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This is the home page of the Statistics Toolkit (StatsToDo) website.

The site began in the 1990s as a facility to support clinical data analysis at the Department of Obstetrics and Gynaecology, the Chinese University of Hong Kong. Since the author's retirement, the site has been transferred to a server away from the university, and additional procedures have gradually been added. Currently it is a stand alone site, and free to all that wish to use the resources.

Every effort has been made to compare results produced by these programs with those reported by standard statistical software such as SPSS, MEDCalc, STATA, and more recently R, to ensure that they are accurate and reliable. However, errors and omissions may still exist, and users should assure themselves that the contents and advise provided are correct before accepting them.

The menu bar on top of every page provides links to the index of resources, and to contact the author.

Comments, feedback, corrections, suggestions, and questions are all very welcomed. These can be sent via the contact page (linked from menu on top of pages)


The author has many years of experience in data analysis as a clinician, in research, auditing, and quality control. However, other than a 1 semester course in Multiple Regression at the Master's level, all information provided on this site are based on knowledge from informal studies and personal experience.

Comments, advice, and suggestions, as well as the algoritms provided, are therefore based on "to the best of the author's current understanding", and can not be considered as formal knowledge or authoratative instructions. Users in doubt are strongly advised to consult a professional statistician.

The author reserves the right to amend, update and delete any information of the website without prior notice, and acepts no liability for any loss, change or damage howsoever arising from any use or misuse of or reliance on any information in this website.

The resources of this site is free to all who wish to use them. However, a condition of use is that the user accepts his/her own responsibility for accepting or reproducing any information from the site or communications with the author.

Numerical precision

Non integer numerical output from StatsToDo are by default in 4 decimal places of precision, unless the situation warrants otherwise. Experience indicates that this is more than sufficient in most circumstances. As percent (%) is usually presented to 1 decimal point, probability to 2 or at most 3 decimal point, t,z,F, and Chi Square have little meaning after the second decimal point, user should be aware that 4 decimal points of precision is often redundant, and edit numerical results before publication.

Many mathematical procedures are iterative approximations. Depending on the power of the processor, and the limits of approximation set during computation, the results may differ. In addition, as servers are continuously upgraded and becomes more powerful, results produced from the same program may change over time. Experience indicates that numerical outputs may differ as much as 0.1% to 0.3% from different sources, and similar differences may occasionally be seen between results of current computation and static tables produced previously. Users should understand the cause of these differences, and not be confused or alarmed. If in doubt, user should send questions via the contact page or consult his/her own statistical advisor.

Default Example Data

In nearly all programs offered from StatsToDo, default example data are provided, to show the user the format of data input, and to provide an example of the results that are produced.

The default examples are all artificially generated to demonstrate the procedures involved, and must not be interpreted as anything reflecting reality. The research model in each example is also deliberately simplistic, so that it does not distract the user from focussing on the statistical and computational aspects

The sizes of the example data are also very small, so that the user can visualize them easily, but do not reflect the required sample size for meaningful interpretation of results.

After testing and understanding the program with the default example data, the user can then replace them with his/her own data to produce the required results.