Techniques of nominal data analyses

• Autorzy: Stanisz A. , Broniatowska E
• Języki publikacji: EN

Abstrakty

EN

Several advanced techniques for statistical analysis of nominal data were discussed to show how interesting associations between examined variables can be obtained using correspondence analysis, logistic regression and log-linear models. All these techniques are introduced on the example of medical data connected with the patients being on the curative diet. The studied data concerns four nominal variables: breaking the diet, overcoming diseases requiring the curative diet, sex and age of patient. Applied analyses were used for searching which of factors mentioned above influence on breaking the diet. The most popular technique in this case, chi-squared test of independence, indicates that only the age and illnesses overcoming before are related with breaking the diet whereas the sex is factor which does not have any relationship with the diet breaking. However, the deeper analysis revealed that we can not omit this variable in our research. Application of more compound statistical methods show the importance of age and sex in breaking the curative diet in detail. Presented methodology can be successfully applied not only in medicine but to data coming from different branches of science as well.

Słowa kluczowe

EN

logistic regression; generalized linear model; correspondence analysis; log-linear analysis

• Wydawca: Uniwersytet Jagielloński - Collegium Medicum ; Index Copernicus Sp. z o.o.
• Czasopismo: Bio-Algorithms and Med-Systems
• Rocznik: 2012
• Tom: Vol. 8, no. 2
• Strony: 237--254
• Opis fizyczny: Bibliogr. 5 poz., tab., wykr.

Twórcy

autor

Stanisz A.

• Jagiellonian University Medical College Dep. Of Bioinformatics and Telemedicine, Łazarza 16, 31-530 Kraków

autor

Broniatowska E

• Jagiellonian University Medical College Dep. Of Bioinformatics and Telemedicine, Łazarza 16, 31-530 Kraków

Bibliografia

• 1. Agresti A., Categorical Analysis Date, Wiley, New York, 2007
• 2. Christensen R., Log-linear Models and Logistic Regression, Springer-Verlag, the New York, 1990.
• 3. Daniel W.W. Biostatistics: A Foundation for Analysis in the Health Sciences, Wiley, New York, 2009.
• 4. Maddala G. S., Introduction to Econometrics, 4th Edition, Wiley, 2001.
• 5. Stanisz A., Przystępny kurs statystyki z zastosowaniem STATISTICA PL na przykładach z medycyny, StatSoft Polska, Kraków, 2007.