More basic instructions

docs/basics.rst

@@ -6,11 +6,11 @@ What is Sensitivity Analysis?
 -----------------------------
 
 According to `Wikipedia <https://en.wikipedia.org/wiki/Sensitivity_analysis>`_,
-sensitivity analysis is "Sensitivity analysis is the study of how the
-uncertainty in the output of a mathematical model or system (numerical or
-otherwise) can be apportioned to different sources of uncertainty in its
-inputs."  The sensitivity of each input is often represented by a numeric value,
-called the *sensitivity index*.  Sensitivity indices come in several forms:
+sensitivity analysis is "the study of how the uncertainty in the output of a 
+mathematical model or system (numerical or otherwise) can be apportioned to 
+different sources of uncertainty in its inputs."  The sensitivity of each input 
+is often represented by a numeric value, called the *sensitivity index*.  
+Sensitivity indices come in several forms:
 
 1. First-order indices: measures the contribution to the output variance by a single
    model input alone.
@@ -23,9 +23,20 @@ called the *sensitivity index*.  Sensitivity indices come in several forms:
    and all higher-order interactions.
 
 SALib is an open source library written in Python for performing
-sensitivity analysis.  A number of different sensitivity analysis methods are
-available
+sensitivity analysis.  A typical sensitivity analysis using SALib follows
+four steps:
+
+1. Determine the model inputs (parameters) and their sample range.  
+
+2. Run the :code:`sample` function to generate the model inputs.
+
+3. Evaluate the model using the generated inputs, saving the model outputs.
+
+4. Run the :code:`analyze` function on the outputs to compute the sensitivity indices.
 
 
 
 SALib is broken into two parts: the *sample generator* and the *sensitivity analysis*.
+
+.. autofunction:: fast.analyze
+    :members: