# Two-Way Anova in F# QuickStart Sample

Illustrates how to use the TwoWayAnovaModel class to perform a two-way analysis of variance in F#.

View this sample in: C# Visual Basic IronPython

```
// Illustrates the use of the TwoWayAnovaModel class for performing
// a two-way analysis of variance.
#light
open System
open Extreme.DataAnalysis
open Extreme.Statistics
// The license is verified at runtime. We're using a demo license here.
// For more information, see:
// https://numerics.net/trial-key
let licensed = Extreme.License.Verify("Demo license")
// This example investigates the effect of the color and shape
// of packages on the sales of the product. The data comes from
// 12 stores. Packages can be either red, green or blue in color.
// The shape can be either square or rectangular.
// Set up the data using records.
type Observation = { Store : int; Color : string; Shape : string; Sales : float }
let dataFrame =
let data =
[|
{ Store = 1; Color = "Blue"; Shape = "Square"; Sales = 6.0 };
{ Store = 2; Color = "Blue"; Shape = "Square"; Sales = 14.0 };
{ Store = 3; Color = "Blue"; Shape = "Rectangle"; Sales = 19.0 };
{ Store = 4; Color = "Blue"; Shape = "Rectangle"; Sales = 17.0 };
{ Store = 5; Color = "Red"; Shape = "Square"; Sales = 18.0 };
{ Store = 6; Color = "Red"; Shape = "Square"; Sales = 11.0 };
{ Store = 7; Color = "Red"; Shape = "Rectangle"; Sales = 20.0 };
{ Store = 8; Color = "Red"; Shape = "Rectangle"; Sales = 23.0 };
{ Store = 9; Color = "Green"; Shape = "Square"; Sales = 7.0 };
{ Store = 10; Color = "Green"; Shape = "Square"; Sales = 11.0 };
{ Store = 11; Color = "Green"; Shape = "Rectangle"; Sales = 18.0 };
{ Store = 12; Color = "Green"; Shape = "Rectangle"; Sales = 10.0 };
|]
DataFrame.FromObjects(data)
// Construct the OneWayAnova object.
let anova = TwoWayAnovaModel(dataFrame, "Sales", "Color", "Shape")
// Alternatively, you can use a formula to specify the variables:
let anova2 = TwoWayAnovaModel(dataFrame, "Sales ~ Color + Shape")
// Perform the calculation.
anova.Fit()
// Verify that the design is balanced:
if (not anova.IsBalanced) then
printfn "The design is not balanced."
// The AnovaTable property gives us a classic anova table.
// We can write the table directly to the console:
printfn "%O" anova.AnovaTable
printfn ""
// A Cell object represents the data in a cell of the model,
// i.e. the data related to one combination of levels of each factor.
// We can use it to access the group means of our color groups.
// First we get the index so we can easily iterate
// through the levels:
let colorFactor = anova.GetFactor<string>(0)
for level in colorFactor do
printfn "Mean for square boxes group '%O': %.4f"
level (anova.Cells.Get(level, "Square").Mean)
// We could have accessed the cells directly as well:
printfn "Variance for red, rectangular packages: %A"
(anova.Cells.Get("Red", "Rectangle").Variance)
printfn ""
// The RowTotals and ColumnTotals properties permits us to
// summarize the data over all levels of a factor. For example,
// to get the means of the shape groups, we use:
let shapeFactor = anova.GetFactor<string>(1)
for level in shapeFactor do
printfn "Mean for group '%O': %.4f" level (anova.ColumnTotals.Get(level).Mean)
printfn ""
// We can get the summary data for the entire model
// by using the TotalCell property.
let totalSummary = anova.TotalCell
printfn "Summary data:"
printfn "# observations: %.0f" totalSummary.Count
printfn "Grand mean: %.4f" totalSummary.Mean
printf "Press any key to exit."
Console.ReadLine() |> ignore
```