Added a page on analysis mode.

SUMMARY.md

@@ -8,6 +8,7 @@
 * [Modeling](basic_modeling/README.md)
   * [Input Types](basic_modeling/input_types.md)
   * [Interpreting Your Results](basic_modeling/interpreting_your_results.md)
+  * [Analysis Mode](basic_modeling/analysis_mode.md)
   * [SIPS and SLURPS](basic_modeling/sips_and_slurps.md)
 * [Function Reference](functions/README.md)
   * [Operators](functions/operators.md)

basic_modeling/analysis_mode.md

@@ -0,0 +1,33 @@
+## Analysis Mode
+A complicated model can give you powerful insight about its output, but how can you best refine those predictions?
+Lowering the uncertainties on your inputs will help lower the uncertainty on your outputs, but collecting additional
+data and refining your input estimates is expensive, and prioritizing which inputs to refine first is very important. To
+help with this process, Guesstimate offers _Analysis Mode_, which helps you analyze how model inputs affect model
+outputs.
+
+To enter analysis mode, simply open the metric sidebar, by clicking the 'gear' icon in the upper right corner of the
+metric card, then click 'Sensitivty'. Once you do this, all related metrics in the model will display visual sensitivity
+analysis plots, comparing their samples to the selected metric's samples. Each plot is a scatter plot, comparing the
+samples of the corresponding metric on the $$x$$ axis to the originally selected metric on the $$y$$ axis.
+
+For example, consider this model, which analyzes how many piano tuners there might be in Boston:
+
+![Piano Tuners in Boston](../assets/Piano Tuners in Boston.png)
+
+We can see that the output estimate here has quite a bit of uncertainty, ranging from 190 to 2,400! If we want to
+understand which of the many input variables are yielding the most output uncertainty, we simply enter analysis mode.
+
+![Piano Tuners in Boston with Sensitivty Analysis](../assets/Piano Tuners in Boston Sensitivity Section.png)
+
+To determine which inputs are causing the most output uncertianty, we can examine the scatter plots, looking for the
+highest correlation between any of the input nodes and our output. We look for the highest correlation because a higher
+correlation means that small deviations in the input affect the output more than nodes with a lower correlation. As the
+input's deviation is goverened by its uncertainty, this indicates which uncertainty is affecting the output most. In
+this case, the input node 'Proportion of Singles with Pianos' has the highest correlation and thus affecting the output
+the most. By hovering over the small scatter plot, we can also see an expanded version which offers more details about
+the relationship between the two nodes.
+
+![Expanded Sensitivty View](../assets/Sensitivity Expanded.png)
+
+Given this information, we can be confident that if we refine our estimate of the proportion of singles with pianos,
+then our output estimate for the number of piano tuners in boston will also lose uncertainty.