| Class | Description |
|---|---|
| S1Anova |
Performing an analysis of variance with crossed classification on simulated data
|
| S1Distrib |
Convolution of uniform distributions
|
| S1Lsq |
Data corresponding to 2nd degree polynomial fitted by 1st degree
|
| S1MaxLike |
Maximum-likelihood estimate of mean life from few decays
|
| S1Min |
Use of various minimization methods; first approximation found by Monte-Carlo method
|
| S1Random |
Creation of random numbers distributed according to a Breit-Wigner distribution
|
| S1Reg |
Polynomial regression on created data
|
| S1TimSer |
Creating data modelling a power law and performing a time series analysis on them
|
| S2Anova |
Performing an analysis of variance with nested classification on simulated data
|
| S2Distrib |
Convolution of uniform distribution with Gaussian
|
| S2Lsq |
Data fitted to power law (linear case)
|
| S2MaxLike |
Distribution of the sample correlation coefficient
|
| S2Min |
Fitting a Breit-Wigner function to small sample and determining errors of parameters by MinCov
|
| S2Random |
Creation of random numbers distributed according to a triangular distribution
|
| S2Reg |
Polynomial regression on created data.
|
| S2TimSer |
Creating data modelling a sine, a step, or a saw-toot function and performing a time series analysis on them
|
| S3Lsq |
Data fitted to power law (nonlinear case)
|
| S3Random |
Points fluctuating about a straight line, having errors of different sizes
|
| S4Lsq |
Data generated according to a Breit-Wigner function are fitted to Breit-Wigner or to Gaussian
|
| S5Lsq |
Data following a Breit-Wigner function are fitted to BW or Gaussian; asym. errors and confid. region shown
|
| S6Lsq |
Fit of a Breit-Wigner function to a histogram
|
| S7Lsq |
Fitting a circle to data points
|