In this example, the same model and data is fit as in the example 1, but additional information is printed.
using System;
using Imsl.Stat;
public class ANOVAFactorialEx2
{
public static void Main(String[] args)
{
int nSubscripts = 3, i;
int[] nLevels = new int[]{3, 2, 10};
double[] y = new double[]{ 73.0, 102.0, 118.0,
104.0, 81.0, 107.0,
100.0, 87.0, 117.0,
111.0, 90.0, 76.0,
90.0, 64.0, 86.0,
51.0, 72.0, 90.0,
95.0, 78.0, 98.0,
74.0, 56.0, 111.0,
95.0, 88.0, 82.0,
77.0, 86.0, 92.0,
107.0, 95.0, 97.0,
80.0, 98.0, 74.0,
74.0, 67.0, 89.0,
58.0, 94.0, 79.0,
96.0, 98.0, 102.0,
102.0, 108.0, 91.0,
120.0, 105.0, 49.0,
82.0, 73.0, 86.0,
81.0, 97.0, 106.0,
70.0, 61.0, 82.0};
String[] labels =
new String[]{"degrees of freedom for the model" +
" ", "degrees of freedom for error" +
" ",
"total (corrected) degrees of freedom ",
"sum of squares for the model ",
"sum of squares for error ",
"total (corrected) sum of squares ",
"model mean square ",
"error mean square ",
"F-statistic ",
"p-value ",
"R-squared (in percent) ",
"Adjusted R-squared (in percent) ",
"est. standard deviation of the model error ",
"overall mean of y ",
"coefficient of variation (in percent) "};
String[] rlabels = new String[]{"A", "B", "A*B"};
String[] mlabels = new String[]{"grand mean ",
"A1 ", "A2 ", "A3 ",
"B1 ", "B2 ", "A1*B1 ",
"A1*B2 ", "A2*B1 ", "A2*B2 ",
"A3*B1 ", "A3*B2 "};
ANOVAFactorial af =
new ANOVAFactorial(nSubscripts, nLevels, y);
Console.Out.WriteLine
("P-value = " + af.Compute().ToString("0.000000"));
Console.Out.WriteLine
("\n * * * Analysis of Variance * * *");
double[] anova = af.GetANOVATable();
for (i = 0; i < anova.Length; i++)
{
Console.Out.WriteLine
(labels[i] + " " + anova[i].ToString("0.0000"));
}
Console.Out.WriteLine
("\n * * * Variation Due to the " + "Model * * *");
Console.Out.WriteLine
("Source\tDF\tSum of Squares\tMean Square" +
"\tProb. of Larger F");
double[,] te = af.GetTestEffects();
for (i = 0; i < te.GetLength(0); i++)
{
Console.Out.WriteLine(
rlabels[i] + "\t" +
te[i,0].ToString("0.0000") + "\t" +
te[i,1].ToString("0.0000") + "\t" +
te[i,2].ToString("0.0000") + "\t\t" +
te[i,3].ToString("0.0000"));
}
Console.Out.WriteLine("\n* * * Subgroup Means * * *");
double[] means = af.GetMeans();
for (i = 0; i < means.Length; i++)
{
Console.Out.WriteLine
(mlabels[i] + " " + means[i].ToString("0.0000"));
}
}
}
P-value = 0.002299
* * * Analysis of Variance * * *
degrees of freedom for the model 5.0000
degrees of freedom for error 54.0000
total (corrected) degrees of freedom 59.0000
sum of squares for the model 4612.9333
sum of squares for error 11586.0000
total (corrected) sum of squares 16198.9333
model mean square 922.5867
error mean square 214.5556
F-statistic 4.3000
p-value 0.0023
R-squared (in percent) 28.4768
Adjusted R-squared (in percent) 21.8543
est. standard deviation of the model error 14.6477
overall mean of y 87.8667
coefficient of variation (in percent) 16.6704
* * * Variation Due to the Model * * *
Source DF Sum of Squares Mean Square Prob. of Larger F
A 2.0000 266.5333 0.6211 0.5411
B 1.0000 3168.2667 14.7666 0.0003
A*B 2.0000 1178.1333 2.7455 0.0732
* * * Subgroup Means * * *
grand mean 87.8667
A1 89.6000
A2 84.9000
A3 89.1000
B1 95.1333
B2 80.6000
A1*B1 100.0000
A1*B2 79.2000
A2*B1 85.9000
A2*B2 83.9000
A3*B1 99.5000
A3*B2 78.7000
Link to C# source.