Data transformation: a focus on the interpretation / 대한마취과학회지
Korean Journal of Anesthesiology
;
: 503-508, 2020.
Article
in English
| WPRIM
| ID: wpr-901679
ABSTRACT
Several assumptions such as normality, linear relationship, and homoscedasticity are frequently required in parametric statistical analysis methods. Data collected from the clinical situation or experiments often violate these assumptions. Variable transformation provides an opportunity to make data available for parametric statistical analysis without statistical errors. The purpose of variable transformation to enable parametric statistical analysis and its final goal is a perfect interpretation of the result with transformed variables. Variable transformation usually changes the original characteristics and nature of units of variables. Back-transformation is crucial for the interpretation of the estimated results. This article introduces general concepts about variable transformation, mainly focused on logarithmic transformation. Back-transformation and other important considerations are also described herein.
Full text:
Available
Index:
WPRIM (Western Pacific)
Language:
English
Journal:
Korean Journal of Anesthesiology
Year:
2020
Type:
Article
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