One-Way Anova in IronPython QuickStart Sample

Illustrates how to use the OneWayAnovaModel class to perform a one-way analysis of variance in IronPython.
```Python
import numerics

from System import Array

from Extreme.Statistics import *

# Illustrates the use of the OneWayAnovaModel class for performing 
# a one-way analysis of variance.

# This QuickStart Sample investigates the effect of the color of packages
# on the sales of the product. The data comes from 12 stores.
# Packages can be either red, green or blue.

# Set up the data in an ADO.NET data table.
import clr
clr.AddReference('System.Data')
from System.Data import DataTable

dataTable = DataTable()
dataTable.Columns.Add("Store", int)
dataTable.Columns.Add("Color", str)
dataTable.Columns.Add("Shape", str)
dataTable.Columns.Add("Sales", float)

dataTable.Rows.Add(Array[object]([1, "Blue", "Square", 6]))
dataTable.Rows.Add(Array[object]([2, "Blue", "Square", 14]))
dataTable.Rows.Add(Array[object]([3, "Blue", "Rectangle", 19]))
dataTable.Rows.Add(Array[object]([4, "Blue", "Rectangle", 17]))

dataTable.Rows.Add(Array[object]([5, "Red", "Square", 18]))
dataTable.Rows.Add(Array[object]([6, "Red", "Square", 11]))
dataTable.Rows.Add(Array[object]([7, "Red", "Rectangle", 20]))
dataTable.Rows.Add(Array[object]([8, "Red", "Rectangle", 23]))

dataTable.Rows.Add(Array[object]([9, "Green", "Square", 7]))
dataTable.Rows.Add(Array[object]([10, "Green", "Square", 11]))
dataTable.Rows.Add(Array[object]([11, "Green", "Rectangle", 18]))
dataTable.Rows.Add(Array[object]([12, "Green", "Rectangle", 10]))

# Construct the OneWayAnovaModel object.
anova =  OneWayAnovaModel(dataTable, "Sales", "Color")
# Verify that the design is balanced:
if not anova.IsBalanced:
	print "The design is not balanced."
# Perform the calculation.
anova.Compute()
			
# The AnovaTable property gives us a classic anova table.
# We can write the table directly to the console:
print anova.AnovaTable.ToString()
print 
			
# A Cell object represents the data in a cell of the model, # i.e. the data related to one level of the factor.
# We can use it to access the group means of our color groups.

# First we get the CategoricalScale object so we can easily iterate
# through the levels:
colorFactor = anova.GetFactor(0)
for level in colorFactor.GetLevels():
	print "Mean for group '{0}': {1:.4f}".format(level, anova.Cells[level].Mean)
			
# We could have accessed the cells directly as well:
print "Variance for blue packages:", anova.Cells["Blue"].Variance
print 
		
# We can get the summary data for the entire model
# by using the special index 'Cell.All':
totalSummary = anova.Cells[Cell.All]
print "Summary data:"
print "# observations:", totalSummary.Count
print "Grand mean:    ", totalSummary.Mean

```