Box-Cox Transfromation and Bootstrapping Approach to One Sample T-Test
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Date
2015
Journal Title
Journal ISSN
Volume Title
Publisher
World Applied Sciences Journal
Abstract
One sample t-test is one of the most popular collections of statistical technique for analyzing data.
Before we perform one sample T-Test the first thing that we should check is normality assumption. In this paper,
we combine Box-Cox and bootstrapping idea in one algorithm. The purpose of Box-Cox is to ensure the data
is normally distributed before the analysis. This combination is very useful for the modelling with an advanced
analysis and perhaps can be an alternative method for modelling options in applied statistics scope. Through
this combining method, we are capable to handle the case of non-normal data and small and limited sample size
data by bootstrapping the original data set to generate new ones. In our case, the term “bootstrap” actually is
referring to the use of the original data set to generate new ones. In this research paper, from a small and limited
sample size data, we performed bootstrapping method in order to generate a new data set with a bigger sample
size. After getting a new sample size, we then perform one sample T-Test using standard procedures and
modified procedure. Results from both analyses will be compared with others to know the efficiency of the
modified procedure. We also provided some example of application of the method discussed by using SAS
language computer software.
Description
Keywords
Bootstrap, One Sample T-Test, Box-Cox Transformation