anchor.diff:    -   ...
   
   Note that there is no information about the direction of variance difference.
  -In this implemented test the larger variance is always the numerator, therefor the comparison to F<sub>df1,df2,1-(alpha/2)</sub> is used for a two sided test. 
  +In this implemented test the larger variance is always the numerator, therefore the comparison to F<sub>df1,df2,1-(alpha/2)</sub> is used for a two sided test. 
   
   _Important note: The effectiveness of a preliminary test of variances is discussed. 
  -The F-test is extremely sensitive to normality-violations, and even if the samples follow a normal distribution, it does not detect many situations where a t-test should be avoided. 
  +The F-test is extremely sensitive to normality-violations, and even if the samples follow a normal distribution, it often does not detect situations where a t-test should be avoided. 
   Conditions for the Effectiveness of a Preliminary Test of Variance , C. and E. Markowski, The American Statistician, Vol 44. No. 4, 11/1990_
   
  -
  +References:
  +
  +  - Jürgen Bortz & Christof Schuster, Statistik für Human- und Sozialwissenschaftler (2010) Chapter 8.6
  +
  +  - Ronald Ley, F curves have two tails but the F test is a one-tailed two-tailed test (1979)
  +  
   *F-Test from data*
   *)
   
   let sampleFA = vector [|5.0; 6.0; 5.8; 5.7|] 
   let sampleFB = vector [|3.5; 3.7; 4.0; 3.3; 3.6|]
  +
   // comparison of sample variances 
  -let fTestFromData = FTest.testVariancesFromData sampleFA sampleFB
  +let fTestFromData = FTest.testVariances sampleFA sampleFB
   (* 
       { Statistic = 2.823383085
       DegreesOfFreedom1 = 3.0
       DegreesOfFreedom2 = 4.0
       PValue = 0.1708599931 }
  -    Using a significance level of 5% the sample variances do differ significantly.
  +    Using a significance level of 0.05 the sample variances do differ significantly.
   *)
   (** 
   *F-Test from given parameters*
  
positive.code: (**
  <a name = "FTest"></a>
  ##F-Test
  The F-test is a method to determine if the variances of two samples are homogeneous. 
  Also, ANOVA (analysis of variance) is based on the F-test and is used for the comparison of more than two samples.  
  ANOVA can detect if there is a difference in variances between samples, without pre-specifying which samples should be compared. 
  Knowing if your variances are equal (H<sub>0</sub> is true) helps to decide which test should be done next. 
  To determine if your variances are in fact equal you can perform a F-test. 
  
  Prerequisite : 
  
    -   samples are normally distributed 
  
    -   samples are independent
  
  Possible further tests :
  
    -   two sample t-test with equal variances
  
  
negative.code:     DegreesOfFreedom1 = 15.0
      DegreesOfFreedom2 = 7.0
      PValue = 0.17963663 }
      Using a significance level of 0.05 the sample variances do differ significantly.
  *)
  
  
  