Regression result shows small $R^2$ fit and large p value? I designed a model for my dissertation which has 7 predictors based on existing theories. After data selection, I got a sample size of 126 and I thought I was okay since the data is cross sectional. After running the regression I obtained the following result:

As you can see (i) the $R^2$ is -3.3% and p value for F test is .86 so I'm worried about the sample size being too small. (ii) all the coefficient has very large p value.
I can't find more samples as the available data is very limited and I don't have enough time to resign the dissertation. How should I interpret this result? Will conducting a failed experiment lead to a problem with the dissertation?
 A: It seems to me you can make a fairly strong conclusion: The outcome you are measuring is not linearly related to the eight independent variables you selected.  You can never prove a negative, but there is no indication here that the model fits your data at all. That might be useful information.
A: If you have anomalies in your data it can create a downwards bias in your significance tests. Additionally if you have series that are highly cross-correlated this also can have an impact on your results. If you wish you can post your data and perhaps some further inspection can be accomplished.
A: Very small $R^2$ and very high $p$-values for the parameters suggest that your model is poor. But, for a moment, don't look at those values, look at the standard errors and think of your parameters in terms of confidence intervals. You would calculate the 95% confidence intervals, by taking, approximately, the parameter value $\pm$ two times the errors. Look at your errors, those intervals would be huge as compared to the point estimates of the parameters, so your parameters can be literally anything and cannot be trusted. (In fact, the $p$-values for the parameters are calculated by looking at size of the parameters as compared to the magnitude of errors.)
So you cannot make any conclusions based on this data and this model. However this doesn't mean that other model wouldn't be appropriate, but that's a different story.
A: Well, that didn't work. Try eliminating parameters with the least contributory p(CB)=0.951 the first to go. Eventually, at least the constant should become significant, which would at least demonstrate invariant data. The other thing to try would be to look at the distribution of the $y$-values. It is entirely possible that they are not nicely distributed. In case they aren't nice, a transformation of that data by something, maybe logs, reciprocals, squares, square roots, exponentiations may produce much better regressions. That is a long list of trials because sometimes the logs or other transforms of the independent parameters is needed as well, and, for best fitting, there may be a mixture of different transforms for $y$ and $x$'s that produce best results. What that means is that if there is a functional relationship, $f(x\text{'s})$ may take any form, e.g., $f(x \text{'s})=\text{sech}(x_1^2)\space e^{x_2^{-({x_3/2})^3 }}\dots$  Although finding a functional relationship is an art form, thinking about what is happening physically can eliminate lots of elbow grease. For example, if the data is proportional type, take logs. If the data has very long right tails, try reciprocation, etc.
