International Journal of Statistics and Applied Mathematics
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2023, Vol. 8, Issue 1, Part A

Three-factor experimental design as a tool in applied statistics

Author(s): Vesna Antoska Knights and Jetmira Millaku

Abstract: The purpose of this research is the implementation of Three-factor experimentaldesign a as a tool in Applied Statistics in purpose of definition of indicators of the effectiveness of reducing stress levels and anxiety in students during meditation training Three-factor experimental design and yoga exercise training. Using this three-factor model three parameters were monitored: blood pressure, duration of sleeping, and smoking. This system of indicators is called a three-factor experimental design with two levels of variation ( ) for the process of stress and anxiety. A way to obtain simplified models is using full factorial designs, which are important means to evaluate the influence of the factors on response. All 30 students-volunteers have been randomly selected, in the three-factor experiment, and analyzed, and in each case, regardless of gender, the effect of the factors. The result obtained from these analyses using the three-factor experiment were very similar for all the respondents. By applying this model, it shows highest influence has a coefficient of factor smoking, then hours of sleeping on response y (stress and anxiety). This model is a systematic method to determine the relationship between factors affecting a process and the output of that process. In other words, it is used to find cause-and-effect relationships. This information is needed to manage process inputs to optimize the output.

DOI: 10.22271/maths.2023.v8.i1a.929

Pages: 46-49 | Views: 206 | Downloads: 13

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How to cite this article:
Vesna Antoska Knights, Jetmira Millaku. Three-factor experimental design as a tool in applied statistics. Int J Stat Appl Math 2023;8(1):46-49. DOI: 10.22271/maths.2023.v8.i1a.929

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