RMSE and a very high R-squared of 0.969, which suggests a very good fit. The pValues are quite low as well, which suggests that the features with the unusual gradients are statistically significant.

I'm wondering why this is the case and how can I interpret these results?

Thanks, Paul.