Assessing the Efficiency of Bootstrap Method: Application SAS Programming Algorithm for Exponential Growth Streptococcus Sobrinus

Introduction: The bootstrap method is commonly used to estimate values linked with estimator and test statistical sampling distributions. As a result, it is required to have a good manner of calculating with some improvement of the proposed strategy to maintain the proposed model’s efficiency and ac...

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Published inCommunications in Mathematics and Applications Vol. 13; no. 3; pp. 1027 - 1035
Main Authors Rohim, Rabiatul Adawiyah Abdul, Jalil, Kamariah Abdul, Ghazali, Puspa Liza, Ahmad, Wan Muhamad Amir W., Ismail, Noor Huda, Fauzi, Siti Nadhirah Mohamad
Format Journal Article
LanguageEnglish
Published Kingsville RGN Publications 01.01.2022
Subjects
Algorithms
Approximation
Bacteria
Confidence intervals
Decision making
Dental caries
Dental plaque
Efficiency
Enzymes
Laboratories
Mathematics
Microorganisms
Oral diseases
Parameter estimation
Probiotics
Programming
Standard error
Statistical analysis
Statistical methods
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Summary:Introduction: The bootstrap method is commonly used to estimate values linked with estimator and test statistical sampling distributions. As a result, it is required to have a good manner of calculating with some improvement of the proposed strategy to maintain the proposed model’s efficiency and accuracy. Objective: This study aimed to compare parameter estimates using SAS programming and determine the optimal computation for representing data using the bootstrapping and non-bootstrapping methods. Methods: In this study, the bootstrap method was applied using SAS programming. Results: In comparison to a model without bootstrapping, exponential growth demonstrates a very good model utilizing bootstrapping approach because the outcome provided the smallest standard error. This implies that the best model is the exponential model with bootstrapping approach. Conclusions: Finally, these prospective methodologies have resulted in the effective study and provide the best outcomes for decision making, particularly for decision-makers, among studies in their sector.
ISSN:0976-5905
0975-8607
DOI:10.26713/cma.v13i3.1781