Much of what is considered nonparametric k_trends_test statistics is included in other chapters. Topics of possible interest in other chapters are: nonparametric measures of location and scale (Chapter 1, “Basic Statistics”), nonparametric measures in a contingency table (Chapter 5, “Categorical and Discrete Data Analysis”), measures of correlation in a contingency table (Chapter 3, “Correlation and Covariance”), and tests of goodness of fit and randomness (Chapter 7, “Tests of Goodness of Fit and Randomness”).
Missing Values
Most routines described in this chaptersection automatically handle missing values (NaN, “Not a Number”; see the Introduction of this manual).
Tied Observations
Many of the routines described in this chaptersection contain an argument IMSLS_FUZZ in the input. Observations that are within fuzz of each other in absolute value are said to be tied. Moreover, in some routines, an observation within fuzz of some value is said to be equal to that value. In routine imsls_f_wilcoxon_sign_rank, for example, such observations are eliminated from the analysis. If fuzz = 0.0, observations must be identically equal before they are considered to be tied. Other positive values of fuzz allow for numerical imprecision or roundoff error.
