Free download:
LeoStatistic.zip or
LeoStatistic.exe
(selfextracting winzip file)

Registration

Finally you have no need to learn hard behavior of each control to run comprehensive statistical analysis of your data:

• Import data (table) from outside source and go to Menu->Data->Full analysis.
• The dialog "Full statistical analysis" will appear.
• Select statistical schemes you could be interested in and click "Run analysis" button.
• LeoStatistic will perform calculation and create and open report in HTML format file.

That is it.

Of course you better to know what numbers in results of any of statistical schemes are really mean... But at least you will have them to report your boss or teacher, there must be first step for everything.

A default setting of controls in the dialog correspond to the most extensive analysis and reporting results of analysis. There are competitive school of reasoning about  how much information is too much. Extreme points of view represent two both rational ideas but mutual contradictory ideas. First is a concept that any correlation between parameters reflects reality and should be reported whatever close to noise it could be. So we can if not eliminate but at least diminished a danger of losing important information. Competitive concept refers to the same fear of omitting important finding but now by reason of hiding important one among number of statistically insignificant. The problem is that the decision about meaninglessness correlation between statistical model (equation with curve for example) is always arbitrary at the first place and depend on the formulation of the task in real life. There is not any universal solution for filtering above noise level fluctuations. For one 97% probability is OK, for others 99.999... not assuring enough. For Jim Carrey's character from movie "Dumb and Dubmer" one to million chance is almost done deal.

At the "Full statistical analysis" dialog of LeoStatistic there are two options to filter poor fitting schemes by ignoring those of them with:

1. when overall fitting that is presented by correlation coefficient is not good enough. Usually value 0.975 and more close to 1.0 is fine for all practical considerations. But again there aren't silver bullet number.
2. in case if any of coefficients in approximation formula is not determined, nailed at their positions with strong certainty. As a criteria for quality in the definition of coefficient the variation coefficient (standard deviation to the value) is used. It is much more sensitive criteria then correlation coefficient as soon it shows not only that the data could be fit in general by some formula that is always possibility. But small values of variation coefficients produce an evidence that the structure of selected fitted formula has a sense. It is not a prove in choosing one formula instead other but strong, very strong argument.

 Other point that better to take into consideration before stating automatic analysis is the amount of the calculation work that is exponentially (not exact exponentially but very strong) correlated to number of the parameters in the table as well to number of parameters in it. Just for example implementation of the scheme "curve fitting" will find best correlation formulas for each couple of parameters. Let's say for table with 10 parameters it will be 81 formulas... Just be sure that you really need all of them before run especially for very long array of records. Our best advise will be - start from sort of pilot, test table that could be produced by transecting initial one.

Building histogram

Distribution of two variables.

Approximation
(constructor style interface).

3D view.

DOW trend.

Signals revealing.

Near neighbors method.

Harmonic analysis.

Fit with free format formula.

Curve fit of crystal growth rate.

Get data from image file.