Changes

[[File:Figure 13.png|none|thumb|716x716px|Figure 13: Running the Simulation]]

[[File:Figure 13.png|none|thumb|716x716px|Figure 13: Running the Simulation]]

= Analysing the Results =

=== Analysing the Results ===

Depending on the data that you have decided to report in the Outputs sheet there will be several ways to analyse results from the Monte Carlo experiments that you perform.  Several equations for statistics on data series generated are automatically entered when the simulation is run, and two additional functions for information on data ranges exist within RRAT.   However, by using the built-in functionality of excel itself you will be able to produce a much wider array of useful information, tables, and figures that will provide insight to your CBA.   

Depending on the data that you have decided to report in the Outputs sheet there will be several ways to analyse results from the Monte Carlo experiments that you perform.  Several equations for statistics on data series generated are automatically entered when the simulation is run, and two additional functions for information on data ranges exist within RRAT.   However, by using the built-in functionality of excel itself you will be able to produce a much wider array of useful information, tables, and figures that will provide insight to your CBA.   

=== Automatically Generated Statistics ===

==== Automatically Generated Statistics ====

For each data series that RRAT produces in the Outputs sheet formulas will be entered to calculate the maximum outcome, the minimum outcome, the mean (average) outcome, the median outcome (the outcome in the middle), the mode (the outcome that occurs the most), and the standard deviation.  These are all calculated using common excel formulas and are done automatically to save the analyst time in entering these standard statistics of interest.   The area where these results are reported can be seen in Figure 12 in rows 12-17. Note that the mode, the result that occurs the most often, will often result in an error result when dealing with continuous distributions as any one particular result is unlikely to be exactly recreated. If you wish to see a mode result, you can try rounding the result for the objective output.

For each data series that RRAT produces in the Outputs sheet formulas will be entered to calculate the maximum outcome, the minimum outcome, the mean (average) outcome, the median outcome (the outcome in the middle), the mode (the outcome that occurs the most), and the standard deviation.  These are all calculated using common excel formulas and are done automatically to save the analyst time in entering these standard statistics of interest.   The area where these results are reported can be seen in Figure 12 in rows 12-17. Note that the mode, the result that occurs the most often, will often result in an error result when dealing with continuous distributions as any one particular result is unlikely to be exactly recreated. If you wish to see a mode result, you can try rounding the result for the objective output.

=== Ranges and Probabilities ===

==== Ranges and Probabilities ====

The last option available within RRAT is associated with the ‘CLEAR ENTRIES BUTTON’. As expected, pressing this button will clear all user entries (including named ranges) and data created from a simulation from the spreadsheet.

The last option available within RRAT is associated with the ‘CLEAR ENTRIES BUTTON’. As expected, pressing this button will clear all user entries (including named ranges) and data created from a simulation from the spreadsheet.

=== Sensitivity Analysis - Exploring Relations ===

==== Sensitivity Analysis - Exploring Relations ====

One of the main purposes of sensitivity analysis is to determine how variability in one assumption impacts the objective function. In terms of regulatory CBA the objective function is usually NPV.  With a Monte-Carlo simulation a huge data set is produced that can be used to perform such sensitivity analysis.  A good way to do this is by producing a scatter chart of the objective function versus the variable or assumption of concern.   

One of the main purposes of sensitivity analysis is to determine how variability in one assumption impacts the objective function. In terms of regulatory CBA the objective function is usually NPV.  With a Monte-Carlo simulation a huge data set is produced that can be used to perform such sensitivity analysis.  A good way to do this is by producing a scatter chart of the objective function versus the variable or assumption of concern.   

Thus, from one scatter-plot we have discovered a number of very important outcomes.  In a complete analysis it would require very little effort to repeat this process for other assumptions in a similar manner. Furthermore, the example used in this guide is relatively simplistic by design but the methods of analysis of outcomes using Monte-Carlo experiments will be similar no matter what the complexity of the model. 

Thus, from one scatter-plot we have discovered a number of very important outcomes.  In a complete analysis it would require very little effort to repeat this process for other assumptions in a similar manner. Furthermore, the example used in this guide is relatively simplistic by design but the methods of analysis of outcomes using Monte-Carlo experiments will be similar no matter what the complexity of the model. 

== Conclusion & Next Steps ==

=== Conclusion & Next Steps ===

I hope that this instruction manual has given enough information for analysts familiar with cost benefit analysis and excel to be able to use the RRAT.  While the tool itself is relatively simplistic as compared to other commercially available statistical analysis packages I believe this manual has demonstrated that combining its functions with the tools that are already available within Excel can provide some very powerful results and analysis to strengthen CBA on a regulatory proposal.

I hope that this instruction manual has given enough information for analysts familiar with cost benefit analysis and excel to be able to use the RRAT.  While the tool itself is relatively simplistic as compared to other commercially available statistical analysis packages I believe this manual has demonstrated that combining its functions with the tools that are already available within Excel can provide some very powerful results and analysis to strengthen CBA on a regulatory proposal.