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Fix for the internal dispersion model links.
* Temporary test of making a confidence interval as described in fitting guide. This is system test Relax_disp.x_test_task_7882_kex_conf, which is not activated by default. Running the test, interestingely shows, there is a possibility for a lower global k<sub>ex</sub>. But the value only differ from k<sub>ex</sub>=1826 to k<sub>ex</sub>=1813. [https://gna.org/task/?7882 Task #7882: Implement Monte-Carlo simulation whereby errors are generated with width of standard deviation or residuals].
* Change to system test Relax_disp.x_test_task_7882_kex_conf(). This is just a temporary system test, to check for local minima. This is method in regression book of Graphpad: http://www.graphpad.com/faq/file/Prism4RegressionBook.pdf Page: 109-111. [https://gna.org/task/?7882 Task #7882: Implement Monte-Carlo simulation whereby errors are generated with width of standard deviation or residuals].
* Raising an error, if the [http://www.nmr-relax.com/manual/The_R2eff_model.html [R2eff ]] model] is used, and drawing errors from the fit. [https://gna.org/task/?7882 Task #7882: Implement Monte-Carlo simulation whereby errors are generated with width of standard deviation or residuals].* To system test Relax_disp.test_task_7882_monte_carlo_std_residual(), adding test for raise of errors, if the [http://www.nmr-relax.com/manual/The_R2eff_model.html [R2eff ]] model] is selected. [https://gna.org/task/?7882 Task #7882: Implement Monte-Carlo simulation whereby errors are generated with width of standard deviation or residuals].
* Added test of argument "distribution" in pipe_control.error_analysis.monte_carlo_create_data(). This is to make sure that a wrong argument is not passed into the function. [https://gna.org/task/?7882 Task #7882: Implement Monte-Carlo simulation whereby errors are generated with width of standard deviation or residuals].
* Extended the [http://www.nmr-relax.com/manual/monte_carlo_create_data.html monte_carlo.create_data user function], to allow for the definition of the STD to use in Gauss distribution. This is for creation of Monte-Carlo simulations, where one has perhaps gained information about the expected errors of the data points, which is not measured. [https://gna.org/task/?7882 Task #7882: Implement Monte-Carlo simulation whereby errors are generated with width of standard deviation or residuals].
