LabWindows/CVI Idea Exchange

It has been discussed a few times in the forum so it might be appropriate to also ask here for the implementation of a native polar plot, i.e. plot r versus theta, with radial grid lines and all the corresponding ingredients.

In close analogy to the functions N_Dist (,)... for calculating the one-sided probability for a standard normal distribution I'd like to suggest test routines

B_Test () for binomial distributions and P_Test () for Poisson distributions.

Background: In many experiments, signals are not measured as a continuous curernt/voltage but are counted as individual events (photons, particles...) For 'rare' events the corresponding distribution function is binomial (few repetitions/sweeps) or Poisson (many repetitions).

In such cases using a Gaussian distribution is not always justified.

I'll continue my quest for better statistics support in CVI... While LabView provides a VI to calculate percentiles of an array, there is no built-in function in CVI

In case you are wondering what the purpose of percentiles is: it is as important as the standard deviation for robust statistics, i.e. instead of calculating the mean and the standard deviation one would choose to calculate the median (possible with CVI) and the 25 and 75 percentiles; the difference (sometimes called the interquartile distance, IQD) is a measure for the width of the distribution.

Robust statistics is valuable of one has to deal with outliers.

Hence I ask for a function to calculate percentiles; may be, if NI is generous, they may even supply a function to calculate IQD

Suprisingly, in the Advanced Analysis Library there is no general routine for numerically finding roots - except for polynomials.

In my opinion this is a quite frequent necessity and should not be missed in an advanced analysis library 

In the Advanced Analysis Library I'd like to see a function for efficiently computing binomial coefficients. The 'standard' definition

a! / ( b! * ( a - b )! )

is not efficient and susceptible to overflow. A better approach for example could be the calculation of ln (n!) using the gamma function as outlined in Numerical Recipes.