Positively Skewed Data: Revisiting the Box-Cox Power Transformation.
Although the normal probability distribution is the cornerstone of applying statistical methodology; data do not always meet the necessary normal distribution assumptions. In these cases, researchers often transform non-normal data to a distribution that is approximately normal. Power transformation...
- Autores:
-
Olivier, Jake
Norberg, Melissa M
- Tipo de recurso:
- Fecha de publicación:
- 2010
- Institución:
- Universidad de San Buenaventura
- Repositorio:
- Repositorio USB
- Idioma:
- spa
- OAI Identifier:
- oai:bibliotecadigital.usb.edu.co:10819/6497
- Acceso en línea:
- http://hdl.handle.net/10819/6497
- Palabra clave:
- Ex-Gaussian distribution
Geometric mean analysis
Logarithmic transformations
Log-normal distribution
Análisis de la media geométrica
Distribución exponencial Gaussiana
Distribución logarítmica normal
Transformaciones logarítmicas
Estadística
Investigación
- Rights
- License
- Atribución-NoComercial-SinDerivadas 2.5 Colombia
Summary: | Although the normal probability distribution is the cornerstone of applying statistical methodology; data do not always meet the necessary normal distribution assumptions. In these cases, researchers often transform non-normal data to a distribution that is approximately normal. Power transformations constitute a family of transformations, which include logarithmic and fractional exponent transforms. The Box-Cox method offers a simple method for choosing the most appropriate power transformation. Another option for data that is positively skewed, often used when measuring reaction times, is the Ex-Gaussian distribution which is a combination of the exponential and normal distributions. In this paper, the Box-Cox power transformation and Ex-Gaussian distribution will be discussed and compared in the context of positively skewed data. This discussion will demonstrate that the Box-Cox power transformation is simpler to apply and easier to interpret than the Ex-Gaussian distribution. |
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